Search results for: forest wood biomass

Authors: Guilherme M. Pereira, Kimmo Teinïla, Danilo Custódio, Risto Hillamo, Célia Alves, Pérola de C. Vasconcellos

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South American large cities often present serious air pollution problems and their atmosphere composition is influenced by a variety of emissions sources. The South American Emissions Megacities, and Climate project (SAEMC) has focused on the study of emissions and its influence on climate in the South American largest cities and it also included Lima (Peru) and Medellin (Colombia), sites where few studies of the genre were done. Lima is a coastal city with more than 8 million inhabitants and the second largest city in South America. Medellin is a 2.5 million inhabitants city and second largest city in Colombia; it is situated in a valley. The samples were collected in quartz fiber filters in high volume samplers (Hi-Vol), in 24 hours of sampling. The samples were collected in intensive campaigns in both sites, in July, 2010. Several species were determined in the aerosol samples of Lima and Medellin. Organic and elemental carbon (OC and EC) in thermal-optical analysis; biomass burning tracers (levoglucosan - Lev, mannosan - Man and galactosan - Gal) in high-performance anion exchange ion chromatography with mass spectrometer detection; water soluble ions in ion chromatography. The average particulate matter was similar for both campaigns, the PM10 concentrations were above the recommended by World Health Organization (50 µg m⁻³ – daily limit) in 40% of the samples in Medellin, while in Lima it was above that value in 15% of the samples. The average total ions concentration was higher in Lima (17450 ng m⁻³ in Lima and 3816 ng m⁻³ in Medellin) and the average concentrations of sodium and chloride were higher in this site, these species also had better correlations (Pearson’s coefficient = 0,63); suggesting a higher influence of marine aerosol in the site due its location in the coast. Sulphate concentrations were also much higher at Lima site; which may be explained by a higher influence of marine originated sulphate. However, the OC, EC and monosaccharides average concentrations were higher at Medellin site; this may be due to the lower dispersion of pollutants due to the site’s location and a larger influence of biomass burning sources. The levoglucosan average concentration was 95 ng m⁻³ for Medellin and 16 ng m⁻³ and OC was well correlated with levoglucosan (Pearson’s coefficient = 0,86) in Medellin; suggesting a higher influence of biomass burning over the organic aerosol in this site. The Lev/Man ratio is often related to the type of biomass burned and was close to 18, similar to the observed in previous studies done at biomass burning impacted sites in the Amazon region; backward trajectories also suggested the transport of aerosol from that region. Biomass burning appears to have a larger influence on the air quality in Medellin, in addition the vehicular emissions; while Lima showed a larger influence of marine aerosol during the study period.

Keywords: aerosol transport, atmospheric particulate matter, biomass burning, SAEMC project

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Authors: Shiming Li, Qingwang Liu, Honggan Wu, Jianbing Zhang

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Plantation forests are useful for timber production, recreation, environmental protection and social development. Stands height is an important parameter for the estimation of forest volume and carbon stocks. Although lidar is suitable technology for the vertical parameters extraction of forests, but high costs make it not suitable for operational inventory. With the development of computer vision and photogrammetry, aerial photos from unmanned aerial system can be used as an alternative solution for height mapping. Structure-from-motion (SfM) photogrammetry technique can be used to extract DSM and DEM information. Canopy height model (CHM) can be achieved by subtraction DEM from DSM. Our result shows that overlapping aerial photos is a potential solution for plantation forests height mapping.

Keywords: forest height mapping, plantation forests, structure-from-motion photogrammetry, UAS

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Authors: Livy Febria Tedjamulia, Aas Nurasyiah, Ivana Josephin Purnama, Monika Diah Maharani Kusumastuti, Achmad Ridwan Ariyantoro

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Papua's candy is one of Indonesia’s indigenous consumption consisting of areca nut (Areca catechu), forest betel fruit (Piper aduncum), and CaCO3. This research aims to determine the concentration of tannins in areca nut, alkaloids in areca nut, flavonoids in forest betel fruit; detect their interaction and CaCO3; also toform a standardize consumption recommendation. The research methodwas includingDPPH assay for papua’s candy mixture, which resulted in IC50 value. Data analysis used is mathematical linear regression for each experiment. The test result of alkaloid is a Rf value of 0.773, while concentration of tannin and flavonoidare 0.603 mgGAE/g and 125.402 gQE/g, respectively. The IC50 value shows number of 3.0403, showing high antioxidant capacity.Other antioxidant assays were being studied using literature review, namely trolox and oxygen radical absorbance capacity, to figure out interaction among the bioactive compounds. It turned out that the interaction detected is antagonistic, which means the compound that is joined already has a stable molecular structure so that could reduce free radicals by donating hydrogen atoms. The recommendation consumptions given are 4 areca nuts, 5 forest betels, and 1 gram of lime betel. Therefore, papua's candy has its potential to be developed into functional food.

Keywords: antioxidant, bioactive compounds interaction, free radical, papua’s candy

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Authors: Febrina Natalia, Noriyuki Tanaka, Mitsuru Osaki

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Sustainability Index for REDD-plus implementation was constructed to evaluate the sustainability of different communities in 5 villages (Taruna Jaya, Tumbang Nusa, Marang, Terantang, and Seragam Jaya) in Central Kalimantan, Indonesia based on the main objectives of REDD-plus project (reducing emission from deforestation and forest degradation, increasing carbon stock, preserving biodiversity and sustaining forest management). This index was separately composed of 3 different components; (1) ecology, (2) economy, and (3) society. The index of sustainability was determined into four categories; 3,3-4,0 (excellent), 2,5-3,2 (good), 1,8-2,4 (fair), and 1,0-1,7 (poor). Overall, this technique aims to assist all stakeholders and local government in particular in providing information of villages’ sustainability index before implementing REDD-plus project that the assistance and benefits given to villages will be beneficial, effective and efficient.

