Search results for: random forest

Authors: Jephte Sompud, Emily A. Gilbert, Andy Russel Mojiol, Cynthia B. Sompud, Alim Biun

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Naturally regenerated acacia forest and secondary indigenous forest forms some of the urban forests in Sabah. Naturally regenerated acacia trees are usually seen along the road that exists as forest islands. Acacia tree is not an indigenous tree species in Sabah that was introduced in the 1960’s as fire breakers that eventually became one of the preferred trees for forest plantation for paper and pulp production. Due to its adaptability to survive even in impoverished soils and poor-irrigated land, this species has rapidly spread throughout Sabah through natural regeneration. Currently, there is a lack of study to investigate the bird population in the naturally regenerated acacia forest. This study is important because it shed some light on the role of naturally regenerated acacia forest on bird’s population, as bird is known to be a good bioindicator forest health. The aim of this study was to document the bird’s population in naturally regenerated acacia forest with that adjacent secondary indigenous forest. The study site for this study was at Universiti Malaysia Sabah (UMS) Campus. Two forest types in the campus were chosen as a study site, of which were naturally regenerated Acacia Forest and adjacent secondary indigenous forest, located at the UMS Hill. A total of 21 sampling days were conducted in each of the forest types. The method used during this study was solely mist nets with three pockets. Whenever a bird is caught, it is extracted from the net to be identified and measurements were recorded in a standard data sheet. Mist netting was conducted from 6 morning until 5 evening. This study was conducted between February to August 2014. Birds that were caught were ring banded to initiate a long-term study on the understory bird’s population in the Campus The data was analyzed using descriptive analysis, diversity indices, and t-test. The bird population diversity at naturally regenerated Acacia forest with those at the secondary indigenous forest was calculated using two common indices, of which were Shannon-Wiener and Simpson diversity index. There were 18 families with 33 species that were recorded from both sites. The number of species recorded at the naturally regenerated acacia forest was 26 species while at the secondary indigenous forest were 19 species. The Shannon diversity index for Naturally Regenerated Acacia Forest and secondary indigenous forests were 2.87 and 2.46. The results show that there was very significantly higher species diversity at the Naturally Regenerated Acacia Forest as opposed to the secondary indigenous forest (p<0.001). This suggests that Naturally Regenerated Acacia forest plays an important role in urban bird conservation. It is recommended that Naturally Regenerated Acacia Forests should be considered as an established urban forest conservation area as they do play a role in biodiversity conservation. More future studies in Naturally Regenerated Acacia Forest should be encouraged to determine the status and value of biodiversity conservation of this ecosystem.

Keywords: naturally regenerated acacia forest, bird population diversity, Universiti Malaysia Sabah, biodiversity conservation

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Authors: Bikram Jung Kunwar, Pralhad Kunwor

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The Community-Based Forest Management (CBFM) approach is often glorified as the best forest management alternatives in the developing countries. However, how the approach has been understood by the local user households, who implement it is remained unanswered for many planners, policy makers, and sometimes researcher as well. The study attempts to assess the understanding of ecology and economics of CBFM in Nepal, where community forest program has been implemented since the 1970s. In order to understand the impacts of the program, eight criteria and sixteen indicators for ecological conservation and similarly same number of criteria and indicators for socio-economic impacts of the program were designed and compared between before and after the program implementation. The community forestry program has positive effects in forest ecology conservation and at the same time rural livelihood improvement of local people. The study revealed that collective understanding of forest ecology and economics leads the CBFM approach towards the sustainability of the program in a win-win situation. The recommendations of the study are expected to be useful to natural resource managers, planners, and policy makers.

Keywords: community, forest management, ecology, economics, Nepal

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Authors: Rauno Pääkkönen, Fabriziomaria Gobba, Leena Korpinen

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The objective of the study is to assess the implementation of LED lighting into forest machine work in the dark. In addition, the paper includes a wide variety of important and relevant safety and health parameters. In modern, computerized work in the cab of forest machines, artificial illumination is a demanding task when performing duties, such as the visual inspections of wood and computer calculations. We interviewed entrepreneurs and gathered the following as the most pertinent themes: (1) safety, (2) practical problems, and (3) work with LED lighting. The most important comments were in regards to the practical problems of LED lighting. We found indications of technical problems in implementing LED lighting, like snow and dirt on the surfaces of lamps that dim the emission of light. Moreover, service work in the dark forest is dangerous and increases the risks of on-site accidents. We also concluded that the amount of blue light to the eyes should be assessed, especially, when the drivers are working in a semi-dark cab.

Keywords: forest machines, health, LED, safety

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Authors: Julija Konstantinavičienė, Vlada Vitunskienė

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The role of sustainable biomass, including wood biomass, is becoming more important because of European Green Deal. The New EU Forest strategy is a flagship element of the European Green Deal and a key action on the EU biodiversity strategy for 2030. The first measure of this strategy is promoting sustainable forest management, including encouraging the sustainable use of wood-based resources. The first aim of this research was to develop and present a new approach to the concept of forest wood biomass potential in terms of sustainable development, distinguishing theoretical, technical and sustainable potential and detailing its constraints. The second aim was to prepare the methodology outline of sustainable forest wood biomass potential assessment and empirically check this methodology, considering economic, social and ecological constraints. The basic methodologies of the research: the review of research (with a combination of semi-systematic and integrative review methodologies), rapid assessment method and statistical data analysis. The developed methodology of assessment of forest wood potential in terms of sustainable development can be used in Lithuania and in other countries and will let us compare this potential a different time and spatial levels. The application of the methodology will be able to serve the development of new national strategies for the wood sector.

