Search results for: weed infestation forecast

Authors: M. Reza Hashemi, Chris Small, Scott Hayward

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The Northeast Coast of the US faces damaging effects of coastal flooding and winds due to Atlantic tropical and extratropical storms each year. Historically, several large storm events have produced substantial levels of damage to the region; most notably of which were the Great Atlantic Hurricane of 1938, Hurricane Carol, Hurricane Bob, and recently Hurricane Sandy (2012). The objective of this study was to develop an integrated modeling system that could be used as a forecasting/hindcasting tool to evaluate and communicate the risk coastal communities face from these coastal storms. This modeling system utilizes the ADvanced CIRCulation (ADCIRC) model for storm surge predictions and the Simulating Waves Nearshore (SWAN) model for the wave environment. These models were coupled, passing information to each other and computing over the same unstructured domain, allowing for the most accurate representation of the physical storm processes. The coupled SWAN-ADCIRC model was validated and has been set up to perform real-time forecast simulations (as well as hindcast). Modeled storm parameters were then passed to a coastal risk assessment tool. This tool, which is generic and universally applicable, generates spatial structural damage estimate maps on an individual structure basis for an area of interest. The required inputs for the coastal risk model included a detailed information about the individual structures, inundation levels, and wave heights for the selected region. Additionally, calculation of wind damage to structures was incorporated. The integrated coastal risk assessment system was then tested and applied to Charlestown, a small vulnerable coastal town along the southern shore of Rhode Island. The modeling system was applied to Hurricane Sandy and a synthetic storm. In both storm cases, effect of natural dunes on coastal risk was investigated. The resulting damage maps for the area (Charlestown) clearly showed that the dune eroded scenarios affected more structures, and increased the estimated damage. The system was also tested in forecast mode for a large Nor’Easters: Stella (March 2017). The results showed a good performance of the coupled model in forecast mode when compared to observations. Finally, a nearshore model XBeach was then nested within this regional grid (ADCIRC-SWAN) to simulate nearshore sediment transport processes and coastal erosion. Hurricane Irene (2011) was used to validate XBeach, on the basis of a unique beach profile dataset at the region. XBeach showed a relatively good performance, being able to estimate eroded volumes along the beach transects with a mean error of 16%. The validated model was then used to analyze the effectiveness of several erosion mitigation methods that were recommended in a recent study of coastal erosion in New England: beach nourishment, coastal bank (engineered core), and submerged breakwater as well as artificial surfing reef. It was shown that beach nourishment and coastal banks perform better to mitigate shoreline retreat and coastal erosion.

Keywords: ADCIRC, coastal flooding, storm surge, coastal risk assessment, living shorelines

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Authors: Ella Tyuryumina, Alexey Neznanov

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Finding algorithms to predict the growth of tumors has piqued the interest of researchers ever since the early days of cancer research. A number of studies were carried out as an attempt to obtain reliable data on the natural history of breast cancer growth. Mathematical modeling can play a very important role in the prognosis of tumor process of breast cancer. However, mathematical models describe primary tumor growth and metastases growth separately. Consequently, we propose a mathematical growth model for primary tumor and primary metastases which may help to improve predicting accuracy of breast cancer progression using an original mathematical model referred to CoM-IV and corresponding software. We are interested in: 1) modelling the whole natural history of primary tumor and primary metastases; 2) developing adequate and precise CoM-IV which reflects relations between PT and MTS; 3) analyzing the CoM-IV scope of application; 4) implementing the model as a software tool. The CoM-IV is based on exponential tumor growth model and consists of a system of determinate nonlinear and linear equations; corresponds to TNM classification. It allows to calculate different growth periods of primary tumor and primary metastases: 1) ‘non-visible period’ for primary tumor; 2) ‘non-visible period’ for primary metastases; 3) ‘visible period’ for primary metastases. The new predictive tool: 1) is a solid foundation to develop future studies of breast cancer models; 2) does not require any expensive diagnostic tests; 3) is the first predictor which makes forecast using only current patient data, the others are based on the additional statistical data. Thus, the CoM-IV model and predictive software: a) detect different growth periods of primary tumor and primary metastases; b) make forecast of the period of primary metastases appearance; c) have higher average prediction accuracy than the other tools; d) can improve forecasts on survival of BC and facilitate optimization of diagnostic tests. The following are calculated by CoM-IV: the number of doublings for ‘nonvisible’ and ‘visible’ growth period of primary metastases; tumor volume doubling time (days) for ‘nonvisible’ and ‘visible’ growth period of primary metastases. The CoM-IV enables, for the first time, to predict the whole natural history of primary tumor and primary metastases growth on each stage (pT1, pT2, pT3, pT4) relying only on primary tumor sizes. Summarizing: a) CoM-IV describes correctly primary tumor and primary distant metastases growth of IV (T1-4N0-3M1) stage with (N1-3) or without regional metastases in lymph nodes (N0); b) facilitates the understanding of the appearance period and manifestation of primary metastases.