Keywords: central kalimantan, Indonesia, REDD-plus, sustainability index

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Authors: Sunil Chandra, Himanshu Rawat, Vikas Gusain, Triparna Barman

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Forest fires are a recurrent phenomenon in the Himalayan region owing to the presence of vulnerable forest types, topographical gradients, climatic weather conditions, and anthropogenic pressure. The present study focuses on the identification of forest fire-affected areas in a small part of the West Himalayan region using a differential normalized burnt ratio method and spectral unmixing methods. The study area has a rugged terrain with the presence of sub-tropical pine forest, montane temperate forest, and sub-alpine forest and scrub. The major reason for fires in this region is anthropogenic in nature, with the practice of human-induced fires for getting fresh leaves, scaring wild animals to protect agricultural crops, grazing practices within reserved forests, and igniting fires for cooking and other reasons. The fires caused by the above reasons affect a large area on the ground, necessitating its precise estimation for further management and policy making. In the present study, two approaches have been used for carrying out a burnt area analysis. The first approach followed for burnt area analysis uses a differential burnt normalized ratio index (dNBR) approach that uses the burnt ratio values generated using the Short Wave Infra Red (SWIR) band and Near Infra Red (NIR) bands of the Sentinel-2A image. The results of the dNBR have been compared with the outputs of the spectral mixing methods. It has been found that the dNBR is able to create good results in fire-affected areas having homogenous forest stratum and with slope degree <5 degrees. However, in a rugged terrain where the landscape is largely influenced by the topographical variations, vegetation types, tree density, the results may be largely influenced by the effects of topography, complexity in tree composition, fuel load composition, and soil moisture. Hence, such variations in the factors influencing burnt area assessment may not be effectively carried out using a dNBR approach which is commonly followed for burnt area assessment over a large area. Hence, another approach that has been attempted in the present study utilizes a spectral mixing method where the individual pixel is tested before assigning an information class to it. The method uses a neural network approach utilizing Sentinel 2A bands. The training and testing data are generated from the sentinel-2A data and the national field inventory, which is further used for generating outputs using ML tools. The analysis of the results indicates that the fire affected regions and their severity can be better estimated using spectral unmixing methods which have the capability to resolve the noise in the data and can classify the individual pixel to the precise burnt/unburnt class.

Keywords: categorical data, multilayer perceptron, neural network, shifting cultivation

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Authors: Praveen K. Khatri, Neha Karanwal, Savita Kaul, Suman L. Jain

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Conversion of biomass through green chemical routes is of great industrial importance as biomass is considered to be most widely available inexpensive renewable resource that can be used as a raw material for the production of bio fuel and value-added organic products. In this regard, acid catalyzed dehydration of biomass derived pentose sugar (mainly D-xylose) to furfural is a process of tremendous research interest in current scenario due to the wider industrial applications of furfural. Furfural is an excellent organic solvent for refinement of lubricants and separation of butadiene from butene mixture in synthetic rubber fabrication. In addition it also serve as a promising solvent for many organic materials, such as resins, polymers and also used as a building block for synthesis of various valuable chemicals such as furfuryl alcohol, furan, pharmaceutical, agrochemicals and THF. Here in a sulfonated polymer impregnated carbon composite solid acid catalyst (P-C-SO3H) was prepared by the pyrolysis of a polymer matrix impregnated with glucose followed by its sulfonation and used for the dehydration of xylose to furfural. The developed catalyst exhibited excellent activity and provided almost quantitative conversion of xylose with the selective synthesis of furfural. The higher catalytic activity of P-C-SO3H may be due to the more even distribution of polycyclic aromatic hydrocarbons generated from incomplete carbonization of glucose along the polymer matrix network, leading to more available sites for sulfonation which resulted in greater sulfonic acid density in P-C-SO3H as compared to sulfonated carbon catalyst (C-SO3H). In conclusion, we have demonstrated sulfonated polymer impregnated carbon composite (P-C-SO3H) as an efficient and selective solid acid catalyst for the dehydration of xylose to furfural. After completion of the reaction, the catalyst was easily recovered and reused for several runs without noticeable loss in its activity and selectivity.

Keywords: Solid acid , Biomass conversion, Xylose Dehydration, Heterogeneous catalyst

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Authors: Kumaraamy Sasikumar

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The study was conducted in Vadamarachchi-East in Sri Lanka. Data collection was done for a period of two months from June to July 2011. The main focus of this study was to examine factors contributing to mangrove deforestation within the study area, and resultant impacts from deforestation. The study found that, the main factors that have contributed to deforestation include: Long civil wars in the region, poverty which pushed people to clear the forest to earn income through the sale of firewood and timber among others, industrial development, increasing demand for farm and settlement land, limited knowledge within the local community, weak government polices and implementation strategies, and natural disasters especially the 2004 Tsunami destruction. The impacts presented are those that impact both on the environment and the economy including; loss of income sources, loss of biodiversity, climate change, desertification, conflicts in the use of forest products and loss of land productivity due to reduced fertility caused by soil erosion. However, a few strategies have been put in place by the government to ensure the sustainable use of mangrove forest products, though these have not proved successful in reducing deforestation. The recommendations make suggestions to the government and other stakeholders to work together in ensuring sustainable use of natural resources, for example implementing laws and regulations aimed at controlling deforestation among others.