Keywords: assessment, constraints, forest wood biomass, methodology, potential, sustainability

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Authors: Bayu Rizky Pratama, Hardiansyah Nur Sahaya

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Riau Province is the worst emitter for forest burning which causes the huge scale of externality such as declining of forest habitat, health disease, and climate change impact. Indonesia forum of budget transparency for Riau Province (FITRA) reported the length of forest burning reached about 186.069 hectares which is 7,13% of total national forest burning disaster, consisted of 107.000 hectares of peatland and the rest 79.069 hectares of mineral land. Anti-fire group, a voluntary group next to the forest, to help in protecting the forest burning and heavily smoke residual has been established but unfortunately the implementation still far from expectation. This research will emphasize on (1) how the anti-fire group contribute to fire hazard tackling; (2) the identification of SWOT analysis to enhance the group benefit; and (3) government policy implication to maximize the role of Anti-fire group and reduce the case of forest burning as well as heavily smoke which can raise climate change impact. As the observation found some weakness from SWOT identification such as (1) lack of education and training; (2) facility in extinguishing the fire damage; (3) law for economic incentive; (4) communication and field experience; (5) also the reporting the fire case.

Keywords: anti-fire group, forest burning impact, SWOT, climate change mitigation

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Authors: Muhtasim Shafi Kader, Fizar Ahmed, Annesha Acharjee

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Machine learning and data mining are crucial in health care, as well as medical information and detection. Machine learning approaches are now being utilized to improve awareness of a variety of critical health issues, including diabetes detection, neuron cell tumor diagnosis, COVID 19 identification, and so on. Parkinson’s disease is basically a disease for our senior citizens in Bangladesh. Parkinson's Disease indications often seem progressive and get worst with time. People got affected trouble walking and communicating with the condition advances. Patients can also have psychological and social vagaries, nap problems, hopelessness, reminiscence loss, and weariness. Parkinson's disease can happen in both men and women. Though men are affected by the illness at a proportion that is around partial of them are women. In this research, we have to get out the accurate ML algorithm to find out the disease with a predictable dataset and the model of the following machine learning classifiers. Therefore, nine ML classifiers are secondhand to portion study to use machine learning approaches like as follows, Naive Bayes, Adaptive Boosting, Bagging Classifier, Decision Tree Classifier, Random Forest classifier, XBG Classifier, K Nearest Neighbor Classifier, Support Vector Machine Classifier, and Gradient Boosting Classifier are used.

Keywords: naive bayes, adaptive boosting, bagging classifier, decision tree classifier, random forest classifier, XBG classifier, k nearest neighbor classifier, support vector classifier, gradient boosting classifier

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Authors: Bernard Kumi-Boateng, Issaka Yakubu

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Forest fire, which is, an uncontrolled fire occurring in nature has become a major concern for the Forestry Commission of Ghana (FCG). The forest fires in Ghana usually result in massive destruction and take a long time for the firefighting crews to gain control over the situation. In order to assess the effect of forest fire at local scale, it is important to consider the role fire plays in vegetation composition, biodiversity, soil erosion, and the hydrological cycle. The occurrence, frequency and behaviour of forest fires vary over time and space, primarily as a result of the complicated influences of changes in land use, vegetation composition, fire suppression efforts, and other indigenous factors. One of the forest zones in Ghana with a high level of vegetation stress is the Goaso forest area. The area has experienced changes in its traditional land use such as hunting, charcoal production, inefficient logging practices and rural abandonment patterns. These factors which were identified as major causes of forest fire, have recently modified the incidence of fire in the Goaso area. In spite of the incidence of forest fires in the Goaso forest area, most of the forest services do not provide a cartographic representation of the burned areas. This has resulted in significant amount of information being required by the firefighting unit of the FCG to understand fire risk factors and its spatial effects. This study uses Remote Sensing and Geographic Information System techniques to develop a fire risk hazard model using the Goaso Forest Area (GFA) as a case study. From the results of the study, natural forest, agricultural lands and plantation cover types were identified as the major fuel contributing loads. However, water bodies, roads and settlements were identified as minor fuel contributing loads. Based on the major and minor fuel contributing loads, a forest fire risk hazard model with a reasonable accuracy has been developed for the GFA to assist decision making.