Keywords: breast cancer, exponential growth model, mathematical modelling, primary metastases, primary tumor, survival

Procedia PDF Downloads 317

Authors: Muhammad Asif Qayyoum, Bilal Saeed Khan

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Poultry red mites (Dermanyssus gallinae De Geer) are economically deleterious parasite of hens in poultry industry in all over the world. Due to lack of proper control managements and result of poor application of commercial products, D. gallinae get resistance and severe infestation in poultry birds. Laboratory experiment was planned for the control of D. gallinae by using different mushroom and plant extracts. We used control treatment (100 ml distilled water) and nine treatments (10 gr Lentinula adobas, Ganoderma lucidum and Pleurotus aryngii with 100 ml methanol, 1% and 2% Neemazal, 1.5% Gamma-T-ol, Echinacea Leaf , 1.5% Fungatol with neem spray and Methanol) with five replication having five mites each. Data collected after 12 and 24 hours every day till mites found dead in every treatment. The significant differences among the mean values were compared with the DUNCAN multiple range test. The efficacy (%) of each treatment was determined with the Abbott formula. All statistical analyses were conducted with the SPSS Version 12 program. Lentinula edodes (80%), Ganoderma lucidum (76%) and Fungatol+Neem spray (1.5%) (80%) were significant against D. gallinae within 3 days.

Keywords: mushroom extracts, plant extracts, D. gallinae, control

Procedia PDF Downloads 288

Authors: Yakubu Adamu, Baba Alfa, Salahudeen Adamu Gene

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As the drive towards clean, renewable and sustainable energy generation is gradually been reshaped by renewable penetration over time, energy storage has thus, become an optimal solution for utilities looking to reduce transmission and capacity cost, therefore the need for capacity resources to be adjusted accordingly such that renewable energy storage may have the opportunity to substitute for retiring conventional energy systems with higher capacity factors. Considering the Nigeria scenario, where Over 80% of the current Nigerian primary energy consumption is met by petroleum, electricity demand is set to more than double by mid-century, relative to 2025 levels. With renewable energy penetration rapidly increasing, in particular biomass, hydro power, solar and wind energy, it is expected to account for the largest share of power output in the coming decades. Despite this rapid growth, the imbalance between load and resources has created a hindrance to the development of energy storage capacity, load and resources, hence forecasting energy storage capacity will therefore play an important role in maintaining the balance between load and resources including supply and demand. Therefore, the degree to which this might occur, its timing and more importantly its sustainability, is the subject matter of the current research. Here, we forecast the future energy storage capacity rating and thus, evaluate the load and resource scenario in Nigeria. In doing so, We used the scenario-based International Energy Agency models, the projected energy demand and supply structure of the country through 2030 are presented and analysed. Overall, this shows that in high renewable (solar) penetration scenarios in Nigeria, energy storage with 4-6h duration can obtain over 86% capacity rating with storage comprising about 24% of peak load capacity. Therefore, the general takeaway from the current study is that most power systems currently used has the potential to support fairly large penetrations of 4-6 hour storage as capacity resources prior to a substantial reduction in capacity ratings. The data presented in this paper is a crucial eye-opener for relevant government agencies towards developing these energy resources in tackling the present energy crisis in Nigeria. However, if the transformation of the Nigeria. power system continues primarily through expansion of renewable generation, then longer duration energy storage will be needed to qualify as capacity resources. Hence, the analytical task from the current survey will help to determine whether and when long-duration storage becomes an integral component of the capacity mix that is expected in Nigeria by 2030.