Keywords: deforestation, impacts, actors, environment, economic, sustainable development

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Authors: Marie Guittonny-LarchevêQue

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In boreal Canada, industrial activities such as forestry, peat extraction and metal mines often occur nearby. At closure, mine waste storage facilities have to be reclaimed. On tailings storage facilities, tree plantations can achieve rapid restoration of forested landscapes. However, trees poorly grow in mine tailings and organic amendments like peat are required to improve tailings’ structure and nutrients. Canada is a well-known producer of horticultural quality peat, but some lower quality peats coming from areas adjacent to the reclaimed mines could allow successful revegetation. In particular, hemic peat coming from the bottom of peat-bogs is more decomposed than fibric peat and is less valued for horticulture. Moreover, forest peat is sometimes excavated and piled by the forest industry after cuttings to stimulate tree regeneration on the exposed mineral soil. The objective of this project was to compare the ability of peats of differing quality and origin to improve tailings structure, nutrients and tree development. A greenhouse experiment was conducted along one growing season in 2016 with a complete randomized block design combining 8 repetitions (blocks) x 2 tree species (Populus tremuloides and Pinus banksiana) x 6 substrates (tailings, commercial horticultural peat, and mixtures of tailings with commercial peat, forest peat, local fibric peat, or local hemic peat) x 2 fertilization levels (with or without mineral fertilization). The used tailings came from a gold mine and were low in sulfur and trace metals. The commercial peat had a slightly acidic pH (around 6) while other peats had a clearly acidic pH (around 3). However, mixing peat with slightly alkaline tailings resulted in a pH close to 7 whatever the tested peats. The macroporosity of mixtures was intermediate between the low values of tailings (4%) and the high values of commercial peat alone (34%). Seedling survival was lower on tailings for poplar compared to all other treatments, with or without fertilization. Survival and growth were similar among all treatments for pine. Fertilization had no impact on the maximal height and diameter of poplar seedlings but changed the relative performance of the substrates. When not fertilized, poplar seedlings grown in commercial peat were the highest and largest, and the smallest and slenderest in tailings, with intermediate values in mixtures. When fertilized, poplar seedlings grown in commercial peat were smaller and slender compared to all other substrates. However for this species, foliar, shoot, and root biomass production was the greatest in commercial peat and the lowest in tailings compared to all mixtures, whether fertilized or not. The mixture with local fibric peat provided the seedlings with the lowest foliar N concentrations compared to all other substrates whatever the species or the fertilization treatment. At the short-term, the performance of all the tested peats were close when mixed to tailings, showing that peats of lower quality could be valorized instead of using horticultural peat. These results demonstrate that intersectorial synergies in accordance with the principles of circular economy may be developed in boreal Canada between local industries around the reclamation of mine waste dumps.

Keywords: boreal trees, mine spoil, mine revegetation, intersectorial synergies

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Authors: Purnendu Bose, Jyoti Kainthola

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Due to recent advances in illumination technology, artificially illuminated algal-bacterial photo bioreactors are now a potentially feasible option for simultaneous and comprehensive organic carbon and nutrients removal from secondary treated domestic sewage. The experiments described herein were designed to determine the extent of nutrient uptake in photo bioreactors through algal assimilation. Accordingly, quasi steady state data on algal photo bioreactor performance was obtained under 20 different conditions. Results indicated that irrespective of influent N and P levels, algal biomass recycling resulted in superior performance of algal photo bioreactors in terms of both N and P removals. Further, both N and P removals were positively related to the growth of algal biomass in the reactor. Conditions in the reactor favouring greater algal growth also resulted in greater N and P removals. N and P removals were adversely impacted in reactors with low algal concentrations due to the inability of the algae to grow fast enough under the conditions provided. Increasing algal concentrations in reactors over a certain threshold value through higher algal biomass recycling was also not fruitful, since algal growth slowed under such conditions due to reduced light availability due to algal ‘self-shading’. It was concluded that N removals greater than 80% at high influent N concentrations is not possible with the present reactor configuration. Greater than 80% N removals may however be possible in similar reactors if higher light intensity is provided. High P removal is possible only if the influent N: P ratio in the reactor is aligned closely with the algal stoichiometric requirements for P.

Keywords: nutrients, algae, photo, bioreactor

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Authors: Gokul Gaudel, Zhang Wen Hui, Dang Quang Hung, Le Thi Hien, Liang Xiao

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The community forests of Nepal serve as a major source of medicinal plants for majority of local people who are dependent on traditional health care system. This study aims to explore the ethnobotanical information of the medicinal plants used by five different community forest user groups of Parbat district of Nepal. The research was conducted during different periods of the year 2015, using semi-structured, open-ended questionnaires, formal and informal interviews, and group discussions. In total 145 different plant species within 77 families were documented, the majority of them being herb were found to be used to treat 84 different ailments. In terms of plant parts use: whole plants, barks, fruits, leaves were found to be in top priorities. Oral administration was the dominant route (57%), followed by both oral and dermal route (29%) and dermal only (14%). Females were found to have 24% more ethnobotanical knowledge than male. The knowledge of ethnobotanical medicinal plants was found excellent on age group 65-75. This study showed that community forests of Parbat district are rich in medicinal plants but the new generation was found less interested in using them. Easy access to modern medicines, lack of documentation and knowledge transfer to young generations are the major causes of diminishing utility of traditional medicinal practices.