Keywords: forest, GIS, remote sensing, Goaso

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Authors: Krishang Surapaneni

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The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42%

Keywords: finance, linear regression model, machine learning model, neural network, stock price

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Authors: Abdul Mutolib, Yonariza Mahdi, Hanung Ismono

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The existence of forests is essential to human lives on earth, but its existence is threatened by forest deforestations and degradations. Forest deforestations and degradations in Indonesia is not only caused by the illegal activity by the company or the like, even today many cases in Indonesia forest damage caused by human activities, one of which cut down forests for agriculture and plantations. In West Sumatra, community forest management are the result supported the enactment of customary land tenure, including ownership of land within the forest. Indigenous forest management have a positive benefit, which gives the community an opportunity to get livelihood and income, but if forest management practices by indigenous peoples is not done wisely, then there is the destruction of forests and cause adverse effects on the environment. Based on intensive field works in Dhamasraya District employing some data collection techniques such as key informant interviews, household surveys, secondary data analysis, and satellite image interpretation. This paper answers the following questions; how the impact of forest management by local communities on forest conditions (foccus in Forest Production and Limited Production Forest) and knowledge of the local community on the benefits of forests. The site is a Nagari Bonjol, Dharmasraya District, because most of the forest in Dharmasraya located and owned by Nagari Bonjol community. The result shows that there is damage to forests in Dharmasraya because of forest management activities by local communities. Damage to the forest area of 33,500 ha in Dharmasraya because forests are converted into oil palm and rubber plantations with monocultures. As a result of the destruction of forests, water resources are also diminishing, and the community has experienced a drought in the dry season due to forest cut down and replaced by oil palm plantations. Knowledge of the local community on the benefits of low forest, the people considered that the forest does not have better benefits and cut down and converted into oil palm or rubber plantations. Local people do not understand the benefits of ecological and environmental services that forests. From the phenomena in Dharmasraya on land ownership, need to educate the local community about the importance of protecting the forest, and need a strategy to integrate forests management to keep the ecological functions that resemble the woods and counts the economic benefits for the welfare of local communities. One alternative that can be taken is to use forest management models agroforestry smallholders in accordance with the characteristics of the local community who still consider the economic, social and environmental.

Keywords: community, customary land, farmer plantations, and forests

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Authors: Alam Zeb, Glen W. Armstrong, Muhammad Qasim

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Loss of forest cover is one of the most important land cover changes and has been of great concern to policy makers. This study quantified forest cover changes over pre logging ban (1973-1993) and post logging ban (1993-2015) to examine the role of geophysical factors and spatial attributes of land in the two periods. We show that despite a complete ban on green felling, forest cover decreased by 28% and mostly converted to rangeland. Nevertheless, the logging ban was completely effective in controlling agriculture expansion. The binary logistic regression revealed that the south facing aspects at low elevation witnessed more deforestation in the pre-ban period compared to post-ban. Opposite to deforestation, forest degradation was more prominent on the northern aspects at higher elevation during the policy period. Agriculture expansion was widespread in the low elevation flat areas with gentle slope, while during the policy period agriculture contraction in the form of regeneration was observed on the low elevation areas of north facing slopes. All proximity variables, except distance to administrative boundary, showed a similar trend across the two periods and were important explanatory variables in understanding forest and agriculture expansion. The changes in determinants of forest and agriculture expansion and contraction over the two periods might be attributed to the influence of policy and a general decrease in resource availability.

Keywords: forest conservation , wood harvesting ban, logistic regression, deforestation, forest degradation, agriculture expansion, Chitral, Pakistan

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Authors: Francisco Domingos Francisco

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Tropical forests are key providers of many Ecosystem Services (ES), contributing to human wellbeing on a global and local scale. Communities around and within Moribane Forest Reserve (MFR), Manica Province - Mozambique, benefit from ES through the exploitation of non-wood and wood forest products. The objective was to assess the provisioning capacity of the MFR in woody forest products in species and profiles of interest to local communities in the main sources of extraction. Social data relating to the basic needs of local communities for these products were captured through an exploratory study before this one. From that study, it became known about the most collected wood species, the sources of collection, and their availability in the profiles of greatest interest to them. A field survey through 39 rectangular 50mx20m plots was conducted with 13 plots established in each of the three land-use types (LUT), namely Restricted Forest, Unrestricted Forest, and Disturbed areas. The results show that 89 species were identified, of which 28 (31.4%) are assumed to be the most used by the communities. The number of species of local interest does not vary across the LUT (p>0.05). The most used species (MUS) is distributed in 82% in Restricted Forest, 75% in Unrestricted, and also 75% in Disturbed. Most individuals of both general and MUS found in Unrestricted Forest, and Degraded areas have lower end profiles (5-7 cm), representing 0.77 and 0.26%, respectively. The profile of individuals of species of local interest varies by LUT (p<0.05), and their greatest proportion (0.51%) outside the lower end is found in Restricted Forest. There were no similarities between the LUT for the species in general (JCI <0.5) but between the MUS (JCI >0.5). Conclusion, the areas authorized for the exploitation of wood forest products in the MFR tend to reduce their ability to provide local communities with forest products in species and profiles of their interest. This reduction item is a serious threat to the biodiversity of the Restricted Forest. The study can help the academic community in future studies by replicating the methodology used for monitoring purposes or conducting studies in other similar areas, and the results may support decision-makers in designing better strategies for sustainability.