Keywords: capacity, energy, power system, storage

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Authors: Yu-Wen Chen, Szu-Wei Huang, Chung-Hsiang Mu, Kelvin Cheng

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This paper developed a data-driven based model to deal with the causality between the Continuous Emission Monitoring System (CEMS, by Environmental Protection Administration, Taiwan) in industrial factories, and the air quality around environment. Compared to the heavy burden of traditional numerical models of regional weather and air pollution simulation, the lightweight burden of the proposed model can provide forecasting hourly with current observations of weather, air pollution and emissions from factories. The observation data are included wind speed, wind direction, relative humidity, temperature and others. The observations can be collected real time from Open APIs of civil IoT Taiwan, which are sourced from 439 weather stations, 10,193 qualitative air stations, 77 national quantitative stations and 140 CEMS quantitative industrial factories. This study completed a causal inference engine and gave an air pollution forecasting for the next 12 hours related to local industrial factories. The outcomes of the pollution forecasting are produced hourly with a grid resolution of 1km*1km on IIoTC (Industrial Internet of Things Cloud) and saved in netCDF4 format. The elaborated procedures to generate forecasts comprise data recalibrating, outlier elimination, Kriging Interpolation and particle tracking and random walk techniques for the mechanisms of diffusion and advection. The solution of these equations reveals the causality between factories emission and the associated air pollution. Further, with the aid of installed real-time flue emission (Total Suspension Emission, TSP) sensors and the mentioned forecasted air pollution map, this study also disclosed the converting mechanism between the TSP and PM2.5/PM10 for different region and industrial characteristics, according to the long-term data observation and calibration. These different time-series qualitative and quantitative data which successfully achieved a causal inference engine in cloud for factory management control in practicable. Once the forecasted air quality for a region is marked as harmful, the correlated factories are notified and asked to suppress its operation and reduces emission in advance.

Keywords: continuous emission monitoring system, total suspension particulates, causal inference, air pollution forecast, IoT

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Authors: Diqin Qi, Jiaming Li, Siman Li

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Based on the use of the three-point smoothing method for selectively processing original data columns, this paper establishes a group of grey GM(1,1) models to predict the concentration ranges of four major air pollutants in Shanghai from 2023 to 2024. The results indicate that PM₁₀, SO₂, and NO₂ maintain the national Grade I standards, while the concentration of PM₂.₅ has decreased but still remains within the national Grade II standards. Combining the forecast results, recommendations are provided for the Shanghai municipal government's efforts in air pollution prevention and control.

Keywords: atmospheric pollutant prediction, Grey GM(1, 1), model group, three-point smoothing method

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Authors: Diogo de Carvalho Cabral

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This article offers a non-declensionist account of tropical deforestation, arguing that, rather than social stamp upon the environment or ecological endgame, deforestation is part of social site-making and remaking, the process through which humans produce sociality by carrying out nature-mediated – and therefore nature-transforming – practices that inevitably reset the very conditions of those practices. Human landscape-shaping inadvertently alters other species’ habitats –most often decimating them, but sometimes improving them–, the outcomes of which always resonate back upon human inhabitation and land use. Despite the overall tendency of biotic homogenization resulting from modern deforestation processes, there are always winners, i.e., species that gain competitive advantages enabling them to thrive in the novel ecosystems. Here it is examined one such case of deforestation-boosted species, namely leafcutter ants, which wrought havoc in the rural landscapes of nineteenth-century Brazil by defoliating a wide range of crops. By combining Historical GIS analysis and qualitative interpretation, it is shown how agricultural deforestation might have changed the ant species' biogeographies, and how in turn these changes – construed as 'infestation' – stimulated social innovations and rearrangements such as technical ingenuity, legal-administrative practices, and even local electoral arenas.