Keywords: ailments, community forest, ethnobotany, medicinal plants, Parbat

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Authors: Shuang Qiu, Lingfeng Wang, Zhipeng Chen, Shijian Ge

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Macroalgae are considered a promising approach for wastewater treatment as well as an alternative animal feed in addition to a biofuel feedstock. Their large size and/or tendency to grow as dense floating mats or substrate-attached turfs lead to lower separation and drying costs than microalgae. In this study, the macroalgae species Ulva lactuca (U. lactuca) were used to investigate their capacity for treating municipal wastewaters, and the feasibility of using the harvested biomass as an alternative food source for the fruitfly Drosophila melanogaster, an animal model for biological research. Results suggested that U. lactuca could successfully grow on three types of wastewaters studied with biomass productivities of 8.12-64.3 g DW (dry weight)/(m²∙d). The secondary wastewater (SW) was demonstrated as the most effective wastewater medium for U. lactuca growth. However, both high nitrogen (92.5-98.9%) and phosphorus (64.5-88.6%) removal efficiencies were observed in all wastewaters, particularly in primary wastewater (PW) and SW, however, in central wastewater (CW), the highest removal rates were obtained (N 24.7 ± 0.97 and P 0.69 ± 0.01 mg/(g DW·d)). Additionally, the inclusion of 20% washed U. lactuca with 80% standard fruitfly food (w/w) resulted in a longer lifespan and more stable body weights in flies. On the other hand, similar results were not obtained for the food treatment with the addition of 20 % unwashed U. lactuca. This study suggests a promising method for the macroalgae-based treatment of municipal wastewater and the biomass for animal feed.

Keywords: animal feed, flies, macroalgae, nutrient recovery, Ulva lactuca, wastewater

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Authors: Gabriela T. Nakashima, Ana Larissa S. Hansted, Gabriela B. Belini, Carlos R. Sette Jr, Hiroyuki Yamamoto, Fabio M. Yamaji

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The aim of this study was to determine the interference from contaminants in the characterization of sugarcane straw. The sugarcane straw was collected after the harvest and taken to the drying oven, and then it was crushed in the mill type Willey. Analyzes of ash contents and Klason lignin were done in triplicate and high heating value (HHV) in duplicate, according to ASTM standard. The results obtained for the sugarcane straw were 5.29% for ash content, 29.87% for Klason lignin and 17.67 MJ.kg-1 for HHV. Also, the material was analyzed by scanning electron microscope (SEM). The presence of contaminants was observed, such as silica. The high amount of contaminants in the samples may impact the results of analyzes, also raising its values, for example in the Klason lignin content. These contaminants can also adversely affect the quality of the biomass. Even using the standards is important to know what the purpose of the analysis and care mainly of sampling.

Keywords: biomass, bioenergy, residues, solid fuel

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Authors: Ishiyaku Abdulkadir, Satish Kumar J

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Land Productivity is a measure of the greenness of above-ground biomass in health and potential gain and is not related to agricultural productivity. Monitoring land productivity dynamics is essential to identify, especially when and where the trend is characterized degraded for mitigation measures. This research aims to monitor the land productivity trend of Gombe State between 2001 and 2015. QGIS was used to compute NDVI from AVHRR/MODIS datasets in a cloud-based method. The result appears that land area with improving productivity account for 773sq.km with 4.31%, stable productivity traced to 4,195.6 sq.km with 23.40%, stable but stressed productivity represent 18.7sq.km account for 0.10%, early sign of decline productivity occupied 5203.1sq.km with 29%, declining productivity account for 7019.7sq.km, represent 39.2%, water bodies occupied 718.7sq.km traced to 4% of the state’s area.

Keywords: above-ground biomass, dynamics, land productivity, man-environment relationship

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Authors: Swee Yang Low, Dong Eon Kim, Canh Tien Trinh Nguyen, Yixiong Cai, Shie-Yui Liong

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Nee Soon swamp forest is one of the last vestiges of tropical wetland in Singapore. Understanding the hydrological regime of the swamp forest and implications for water quality is critical to guide stakeholders in implementing effective measures to preserve the wetland against anthropogenic impacts. In particular, although current field measurement data do not indicate a concern with organic pollution, reviewing the ways in which the wetland responds to elevated organic waste influx (and the corresponding impact on dissolved oxygen, DO) can help identify potential hotspots, and the impact on the outflow from the catchment which drains into downstream controlled watercourses. An integrated water quality model is therefore developed in this study to investigate spatial and temporal concentrations of DO levels and organic pollution (as quantified by biochemical oxygen demand, BOD) within the catchment’s river network under hypothetical, projected scenarios of spiked upstream inflow. The model was developed using MIKE HYDRO for modelling the study domain, as well as the MIKE ECO Lab numerical laboratory for characterising water quality processes. Model parameters are calibrated against time series of observed discharges at three measurement stations along the river network. Over a simulation period of April 2014 to December 2015, the calibrated model predicted that a continuous spiked inflow of 400 mg/l BOD will elevate downstream concentrations at the catchment outlet to an average of 12 mg/l, from an assumed nominal baseline BOD of 1 mg/l. Levels of DO were decreased from an initial 5 mg/l to 0.4 mg/l. Though a scenario of spiked organic influx at the swamp forest’s undeveloped upstream sub-catchments is currently unlikely to occur, the outcomes nevertheless will be beneficial for future planning studies in understanding how the water quality of the catchment will be impacted should urban redevelopment works be considered around the swamp forest.

Keywords: hydrology, modeling, water quality, wetland

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Authors: Marina Yurievna Aleksandrova

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Research of the Holocaust retains value not only for history but also for sociology and psychology. One of the most important fields of study is how people were coping during and after this traumatic event. The aim of this paper is to identify the main contexts of the topic of love and to determine which contexts are more characteristic for different groups of victims of the Holocaust (gender, nationality, age). In this research, transcripts of interviews with Holocaust victims that were collected during 1946 for the "Voices of the Holocaust" project were used as data. Main contexts were analyzed with methods of network analysis and latent semantic analysis and classified by gender, age, and nationality with random forest. The results show that love is articulated and described significantly differently for male and female informants, nationality is shown results with lower values of quality metrics, as well as the age.