Keywords: ecosystem services, land-use types, local communities, species profile, wellbeing, wood forest product

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Authors: Shashi Bhushan, Sucharita Sen

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In India, forest and people are inextricably linked since millions of people live adjacent to or within protected areas and harvest forest products. Indian forest has their own legacy to sustain by its own climatic nature with several social, economic and cultural activities. People surrounding forest areas are not only dependent on this resource for their livelihoods but also for the other source, like religious ceremonies, social customs and herbal medicines, which are determined by the forest like agricultural land, groundwater level, and soil fertility. The assumption that fuelwood and fodder extraction, which is the part of local livelihood leads to deforestation, has so far been the dominant mainstream views in deforestation discourses. Given the occupational division across social groups in Kaimur reserve forest, the differential nature of dependence of forest resources is important to understand. This paper attempts to assess the nature of dependence and impact of forest degradation on rural households across various social groups. Also, an additional element that is added to the enquiry is the way degradation of forests leading to scarcity of forest-based resources impacts the patterns of dependence across various social groups. Change in forest area calculated through land use land cover analysis using remote sensing technique and examination of different economic activities carried out by the households that are forest-based was collected by primary survey in Kaimur reserve forest of state of Bihar in India. The general finding indicates that the Scheduled Tribe and Scheduled Caste communities, the most socially and economically deprived sections of the rural society are involved in a significant way in collection of fuelwood, fodder, and fruits, both for self-consumption and sale in the market while other groups of society uses fuelwood, fruit, and fodder for self-use only. Depending on the local forest resources for fuelwood consumption was the primary need for all social groups due to easy accessibility and lack of alternative energy source. In last four decades, degradation of forest made a direct impact on rural community mediated through the socio-economic structure, resulting in a shift from forest-based occupations to cultivation and manual labour in agricultural and non-agricultural activities. Thus there is a need to review the policies with respect to the ‘community forest management’ since this study clearly throws up the fact that engagement with and dependence on forest resources is socially differentiated. Thus tying the degree of dependence and forest management becomes extremely important from the view of ‘sustainable’ forest resource management. The statization of forest resources also has to keep in view the intrinsic way in which the forest-dependent population interacts with the forest.

Keywords: forest degradation, livelihood, social groups, tribal community

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Authors: Viacheslav Shkuratskyy, Aminu Bello Usman, Michael O’Dea, Saifur Rahman Sabuj

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This paper examines relationships between solar activity and earthquakes; it applied machine learning techniques: K-nearest neighbour, support vector regression, random forest regression, and long short-term memory network. Data from the SILSO World Data Center, the NOAA National Center, the GOES satellite, NASA OMNIWeb, and the United States Geological Survey were used for the experiment. The 23rd and 24th solar cycles, daily sunspot number, solar wind velocity, proton density, and proton temperature were all included in the dataset. The study also examined sunspots, solar wind, and solar flares, which all reflect solar activity and earthquake frequency distribution by magnitude and depth. The findings showed that the long short-term memory network model predicts earthquakes more correctly than the other models applied in the study, and solar activity is more likely to affect earthquakes of lower magnitude and shallow depth than earthquakes of magnitude 5.5 or larger with intermediate depth and deep depth.

Keywords: k-nearest neighbour, support vector regression, random forest regression, long short-term memory network, earthquakes, solar activity, sunspot number, solar wind, solar flares

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Authors: Kiomars Sefidi

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Dead and habitat tree such as fallen logs, snags, stumps and cracks and loos bark etc. is regarded as an important ecological component of forests on which many forest dwelling species depend, yet its relation to management history in Caspian forest has gone unreported. The aim of research was to compare the amounts of dead tree and habitat in the forests with historically different intensities of management, including: forests with the long term implication of management (PS), the short-term implication of management (NS) which were compared with semi virgin forest (GS). The number of 405 individual dead and habitat trees were recorded and measured at 109 sampling locations. ANOVA revealed volume of the dead tree in the form and decay classes significantly differ within sites and dead volume in the semi virgin forest significantly higher than managed sites. Comparing the amount of dead and habitat tree in three sites showed that dead tree volume related with management history and significantly differ in three study sites. Also, the numbers of habitat trees including cavities, Cracks and loose bark and Fork split trees significantly vary among sites. Reaching their highest in virgin site and their lowest in the site with the long term implication of management, it was concluded that forest management cause reduction of the amount of dead and habitat tree. Forest management history affect the forest's ability to generate dead tree especially in a large size, thus managing this forest according to ecological sustainable principles require a commitment to maintaining stand structure that allow, continued generation of dead tree in a full range of size.

Keywords: forest biodiversity, cracks trees, fork split trees, sustainable management, Fagus orientalis, Iran

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this study is to evaluate the advantages and disadvantages of various machine learning techniques in forecasting ground-motion intensity measures given source characteristics, source-to-site distance, and local site condition. Intensity measures such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Estimating these variables for future earthquake events is a key step in seismic hazard assessment and potentially subsequent risk assessment of different types of structures. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as a statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The algorithms are adjusted to quantify event-to-event and site-to-site variability of the ground motions by implementing them as random effects in the proposed models to reduce the aleatory uncertainty. All the algorithms are trained using a selected database of 4,528 ground-motions, including 376 seismic events with magnitude 3 to 5.8, recorded over the hypocentral distance range of 4 to 500 km in Oklahoma, Kansas, and Texas since 2005. The main reason of the considered database stems from the recent increase in the seismicity rate of these states attributed to petroleum production and wastewater disposal activities, which necessities further investigation in the ground motion models developed for these states. Accuracy of the models in predicting intensity measures, generalization capability of the models for future data, as well as usability of the models are discussed in the evaluation process. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available.