Keywords: deforestation, leafcutter ants, nineteenth-century Brazil, socio-ecological change

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Authors: Meimei Zheng

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Product personalization could not only bring new profits to companies but also provide the direction of long-term development for companies. However, the characteristics of personalized product cause some new problems. This paper investigates how companies make decisions on the supply of personalized products when facing different customer attitudes to personalized product and service, constraints due to limited capacity and updates of personalized demand information. This study will provide optimal decisions for companies to develop personalized markets, resulting in promoting business transformation and improving business competitiveness.

Keywords: demand forecast updating, limited capacity, personalized products, optimization

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Authors: Valeria Bondarenko

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We proposed algorithms for: estimation of parameters fBm (volatility and Hurst exponent) and for the approximation of random time series by functional of fBm. We proved the consistency of the estimators, which constitute the above algorithms, and proved the optimal forecast of approximated time series. The adequacy of estimation algorithms, approximation, and forecasting is proved by numerical experiment. During the process of creating software, the system has been created, which is displayed by the hierarchical structure. The comparative analysis of proposed algorithms with the other methods gives evidence of the advantage of approximation method. The results can be used to develop methods for the analysis and modeling of time series describing the economic, physical, biological and other processes.

Keywords: mathematical model, random process, Wiener process, fractional Brownian motion

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Authors: Lei Ren, Michael Hartnett, Stephen Nash

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The abundant measured surface current from HF radar system in coastal area is assimilated into model to improve the modeling forecasting ability. A simple sequential data assimilation scheme, Direct Insertion (DI), is applied to update model forecast states. The influence of Direct Insertion data assimilation over time is analyzed at one reference point. Vector maps of surface current from models are compared with HF radar measurements. Root-Mean-Squared-Error (RMSE) between modeling results and HF radar measurements is calculated during the last four days with no data assimilation.

Keywords: data assimilation, CODAR, HF radar, surface current, direct insertion

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Authors: Alexander Gretchenko, Liudmila Goncharenko, Sergey Sybachin

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This article summarizes the history of the discovery of N.D. Kondratiev of large cycles of economic conditions, as well as the creation and justification of the theory of innovation-cyclical economic development of Kondratiev-Schumpeter. An analysis of it in modern conditions is providing. The main conclusion in this article is that in general terms today it can be argued that the Kondratiev-Schumpeter theory is sufficiently substantiated. Further, the possibility of making a forecast of the development of the economic situation in the direction of applying this theory in practice, which demonstrate its effectiveness, is considered.

Keywords: Kondratiev's big cycles of economic conjuncture, Schumpeter's theory of innovative economic development, long-term cyclical forecasting, dating of Kondratiev cycles

Procedia PDF Downloads 138

Authors: Zakia Alioua, Amira Soumia, Zerouali-Khodja Fatiha

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In spite of a wide variety of contaminants such as heavy metals and organic compounds in addition to the importance of extended pollution, the deep-sea and its species are not in haven and being affected through contaminants exposure. This investigation is performed in order to provide data on the presence of pathological changes in the liver and gonads of the greater forkbeard. A total of 998 specimens of the teleost fish Phycis blennoides Brünnich, 1768 ranged from 5,7 to 62,7 cm in total length, were obtained from the commercial fisheries of Algerian ports. The sampling has been carried out monthly from December 2013 to June 2015 and from January to June 2016 caught by trawlers and longlines between 75 and 600 fathoms in the coast of Algeria. Individuals were sexed their gonads, and their livers were removed and processed for light microscopy and one case of atresia was identified. In whole, overall 0,002% of the specimens presented some degree of liver steatosis. For the gastric section, 442 selected stomachs contents were observed looking for parasitic infestation and enumerate 212 nematodes. A prospecting survey for metal contaminant was performed on the liver by atomic absorption spectrophotometry analysis.