Keywords: Holocaust, latent semantic analysis, network analysis, text-mining, random forest

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Authors: Kimberley K. Whyte-Jones

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Public participation may improve the quality of environmental management decisions. However, the quality of such a decision is strongly dependent on the quality of the process that leads to it. In order to ensure an effective and efficient process, key features of best practice in participation should be carefully observed; this would also combat disillusionment of citizens, decision-makers and practitioners. The overarching aim of this study is to determine what constitutes an effective public participation process relevant to the Newfoundland and Labrador, Canada context, and to discover whether the public participation process that led to the 2014-2024 Provincial Sustainable Forest Management Strategy (PSFMS) met best practices criteria. The research design uses an exploratory case study strategy to consider a specific participatory process in environmental decision-making in Newfoundland and Labrador. Data collection methods include formal semi-structured interviews and the review of secondary data sources. The results of this study will determine the validity of a specific public participation best practice framework. The findings will be useful for informing citizen participation processes in general and will deduce best practices in public participation in environmental management in the province. The study is, therefore, meaningful for guiding future policies and practices in the management of forest resources in the province of Newfoundland and Labrador, and will help in filling a noticeable gap in research compiling best practices for environmentally related public participation processes.

Keywords: best practices, environmental decision-making, forest management, public participation

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Authors: Marcin Zieliński, Marcin Dębowski, Mirosław Krzemieniewski

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This publication presents a device designed for mechanical fragmentation of plant substrate before methane fermentation. The device is equipped with a perforated rotary cylindrical drum coated with a thermal layer, connected to a substrate feeder and driven by a motoreducer. The drum contains ball- or cylinder-shaped weights of different diameters, while its interior is mounted with lateral permanent magnets with an attractive force ranging from 100 kg to 2 tonnes per m2 of the surface. Over the perforated rotary drum, an infrared radiation generator is mounted, producing 0.2 kW to 1 kW of infrared radiation per 1 m2 of the perforated drum surface. This design reduces the energy consumption required for the biomass destruction process by 10-30% in comparison to the conventional ball mill. The magnetic field generated by the permanent magnets situated within the perforated rotary drum promotes this process through generation of free radicals that act as powerful oxidants, accelerating the decomposition rate. Plant substrate shows increased susceptibility to biodegradation when subjected to magnetic conditioning, reducing the time required for biomethanation by 25%. Additionally, the electromagnetic radiation generated by the radiator improves substrate destruction by 10% and the efficiency of the process. The magnetic field and the infrared radiation contribute synergically to the increased efficiency of destruction and conversion of the substrate.

Keywords: biomass pretreatment, mechanical fragmentation, biomass, methane fermentation

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Authors: Neha Ahirwar

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In the contemporary digital era, the rise of credit card fraud poses a significant threat to both financial institutions and consumers. As fraudulent activities become more sophisticated, there is an escalating demand for robust and effective fraud detection mechanisms. Advanced machine learning algorithms have become crucial tools in addressing this challenge. This paper conducts a thorough examination of the design and evaluation of a credit card fraud detection system, utilizing four prominent machine learning algorithms: random forest, logistic regression, decision tree, and XGBoost. The surge in digital transactions has opened avenues for fraudsters to exploit vulnerabilities within payment systems. Consequently, there is an urgent need for proactive and adaptable fraud detection systems. This study addresses this imperative by exploring the efficacy of machine learning algorithms in identifying fraudulent credit card transactions. The selection of random forest, logistic regression, decision tree, and XGBoost for scrutiny in this study is based on their documented effectiveness in diverse domains, particularly in credit card fraud detection. These algorithms are renowned for their capability to model intricate patterns and provide accurate predictions. Each algorithm is implemented and evaluated for its performance in a controlled environment, utilizing a diverse dataset comprising both genuine and fraudulent credit card transactions.

Keywords: efficient credit card fraud detection, random forest, logistic regression, XGBoost, decision tree

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Authors: P. V. Pramila , V. Mahesh

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Pulmonary Function Tests are important non-invasive diagnostic tests to assess respiratory impairments and provides quantifiable measures of lung function. Spirometry is the most frequently used measure of lung function and plays an essential role in the diagnosis and management of pulmonary diseases. However, the test requires considerable patient effort and cooperation, markedly related to the age of patients esulting in incomplete data sets. This paper presents, a nonlinear model built using Multivariate adaptive regression splines and Random forest regression model to predict the missing spirometric features. Random forest based feature selection is used to enhance both the generalization capability and the model interpretability. In the present study, flow-volume data are recorded for N= 198 subjects. The ranked order of feature importance index calculated by the random forests model shows that the spirometric features FVC, FEF 25, PEF,FEF 25-75, FEF50, and the demographic parameter height are the important descriptors. A comparison of performance assessment of both models prove that, the prediction ability of MARS with the `top two ranked features namely the FVC and FEF 25 is higher, yielding a model fit of R2= 0.96 and R2= 0.99 for normal and abnormal subjects. The Root Mean Square Error analysis of the RF model and the MARS model also shows that the latter is capable of predicting the missing values of FEV1 with a notably lower error value of 0.0191 (normal subjects) and 0.0106 (abnormal subjects). It is concluded that combining feature selection with a prediction model provides a minimum subset of predominant features to train the model, yielding better prediction performance. This analysis can assist clinicians with a intelligence support system in the medical diagnosis and improvement of clinical care.