Keywords: artificial neural network, ground-motion models, machine learning, random forest, support vector machine

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Authors: Tanka Nath Dahal

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Community-based forestry is seen as a promising instrument for sustainable forest management (SFM) through the purposeful involvement of local communities. Globally, forest area managed by local communities is on the rise. However, transferring management responsibilities to forest users alone cannot guarantee the sustainability of forest management. A monitoring tool, that allows the local communities to track the progress of forest management towards the goal of sustainability, is essential. A case study, including six forest user groups (FUGs), two from each three community-based forestry models—community forestry (CF), buffer zone community forestry (BZCF), and collaborative forest management (CFM) representing three different physiographic regions, was conducted in Nepal. The study explores which community-based forest management model (CF, BZCF or CFM) is doing well in terms of sustainable forest management. The study assesses the overall performance of the three models towards SFM using locally developed criteria (four), indicators (26) and verifiers (60). This paper attempts to quantify the sustainability of the models using sustainability index for individual criteria (SIIC), and overall sustainability index (OSI). In addition, rating to the criteria and scoring of the verifiers by the FUGs were done. Among the four criteria, the FUGs ascribed the highest weightage to institutional framework and governance criterion; followed by economic and social benefits, forest management practices, and extent of forest resources. Similarly, the SIIC was found to be the highest for the institutional framework and governance criterion. The average values of OSI for CFM, CF, and BZCF were 0.48, 0.51 and 0.60 respectively; suggesting that buffer zone community forestry is the more sustainable model among the three. The study also suggested that the SIIC and OSI help local communities to quantify the overall progress of their forestry practices towards sustainability. The indices provided a clear picture of forest management practices to indicate the direction where they are heading in terms of sustainability; and informed the users on issues to pay attention to enhancing the sustainability of their forests.

Keywords: community forestry, collaborative management, overall sustainability, sustainability index for individual criteria

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Authors: Keshav Raj Acharya, Thakur Silwal, Neelam C. Poudyal

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Buffer zones, the peripheral areas around the national parks and wildlife reserves, are available for the purpose of benefitting the local inhabitants by providing forest products for subsistence needs of basic forest products outside the protected areas. The forest area within the buffer zone has been managed as a buffer zone community forest (BZCF) for the last 25 years after the approval of the buffer zone management regulation 1996. With a case study of select BZCF in Bardia National Park, this study aims to analyze whether the benefit provided by BZCF is equally available to poor users among other socioeconomic classes of the users. The findings are based on the analysis of cross-sectional data involving household surveys (n=305) and key informants’ interviews (n=10) as well as office records available at different 5 buffer zone community forest user groups offices. Results indicate that despite the provisions of subsidized rates for poor; poor households were more deprived due to higher forest products price particularly, the timber price in buffer zone. Evidence also indicate that due to the increased forest coverage, the incidence of wildlife damage has also increased and impacted the poor more due to lack of land ownership as well as limited alternatives. Clear community forest management guidelines with equitable benefit sharing and compensatory mechanisms to the users of poor socioeconomic class have been identified as a solution to increase the benefit to poor users in BZCFUGs.

Keywords: crop depredation, forest products, users, wellbeing ranking

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Authors: Admassu Addi, Sebesebe Demissew, Teshome Soromessa, Zemede Asfaw

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This study measures the carbon stock of the Moist Afromontane Gesha-Sayilem forest found in Gesha and Sayilem District in southwest Ethiopia. A stratified sampling method was used to identify the number of sampling point through the Global Positioning System. A total of 90 plots having nested plots to collect tree species and soil data were demarcated. The results revealed that the total carbon stock of the forest was 362.4 t/ha whereas the above ground carbon stock was 174.95t/ha, below ground litter, herbs, soil, and dead woods were 34.3,1.27, 0.68, 128 and 23.2 t/ha (up to 30 cm depth) respectively. The Gesha- Sayilem Forest is a reservoir of high carbon and thus acts as a great sink of the atmospheric carbon. Thus conservation of the forest through introduction REDD+ activities is considered an appropriate action for mitigating climate change.

Keywords: carbon sequestration, carbon stock, climate change, allometric, Ethiopia

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Authors: Dammar Bahadur Adhikari

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Community forestry in Nepal has got disproportionately high level of support from government and other actors in forestry sector. Even though master plan for forestry sector (1989) has highlighted community and private forestry as one component, the government policies and other intervention deliberately left out private forestry in its structure and programs. The study aimed at providing the pathway for formulating appropriate policies to address need of different kind of forest management regimes in Rautahat district, Nepal. The key areas the research focused were assessment of current status of private forestry, community forest users' understanding on private forestry; criteria for choosing species of private forestry and factors affecting establishment of private forestry in the area. Qualitative and quantitative data were collected employing questionnaire survey, rapid forest assessment and key informant interview. The study found out that forest policies are imposed due to intense pressure of exogenous forces than due to endogenous demand. Most of the local people opine that their traditional knowledge and skills are not sufficient for private forestry and hence need training on the matter. Likewise, local use, market value and rotation dictate the choice of species for plantation in private forests. Currently district forest office is the only government institution working in the area of private forestry all other governmental and non-governmental organizations have condoned. private forestry. Similarly, only permanent settlers in the area are found to establish private forests other forest users such as migrants and forest encroachers follow opportunistic behavior to meet their forest product need from community and national forests. In this regard, the study recommends taking appropriate step to support other forest management system including private forestry provide community forestry the benefits of competition as suggested by Darwin in 18th century, one and half century back and to help alleviate poverty by channelizing benefits to household level.