Keywords: atresia, coast of Algeria, histopathology, nematode, Phycis blennoides, steatosis

Procedia PDF Downloads 208

Authors: H. H. Goh, K. C. Goh, W. N. Z. S. Wan Sukri, Q. S. Chua, S. W. Lee, B. C. Kok

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Endowed of renewable energy sources (RES) are the advantages of ASEAN, but they are using a low amount of RES only to generate electricity because their primary energy sources are fossil and coal. The cost of purchasing fossil and coal is cheaper now, but it might be expensive soon, as it will be depleted sooner and after. ASEAN showed that the RES are convenient to be implemented. Some country in ASEAN has huge renewable energy sources potential and use. The primary aim of this project is to assist ASEAN countries in preparing the renewable energy and to guide the policies for RES in the more upright direction. The Green-Y model will help ASEAN government to study and forecast the economic concept, including feed-in tariff.

Keywords: ASEAN RES, Renewable Energy, RES Policies, RES Potential, RES Utilization

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Authors: Adil Rasheed, Mandar Tabib

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The paper describes the ability of a high-resolution Computational Fluid Dynamics model to predict terrain-induced turbulence and wind shear close to the ground. Various sensitivity studies to choose the optimal simulation setup for modeling the flow characteristics in a complex terrain are presented. The capabilities of the model are demonstrated by applying it to the Sandnessjøen Airport, Stokka in Norway, an airport that is located in a mountainous area. The model is able to forecast turbulence in real time and trigger an alert when atmospheric conditions might result in high wind shear and turbulence.

Keywords: aviation safety, terrain-induced turbulence, atmospheric flow, alert system

Procedia PDF Downloads 395

Authors: Daniil Karzanov

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This paper studies the effect of quarterly earnings reports on the stock price. The profitability of the stock is modeled by geometric Brownian diffusion and the Constant Elasticity of Variance model. We fit several variations of stochastic differential equations to the pre-and after-report period using the Maximum Likelihood Estimation and Grid Search of parameters method. By examining the change in the model parameters after reports’ publication, the study reveals that the reports have enough evidence to be a structural breakpoint, meaning that all the forecast models exploited are not applicable for forecasting and should be refitted shortly.

Keywords: stock market, earnings reports, financial time series, structural breaks, stochastic differential equations

Procedia PDF Downloads 173

Authors: Musadiq Idris, Sajjad Ali, Syed A. Khaliq, Umer Farooq

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Postmortem record of 217 captive ungulates including Black-buck (n=31), Chinkara (n=20), Hog deer (n=116), Spotted deer (n=35), Red Deer n=(04), and Rusa deer (n=11) submitted to the Veterinary Research Institute, Lahore, Pakistan was analyzed to determine the primary cause of mortality in these animals. The submissions included temporal distribution from Government wildlife captive farms, zoo, and private ownerships, over a three year period (2007-2009). The most common cause of death was found to be trauma (20.27%), followed by parasitic diseases (15.67%), bacterial diseases (11.98%), stillbirths (9.21%), snakebites (2.76%), gut affections (2.30%), neoplasia (1.38%) and starvation (0.92%). The exact cause of death could not be determined in 77 of 217 animals. Pneumonia (8.29%) and tuberculosis (3.69%) were the most common bacterial diseases. Analyses for parasitic infestation revealed tapeworms to be highest (11.05%), followed by roundworms (8.29%) and hemoparasitism (5.07%) (babesiosis and theileriosis). The mortality rate in young ungulates was lower as compared to adults (32.26% and 67.74%). Gender wise data presented higher mortality in females (55.30%) compared to males (44.70%). In conclusion, highest mortality factor in captive ungulates was trauma, followed by parasitic and bacterial infestations/infections of tapeworms and pneumonia, respectively. Furthermore, necropsies provided substantial information on etiology of death and other related epidemiological aspects.