Keywords: FEV, multivariate adaptive regression splines pulmonary function test, random forest

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Authors: Yung-Chung Chuang

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The cultural layers of archaeological sites are mainly affected by surface land use, land cover, and root system of surface vegetation. For this reason, continuous monitoring of land use and land cover change is important for archaeological sites protection and management. However, in actual operation, on-site investigation and orthogonal photograph interpretation require a lot of time and manpower. For this reason, it is necessary to perform a good alternative for surface vegetation survey in an automated or semi-automated manner. In this study, we applied semi-automatic digital aerial survey technology and canopy characters classification with very high-resolution aerial photographs for surface vegetation interpretation of archaeological sites. The main idea is based on different landscape or forest type can easily be distinguished with canopy characters (e.g., specific texture distribution, shadow effects and gap characters) extracted by semi-automatic image classification. A novel methodology to classify the shape of canopy characters using landscape indices and multivariate statistics was also proposed. Non-hierarchical cluster analysis was used to assess the optimal number of canopy character clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy character classification (seven categories). Therefore, people could easily predict the forest type and vegetation land cover by corresponding to the specific canopy character category. The results showed that the semi-automatic classification could effectively extract the canopy characters of forest and vegetation land cover. As for forest type and vegetation type prediction, the average prediction accuracy reached 80.3%~91.7% with different sizes of test frame. It represented this technology is useful for archaeological site survey, and can improve the classification efficiency and data update rate.

Keywords: digital aerial survey, canopy characters classification, archaeological sites, multivariate statistics

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Authors: Young Nam Chun, Soo Hyuk Yun, Byeo Ri Jeong

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The recent gradual increase in the energy demand is mostly met by fossil fuel, but the research on and development of new alternative energy sources is drawing much attention due to the limited fossil fuel supply and the greenhouse gas problem. Biomass is an eco-friendly renewable energy that can achieve carbon neutrality. The conversion of the biomass sludge wastes discharged from a wastewater treatment plant to clean energy is an important green energy technology in an eco-friendly way. In this NRF study, a new type of microwave thermal treatment was developed to apply the biomass-CCS technology to sludge wastes. For this, the microwave dielectric heating characteristics were examined to investigate the energy conversion mechanism for the combined drying-pyrolysis/gasification of the dewatered wet sludge. The carbon dioxide gasification was tested using the CO2 captured from the pre-combustion capture process. In addition, the results of the pyrolysis and gasification test with the wet sludge were analyzed to compare the microwave energy conversion results with the results of the use of the conventional heating method. Gas was the largest component of the product of both pyrolysis and gasification, followed by sludge char and tar. In pyrolysis, the main components of the producer gas were hydrogen and carbon monoxide, and there were some methane and hydrocarbons. In gasification, however, the amount of carbon monoxide was greater than that of hydrogen. In microwave gasification, a large amount of heavy tar was produced. The largest amount of benzene among light tar was produced in both pyrolysis and gasification. NH3 and HCN which are the precursors of NOx, generated as well. In microwave heating, the sludge char had a smooth surface, like that of glass, and in the conventional heating method with an electric furnace, deep cracks were observed in the sludge char. This indicates that the gas obtained from the microwave pyrolysis and gasification of wet sewage sludge can be used as fuel, but the heavy tar and NOx precursors in the gas must be treated. Sludge char can be used as solid fuel or as a tar reduction adsorbent in the process if necessary. This work supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2015R1R1A2A2A03003044).

Keywords: microwave heating, pyrolysis gasification, precombustion CCS, sewage sludge, biomass energy

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Authors: Khaled M. Khleifat

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Pseudomonas aeruginosa was isolated from infected burn patients and characterized by standard biochemical tests. The in vitro copper uptake was compared between this isolated pathogenic strain and two non-pathogenic control strains of Gram-positive bacteria Bacillusthuringiensis strain Israelisas well as Gram-negative bacteria Enterobacter aerogenes. Maximum copper uptake of 470 ppm/g biomass was obtained by P. aeruginosa strain, while the control strains B. thuringiensis and Enterobacter aerogenes had copper uptake of 350 and 383 ppm/g biomass, respectively. However, the lowest copper uptake (60 ppm/g biomass) was observed with another control the saprophytic strain Pseudomonas (Shewanella) putrefaciens. A further investigation regarding the effect of copper toxicity on bacterial growth, gave an MIC score of 600 ppm for P. aeruginosa strain compared to 460 and 300 ppm for the two Gram positive and Gram negative control strains, respectively. In tandem with these in vitro findings, blood analysis on burn patients infected with P. aeruginosa has indicated a selective decrease of copper (hypocupremia) and ceruloplasmin plasma levels. The iron metabolism was also affected by this copper deprivation leading to a similar decrease in plasma levels of PCV, iron, total iron-binding capacity, and transferrin. All these hematological changes were significantly different (P < 0.05) from the matched group of non-infected burn patients. The observed hypocupremia in infected burn patients was attributed to demanding scavenger ability by P. aeruginosa strain for the copper of plasma.