Keywords: community forest, forest management, poverty, private forest, users’ group

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Authors: Naduni Ranasinghe

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E-learning environments are getting more popular than any other due to the impact of COVID19. Even though e-learning is one of the best solutions for the teaching-learning process in the academic process, it’s not without major challenges. Nowadays, machine learning approaches are utilized in the analysis of how behavioral factors lead to better adoption and how they related to better performance of the students in eLearning environments. During the pandemic, we realized the academic process in the eLearning approach had a major issue, especially for the performance of the students. Therefore, an approach that investigates student behaviors in eLearning environments using a data-intensive machine learning approach is appreciated. A hybrid approach was used to understand how each previously told variables are related to the other. A more quantitative approach was used referred to literature to understand the weights of each factor for adoption and in terms of performance. The data set was collected from previously done research to help the training and testing process in ML. Special attention was made to incorporating different dimensionality of the data to understand the dependency levels of each. Five independent variables out of twelve variables were chosen based on their impact on the dependent variable, and by considering the descriptive statistics, out of three models developed (Random Forest classifier, SVM, and Decision tree classifier), random forest Classifier (Accuracy – 0.8542) gave the highest value for accuracy. Overall, this work met its goals of improving student performance by identifying students who are at-risk and dropout, emphasizing the necessity of using both static and dynamic data.

Keywords: academic performance prediction, e learning, learning analytics, machine learning, predictive model

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Authors: Armand Okende, Benjamin Beaumont

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Introduction: Deforestation is a change in the structure and composition of flora and fauna, which leads to a loss of biodiversity, production of goods and services and an increase in fires. It concerns vast territories in tropical zones particularly; this is the case of the territory of Bolobo in the current province of Maï- Ndombe in the Democratic Republic of Congo. Indeed, through this study between 2001 and 2018, we believe that it was important to show and analyze quantitatively the important forests changes and analyze quantitatively. It’s the overall objective of this study because, in this area, we are witnessing significant deforestation. Methodology: Mapping and quantification are the methodological approaches that we have put forward to assess the deforestation or forest changes through satellite images or raster layers. These satellites data from Global Forest Watch are integrated into the GIS software (GRASS GIS and Quantum GIS) to represent the loss of forest cover that has occurred and the various changes recorded (e.g., forest gain) in the territory of Bolobo. Results: The results obtained show, in terms of quantifying deforestation for the periods 2001-2006, 2007-2012 and 2013-2018, the loss of forest area in hectares each year. The different change maps produced during different study periods mentioned above show that the loss of forest areas is gradually increasing. Conclusion: With this study, knowledge of forest management and protection is a challenge to ensure good management of forest resources. To do this, it is wise to carry out more studies that would optimize the monitoring of forests to guarantee the ecological and economic functions they provide in the Congo Basin, particularly in the Democratic Republic of Congo. In addition, the cartographic approach, coupled with the geographic information system and remote sensing proposed by Global Forest Watch using raster layers, provides interesting information to explain the loss of forest areas.

Keywords: deforestation, loss year, forest change, remote sensing, drivers of deforestation

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Authors: Artur Krukowski, Emmanouela Vogiatzaki

Abstract:

The article presents results from the AF3 project “Advanced Forest Fire Fighting” focused on Unmanned Aircraft Systems (UAS)-based 3D surveillance and 3D area mapping using high-resolution photogrammetric methods from multispectral imaging, also taking advantage of the 3D scanning techniques from the SCAN4RECO project. We also present a proprietary embedded sensor system used for the detection of fire ignitions in the forest using near-infrared based scanner with weight and form factors allowing it to be easily deployed on standard commercial micro-UAVs, such as DJI Inspire or Mavic. Results from real-life pilot trials in Greece, Spain, and Israel demonstrated added-value in the use of UAS for precise and reliable detection of forest fires, as well as high-resolution 3D aerial modeling for accurate quantification of human resources and equipment required for firefighting.