Keywords: age, epidemiology, gender, mortality, ungulates

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Authors: Syed Ahzam Tariq, Atharva Modi

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This paper presents a probabilistic analysis of the safety of water tube boilers using fault tree analysis (FTA). A fault tree has been constructed by considering all possible areas where a malfunction could lead to a boiler accident. Boiler accidents are relatively rare, causing a scarcity of data. The fuzzy approach is employed to perform a quantitative analysis, wherein theories of fuzzy logic are employed in conjunction with expert elicitation to calculate failure probabilities. The Fuzzy Fault Tree Analysis (FFTA) provides a scientific and contingent method to forecast and prevent accidents.

Keywords: fault tree analysis water tube boiler, fuzzy probability score, failure probability

Procedia PDF Downloads 97

Authors: Ella Tyuryumina, Alexey Neznanov

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This study is an attempt to obtain reliable data on the natural history of breast cancer growth. We analyze the opportunities for using classical mathematical models (exponential and logistic tumor growth models, Gompertz and von Bertalanffy tumor growth models) to try to describe growth of the primary tumor and the secondary distant metastases of human breast cancer. The research aim is to improve predicting accuracy of breast cancer progression using an original mathematical model referred to CoMPaS and corresponding software. We are interested in: 1) modelling the whole natural history of the primary tumor and the secondary distant metastases; 2) developing adequate and precise CoMPaS which reflects relations between the primary tumor and the secondary distant metastases; 3) analyzing the CoMPaS scope of application; 4) implementing the model as a software tool. The foundation of the CoMPaS is the exponential tumor growth model, which is described by determinate nonlinear and linear equations. The CoMPaS corresponds to TNM classification. It allows to calculate different growth periods of the primary tumor and the secondary distant metastases: 1) ‘non-visible period’ for the primary tumor; 2) ‘non-visible period’ for the secondary distant metastases; 3) ‘visible period’ for the secondary distant metastases. The CoMPaS is validated on clinical data of 10-years and 15-years survival depending on the tumor stage and diameter of the primary tumor. The new predictive tool: 1) is a solid foundation to develop future studies of breast cancer growth models; 2) does not require any expensive diagnostic tests; 3) is the first predictor which makes forecast using only current patient data, the others are based on the additional statistical data. The CoMPaS model and predictive software: a) fit to clinical trials data; b) detect different growth periods of the primary tumor and the secondary distant metastases; c) make forecast of the period of the secondary distant metastases appearance; d) have higher average prediction accuracy than the other tools; e) can improve forecasts on survival of breast cancer and facilitate optimization of diagnostic tests. The following are calculated by CoMPaS: the number of doublings for ‘non-visible’ and ‘visible’ growth period of the secondary distant metastases; tumor volume doubling time (days) for ‘non-visible’ and ‘visible’ growth period of the secondary distant metastases. The CoMPaS enables, for the first time, to predict ‘whole natural history’ of the primary tumor and the secondary distant metastases growth on each stage (pT1, pT2, pT3, pT4) relying only on the primary tumor sizes. Summarizing: a) CoMPaS describes correctly the primary tumor growth of IA, IIA, IIB, IIIB (T1-4N0M0) stages without metastases in lymph nodes (N0); b) facilitates the understanding of the appearance period and inception of the secondary distant metastases.