Keywords: Pseudomonas aeruginosa, hypocupremia, correlation, PCV

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Authors: Arzu Firlarer, Duygu Gocmen, Gokhan Uysal

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There is currently a growing trend among universities towards blended learning. Blended learning is becoming increasingly important in higher education, with the aims of better accomplishing course learning objectives, meeting students’ changing needs and promoting effective learning both in a theoretical and practical dimension like interior architecture discipline. However, the practical dimension of the discipline cannot be supported in the university environment. During the undergraduate program, the practical training which is tried to be supported by two different internship programs cannot fully meet the requirements of the blended learning. The lack of education program frequently expressed by our graduates and employers is revealed in the practical knowledge and skills dimension of the profession. After a series of meetings for curriculum studies, interviews with the chambers of profession, meetings with interior architects, a gap between the theoretical and practical training modules is seen as a problem in all interior architecture departments. It is thought that this gap can be solved by a new education model which is formed by the cooperation of University-Industry in the concept of blended learning. In this context, it is considered that theoretical and applied knowledge accumulation can be provided by the creation of industry-supported educational environments at the university. In the application process of the Interior Architecture discipline, the use of materials and technical competence will only be possible with the cooperation of industry and participation of students in the production/manufacture processes as observers and practitioners. Wood manufacturing is an important part of interior architecture applications. Wood productions is a sustainable structural process where production details, material knowledge, and process details can be observed in the most effective way. From this point of view, after theoretical training about wooden materials, wood applications and production processes are given to the students, practical training for production/manufacture planning is supported by active participation and observation in the processes. With this blended model, we aimed to develop a training model in which theoretical and practical knowledge related to the production of wood works will be conveyed in a meaningful, lasting way by means of university-industry cooperation. The project is carried out in Ankara with Arcora Architecture and Furniture Company and Başkent University Department of Interior Design where university-industry cooperation is realized. Within the scope of the project, every week the video of that week’s lecture is recorded and prepared to be disseminated by digital medias such as Udemy. In this sense, the program is not only developed by the project participants, but also other institutions and people who are trained and practiced in the field of design. Both academicians from University and at least 15-year experienced craftsmen in the wood metal and dye sectors are preparing new training reference documents for interior architecture undergraduate programs. These reference documents will be a model for other Interior Architecture departments of the universities and will be used for creating an online education module.

Keywords: blended learning, interior design, sustainable training, effective learning.

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Authors: Ruijia Hu, Susanna T.Y. Tong

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Light detection and ranging (LiDAR) and four-channel red, green, blue, and near-infrared (RGBI) remote sensed imageries allow an accurate quantification and contiguous measurement of vegetation characteristics and forest structures. This information facilitates the generation of habitat structure variables for forest species distribution modelling. However, applications of remote sensing data, especially the combination of structural and spectral information, to support evidence-based decisions in forest managements and conservation practices at local scale are not widely adopted. In this study, we examined the habitat requirements of pileated woodpecker (Dryocopus pileatus) (PW) in Hamilton County, Ohio, using ecologically relevant forest structural and vegetation characteristics derived from LiDAR and RGBI data. We hypothesized that the habitat of PW is shaped by vegetation characteristics that are directly associated with the availability of food, hiding and nesting resources, the spatial arrangement of habitat patches within home range, as well as proximity to water sources. We used 186 PW presence or absence locations to model their presence and absence in generalized additive model (GAM) at two scales, representing foraging and home range size, respectively. The results confirm PW’s preference for tall and large mature stands with structural complexity, typical of late-successional or old-growth forests. Besides, the crown size of dead trees shows a positive relationship with PW occurrence, therefore indicating the importance of declining living trees or early-stage dead trees within PW home range. These locations are preferred by PW for nest cavity excavation as it attempts to balance the ease of excavation and tree security. In addition, we found that PW can adjust its travel distance to the nearest water resource, suggesting that habitat fragmentation can have certain impacts on PW. Based on our findings, we recommend that forest managers should use different priorities to manage nesting, roosting, and feeding habitats. Particularly, when devising forest management and hazard tree removal plans, one needs to consider retaining enough cavity trees within high-quality PW habitat. By mapping PW habitat suitability for the study area, we highlight the importance of riparian corridor in facilitating PW to adjust to the fragmented urban landscape. Indeed, habitat improvement for PW in the study area could be achieved by conserving riparian corridors and promoting riparian forest succession along major rivers in Hamilton County.

Keywords: deadwood detection, generalized additive model, individual tree crown delineation, LiDAR, pileated woodpecker, RGBI aerial imagery, species distribution models

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Authors: Nivedita Sharma

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The global demand for fossil fuels is very high, but their use is not sustainable since its reserves are declining. Additionally, fossil fuels are responsible for the accumulation of greenhouse gases. The emission of greenhouse gases from the transport sector can be reduced by substituting fossil fuels by biofuels. Thus, renewable fuels capable of sequestering carbon dioxide are in high demand. Second‐generation biofuels, which require lignocellulosic biomass as a substrate and ultimately producing ethanol, fall largely in this category. Bioethanol is a favorable and near carbon-neutral renewable biofuel leading to reduction in tailpipe pollutant emission and improving the ambient air quality. Lignocellulose consists of three main components: cellulose, hemicellulose and lignin which can be converted to ethanol with the help of microbial enzymes. Enzymatic hydrolysis of lignocellulosic biomass in 1st step is considered as the most efficient and least polluting methods for generating fermentable hexose and pentose sugars which subsequently are fermented to power alcohol by yeasts in 2nd step of the process. In the present technology, a complete bioconversion process i.e. potential hydrolytic enzymes i.e. cellulase and xylanase producing microorganisms have been isolated from different niches, screened for enzyme production, identified using phenotyping and genotyping, enzyme production, purification and application of enzymes for saccharification of different lignocellulosic biomass followed by fermentation of hydrolysate to ethanol with high yield is to be presented in detail.