Keywords: forest wildfires, surveillance, fuel volume estimation, firefighting, ignition detectors, 3D modelling, UAV

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Authors: Tarek Dandashi

Abstract:

WSNs may provide a fast and reliable solution for the early detection of environment events like forest fires. This is crucial for alerting and calling for fire brigade intervention. Sensor nodes communicate sensor data to a host station, which enables a global analysis and the generation of a reliable decision on a potential fire and its location. A WSN with TinyOS and nesC for the capturing and transmission of a variety of sensor information with controlled source, data rates, duration, and the records/displaying activity traces is presented. We propose a similarity distance (SD) between the distribution of currently sensed data and that of a reference. At any given time, a fire causes diverging opinions in the reported data, which alters the usual data distribution. Basically, SD consists of a metric on the Cumulative Distribution Function (CDF). SD is designed to be invariant versus day-to-day changes of temperature, changes due to the surrounding environment, and normal changes in weather, which preserve the data locality. Evaluation shows that SD sensitivity is quadratic versus an increase in sensor node temperature for a group of sensors of different sizes and neighborhood. Simulation of fire spreading when ignition is placed at random locations with some wind speed shows that SD takes a few minutes to reliably detect fires and locate them. We also discuss the case of false negative and false positive and their impact on the decision reliability.

Keywords: forest fire, WSN, wireless sensor network, algortihm

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Authors: Mojo Mengistu Gelasso

Abstract:

The tropical forest is considered the most important forest ecosystem for mitigating climate change by sequestering a high amount of carbon. The potential carbon stock of the forest can be influenced by many factors. Therefore, studying these factors is crucial for understanding the determinants that affect the potential for woody carbon storage in the forest. This study was conducted to evaluate the potential for woody carbon stock and how it varies based on plant community types, as well as along altitudinal, slope, and aspect gradients in the Munessa dry Afromontane forest. Vegetation data was collected using systematic sampling. Five line transects were established at 100 m intervals along the altitudinal gradient between two consecutive transect lines. On each transect, 10 quadrats (20 x 20 m), separated by 200 m, were established. The woody carbon was estimated using an appropriate allometric equation formulated for tropical forests. The data was analyzed using one-way ANOVA in R software. The results showed that the total woody carbon stock of the Munessa forest was 210.43 ton/ha. The analysis of variance revealed that woody carbon density varied significantly based on environmental factors, while community types had no significant effect. The highest mean carbon stock was found at middle altitudes (2367-2533 m.a.s.l), lower slopes (0-13%), and west-facing aspects. The Podocarpus falcatus-Croton macrostachyus community type also contributed a higher woody carbon stock, as larger tree size classes and older trees dominated it. Overall, the potential for woody carbon sequestration in this study was strongly associated with environmental variables. Additionally, the uneven distribution of species with larger diameter at breast height (DBH) in the study area might be linked to anthropogenic factors, as the current forest growth indicates characteristics of a secondary forest. Therefore, our study suggests that the development and implementation of a sustainable forest management plan is necessary to increase the carbon sequestration potential of this forest and mitigate climate change.

Keywords: munessa forest, woody carbon stock, environmental factors, climate mitigation

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Authors: Mengistu Gelasso Mojo

Abstract:

The tropical forest is considered the most important forest ecosystem for mitigating climate change by sequestering a high amount of carbon. The potential carbon stock of the forest can be influenced by many factors. Therefore, studying these factors is crucial for understanding the determinants that affect the potential for woody carbon storage in the forest. This study was conducted to evaluate the potential for woody carbon stock and how it varies based on plant community types, as well as along altitudinal, slope, and aspect gradients in the Munessa dry Afromontane forest. Vegetation data was collected using systematic sampling. Five line transects were established at 100 m intervals along the altitudinal gradient between two consecutive transect lines. On each transect, 10 quadrats (20 x 20 m), separated by 200 m, were established. The woody carbon was estimated using an appropriate allometric equation formulated for tropical forests. The data was analyzed using one-way ANOVA in R software. The results showed that the total woody carbon stock of the Munessa forest was 210.43 ton/ha. The analysis of variance revealed that woody carbon density varied significantly based on environmental factors, while community types had no significant effect. The highest mean carbon stock was found at middle altitudes (2367-2533 m.a.s.l), lower slopes (0-13%), and west-facing aspects. The Podocarpus falcatus-Croton macrostachyus community type also contributed a higher woody carbon stock, as larger tree size classes and older trees dominated it. Overall, the potential for woody carbon sequestration in this study was strongly associated with environmental variables. Additionally, the uneven distribution of species with larger diameter at breast height (DBH) in the study area might be linked to anthropogenic factors, as the current forest growth indicates characteristics of a secondary forest. Therefore, our study suggests that the development and implementation of a sustainable forest management plan is necessary to increase the carbon sequestration potential of this forest and mitigate climate change.

Keywords: munessa forest, woody carbon stock, environmental factors, climate mitigation

Procedia PDF Downloads 37

Authors: Said Alshukri, Mazhar Hussain Malik

Abstract:

Green hydrogen is hydrogen produced using renewable energy sources. In the last few years, Oman aimed to reduce its dependency on fossil fuels. Recently, the hydrogen economy has become a global trend, and many countries have started to investigate the feasibility of implementing this sector. Oman created an alliance to establish the policy and rules for this sector. With motivation coming from both global and local interest in green hydrogen, this paper investigates the potential of producing hydrogen from wind and solar energies in three different locations in Oman, namely Duqm, Salalah, and Sohar. By using machine learning-based software “WEKA” and local metrological data, the project was designed to figure out which location has the highest wind and solar energy potential. First, various supervised models were tested to obtain their prediction accuracy, and it was found that the Random Forest (RF) model has the best prediction performance. The RF model was applied to 2021 metrological data for each location, and the results indicated that Duqm has the highest wind and solar energy potential. The system of one wind turbine in Duqm can produce 8335 MWh/year, which could be utilized in the water electrolysis process to produce 88847 kg of hydrogen mass, while a solar system consisting of 2820 solar cells is estimated to produce 1666.223 MWh/ year which is capable of producing 177591 kg of hydrogen mass.