Keywords: cellulase, xylanase, lignocellulose, bioethanol, microbial enzymes

Procedia PDF Downloads 92

Authors: Funda Ates

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Primary energy sources, such as petroleum, coal and natural gas are principle responsible of world’s energy consumption. However, the rapid worldwide increase in the depletion of these energy sources is remarkable. In addition to this, they have damaging environmentally effect. Renewable energy sources are capable of providing a considerable fraction of World energy demand in this century. Biomass is one of the most abundant and utilized sources of renewable energy in the world. It can be converted into commercial fuels, suitable to substitute for fossil fuels. A high number of biomass types can be converted through thermochemical processes into solid, liquid or gaseous fuels. Pyrolysis is the thermal decomposition of biomass in the absence of air or oxygen. In this study, barley straw has been investigated as an alternative feedstock to obtain fuels and chemicals via pyrolysis in fixed-bed reactor. The influence of pyrolysis temperature in the range 450–750 °C as well as the catalyst effects on the products was investigated and the obtained results were compared. The results indicated that a maximum oil yield of 20.4% was obtained at a moderate temperature of 550 °C. Oil yield decreased by using catalyst. Pyrolysis oils were examined by using instrumental analysis and GC/MS. Analyses revealed that the pyrolysis oils were chemically very heterogeneous at all temperatures. It was determined that the most abundant compounds composing the bio-oil were phenolics. Catalyst decreased the reaction temperature. Most of the components obtained using a catalyst at moderate temperatures was close to those obtained at high temperatures without using a catalyst. Moreover, the use of a catalyst also decreased the amount of oxygenated compounds produced.

Keywords: Barley straw, pyrolysis, catalyst, phenolics

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Authors: Vincent Alexander, Rizkita Esyanti

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Leaves of Stevia rebaudiana contain steviol glycoside which mainly comprise of stevioside, a natural sweetener compound that is 100-300 times sweeter than sucrose. Current cultivation method of Stevia rebaudiana in Indonesia has yet to reach its optimum efficiency and productivity to produce stevioside as a safe sugar substitute sweetener for people with diabetes. An alternative method that is not limited by environmental factor is in vitro temporary immersion system (TIS) culture method using recipient for automated immersion (RITA^®) bioreactor. The aim of this research was to evaluate the effect of red LED light induction towards shoot growth and stevioside accumulation in TIS RITA^® bioreactor system, as an endeavour to increase the secondary metabolite synthesis. The result showed that the stevioside accumulation in TIS RITA^® bioreactor system induced with red LED light for one hour during night was higher than that in TIS RITA^® bioreactor system without red LED light induction, i.e. 71.04 ± 5.36 μg/g and 42.92 ± 5.40 μg/g respectively. Biomass growth rate reached as high as 0.072 ± 0.015/day for red LED light induced TIS RITA^® bioreactor system, whereas TIS RITA^® bioreactor system without induction was only 0.046 ± 0.003/day. Productivity of Stevia rebaudiana shoots induced with red LED light was 0.065 g/L medium/day, whilst shoots without any induction was 0.041 g/L medium/day. Sucrose, salt, and inorganic consumption in both bioreactor media increased as biomass increased. It can be concluded that Stevia rebaudiana shoot in TIS RITA^® bioreactor induced with red LED light produces biomass and accumulates higher stevioside concentration, in comparison to bioreactor without any light induction.

Keywords: LED, Stevia rebaudiana, Stevioside, TIS RITA

Procedia PDF Downloads 363

Authors: Rahila Yilangai, Sonali Saha, Amartya Saha, Augustine Ezealor

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Gallery forests in sub-Saharan Africa are drastically disappearing due to intensive anthropogenic activities thus reducing ecosystem services, one of which is water provisioning. The role played by forest cover in regulating streamflow and water yield is not well understood, especially in West Africa. This pioneering 2-year study investigated the interrelationships between plant cover and hydrology in protected and unprotected gallery forests. Rainfall, streamflow, and evapotranspiration (ET) measurements/estimates over 2015-2016 were obtained to form a water balance for both catchments. In addition, transpiration in the protected gallery forest with high vegetation cover was calculated from stomatal conductance readings of selected species chosen from plot level data of plant diversity and abundance. Results showed that annual streamflow was significantly higher in the unprotected site than the protected site, even when normalized by catchment area. However, streamflow commenced earlier and lasted longer in the protected site than the degraded unprotected site, suggesting regulation by the greater tree density in the protected site. Streamflow correlated strongly with rainfall with the highest peak in August. As expected, transpiration measurements were less than potential evapotranspiration estimates, while rainfall exceeded ET in the water cycle. The water balance partitioning suggests that the lower vegetation cover in the unprotected catchment leads to a larger runoff in the rainy season and less infiltration, thereby leading to streams drying up earlier, than in the protected catchment. This baseline information is important in understanding the contribution of plants in water cycle regulation, for modeling integrative water management in applied research and natural resource management in sustaining water resources with changing the land cover and climate uncertainties in this data-poor region.

Keywords: evapotranspiration, gallery forest, rainfall, streamflow, transpiration

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Authors: Narasimhulu Korrapati

Abstract:

The objective of this present study is to optimize the process parameters for batch biosorption of Cr(VI) and Cu(II) ions by Bacillus subtilis using Response Surface Methodology (RSM). Batch biosorption studies were conducted under optimum pH, temperature, biomass concentration and contact time for the removal of Cr(VI) and Cu(II) ions using Bacillus subtilis. From the studies it is noticed that the maximum biosorption of Cr(VI) and Cu(II) was by Bacillus subtilis at optimum conditions of contact time of 30 minutes, pH of 4.0, biomass concentration of 2.0 mg/mL, the temperature of 32°C in batch biosorption studies. Predicted percent biosorption of the selected heavy metal ions by the design expert software is in agreement with experimental results of percent biosorption. The percent biosorption of Cr(VI) and Cu(II) in batch studies is 80% and 78.4%, respectively.

Keywords: heavy metal ions, response surface methodology, biosorption, wastewater

Procedia PDF Downloads 267

Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: cost prediction, machine learning, project management, random forest, neural networks

Procedia PDF Downloads 37