Keywords: green hydrogen, machine learning, wind and solar energies, WEKA, supervised models, random forest

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Authors: Saba Gachpaz, Hamid Reza Heidari

Abstract:

The sharp increase in population growth leads to more pressure on agricultural areas to satisfy the food supply. To achieve this, more resources should be consumed and, besides other environmental concerns, highlight sustainable agricultural development. Land-use management is a crucial factor in obtaining optimum productivity. Machine learning is a widely used technique in the agricultural sector, from yield prediction to customer behavior. This method focuses on learning and provides patterns and correlations from our data set. In this study, nine physical control factors, namely, soil classification, electrical conductivity, normalized difference water index (NDWI), groundwater level, elevation, annual precipitation, pH of water, annual mean temperature, and slope in the alluvial plain in Khuzestan (an agricultural hotspot in Iran) are used to decide the best agricultural land use for both rainfed and irrigated agriculture for ten different crops. For this purpose, each variable was imported into Arc GIS, and a raster layer was obtained. In the next level, by using training samples, all layers were imported into the python environment. A random forest model was applied, and the weight of each variable was specified. In the final step, results were visualized using a digital elevation model, and the importance of all factors for each one of the crops was obtained. Our results show that despite 62% of the study area being allocated to agricultural purposes, only 42.9% of these areas can be defined as a suitable class for cultivation purposes.

Keywords: land suitability, machine learning, random forest, sustainable agriculture

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Authors: Songul Cinaroglu

Abstract:

Regional planning in health has gained speed for developing countries in recent years. In Turkey, 89 different Public Hospital Unions (PHUs) were conducted based on provincial levels. In this study technical efficiencies of 89 PHUs were examined by using Data Envelopment Analysis (DEA) and machine learning techniques by dividing them into two clusters in terms of similarities of input and output indicators. Number of beds, physicians and nurses determined as input variables and number of outpatients, inpatients and surgical operations determined as output indicators. Before performing DEA, PHUs were grouped into two clusters. It is seen that the first cluster represents PHUs which have higher population, demand and service density than the others. The difference between clusters was statistically significant in terms of all study variables (p ˂ 0.001). After clustering, DEA was performed for general and for two clusters separately. It was found that 11% of PHUs were efficient in general, additionally 21% and 17% of them were efficient for the first and second clusters respectively. It is seen that PHUs, which are representing urban parts of the country and have higher population and service density, are more efficient than others. Random forest decision tree graph shows that number of inpatients is a determinative factor of efficiency of PHUs, which is a measure of service density. It is advisable for public health policy makers to use statistical learning methods in resource planning decisions to improve efficiency in health care.

Keywords: public hospital unions, efficiency, data envelopment analysis, random forest

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Authors: Ehikhamele Isaac Erhomosele, Ogbogu Sunday Sylvester

Abstract:

A biodiversity survey was conducted in Shasha Forest Reserve, Ife southern, Osun State, Nigeria between May 2019 to April 2021 with a view to determining the nature of Odonata fauna of the forest. A total number of 1055 individuals of adult dragonflies and damselflies belonging to 8 families (Aeshnidae, Calopterygidae, Chlorocyphidae, Coenagrionidae, Gomphidae, Lestidae, Libellulidae and Platycnemididae) were recorded. Five (5) of these families of which belong to the suborder Zygoptera, and the remaining three (3) to Anisoptera. Libellulidae was the most abundant family while Gomphidae recorded the least representative. No new species of Odonata was recorded. Most of the families reported were previously been documented in the tropical region. Logging activities and human disturbance have been attributed to the nature of Odonata species in the Forest. It is therefore recommended that logging should be restricted to designated areas of the forest and regulated by the Department of Forestry of Osun State Ministry of Agriculture and Natural Resources in conjunction with the Federal Ministry of Environment.

Keywords: checklist, Odonata, Shasha, families

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Authors: John White

Abstract:

This study investigated the effects of practice schedules on learning and performance in golf putting, specifically focusing on the impact of increasing contextual interference (CI). University students (n=7) were randomly assigned to blocked, random, or increasing practice schedules. During acquisition, participants performed 135 putting trials using different weighted golf balls. The blocked group followed a specific sequence of ball weights, while the random group practiced with the balls in a random order. The increasing group started with a blocked schedule, transitioned to a serial schedule, and concluded with a random schedule. Retention and transfer tests were conducted 24 hours later. The results indicated that high levels of CI (random practice) were more beneficial for learning than low levels of CI (blocked practice). The increasing practice schedule, incorporating blocked, serial, and random practice, demonstrated advantages over traditional blocked and random schedules. Additionally, EEG was used to explore the neurophysiological effects of the increasing practice schedule.

Keywords: skill acquisition, motor control, learning, contextual interference

Procedia PDF Downloads 59