Keywords: breast cancer, exponential growth model, mathematical model, metastases in lymph nodes, primary tumor, survival

Procedia PDF Downloads 326

Authors: Y. Atibi, A. Boutaleb Joutei, T. Slimani

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Insecticidal properties of Alliaceae are widely known, they are plant with varied biological properties. Garlic and onion are known for their positive effect on health, including the prevention of cardiovascular disease and some digestive cancers. These health benefits molecules are also responsible for pest potential control of Alliaceae. With these properties, we can consider using Alliaceae as acaricides. The purpose of this study was to compare the effect of chemical and biopesticides on citrus mites, especially Tetranychus urticae, Panonychus citri and Eutetranychus orientalis. Chemical treatment (Dicofol) and biopesticides (Garlic juice + Alcohol) applied on this study to control the various stages of mites, have reduced the proliferation of mobile forms and reducing the number of eggs to acceptable levels. Garlic juice + alcohol revealed efficiency from 50 to 57.69 % against the mobile forms of T. urticae, however, it was effective against the mobile forms of P. citri and E. orientalis with an efficiency of 85.71 % and 100 % respectively, its action has also reduced the number of eggs of T. urticae and E. orientalis at low levels. Therefore, this biopesticide is conceivable viewpoint technical and economic as the infestation by mite is low.

Keywords: Garlic juice, acaricide, biopesticide, mites, alcohol, Tetranychus urticae, Panonychus citri, Eutetranychus orientalis.

Procedia PDF Downloads 501

Authors: Li-hsing Shih, Wei-Jen Hsu

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Recently, the sale of electrical vehicles (EVs) has increased dramatically due to maturing technology development and decreasing cost. Governments of many countries have made regulations and policies in favor of EVs due to their long-term commitment to net zero carbon emissions. However, due to uncertain factors such as the future price of EVs, forecasting the future market share of EVs is a challenging subject for both the auto industry and local government. This study tries to forecast the market share of EVs using conjoint models and Monte Carlo simulation. The research is conducted in three phases. (1) A conjoint model is established to represent the customer preference structure on purchasing vehicles while five product attributes of both EV and internal combustion engine vehicles (ICEV) are selected. A questionnaire survey is conducted to collect responses from Taiwanese consumers and estimate the part-worth utility functions of all respondents. The resulting part-worth utility functions can be used to estimate the market share, assuming each respondent will purchase the product with the highest total utility. For example, attribute values of an ICEV and a competing EV are given respectively, two total utilities of the two vehicles of a respondent are calculated and then knowing his/her choice. Once the choices of all respondents are known, an estimate of market share can be obtained. (2) Among the attributes, future price is the key attribute that dominates consumers’ choice. This study adopts the assumption of a learning curve to predict the future price of EVs. Based on the learning curve method and past price data of EVs, a regression model is established and the probability distribution function of the price of EVs in 2030 is obtained. (3) Since the future price is a random variable from the results of phase 2, a Monte Carlo simulation is then conducted to simulate the choices of all respondents by using their part-worth utility functions. For instance, using one thousand generated future prices of an EV together with other forecasted attribute values of the EV and an ICEV, one thousand market shares can be obtained with a Monte Carlo simulation. The resulting probability distribution of the market share of EVs provides more information than a fixed number forecast, reflecting the uncertain nature of the future development of EVs. The research results can help the auto industry and local government make more appropriate decisions and future action plans.

Keywords: conjoint model, electrical vehicle, learning curve, Monte Carlo simulation

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Authors: Hasik Lee, Seung-Young Kho

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Nowadays, automatic fare collection (AFC) system is widely used in many countries. However, smart card data from many of cities does not contain alighting information which is necessary to build OD matrices. Therefore, in order to utilize smart card data, destinations of passengers should be estimated. In this paper, kernel density estimation was used to forecast probabilities of alighting stations of bus passengers and applied to smart card data in Seoul, Korea which contains boarding and alighting information. This method was also validated with actual data. In some cases, stochastic method was more accurate than deterministic method. Therefore, it is sufficiently accurate to be used to build OD matrices.

Keywords: destination estimation, Kernel density estimation, smart card data, validation

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Authors: Yashasvi Thakur, Vikas K. Sharma

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The fragile Himalayan ecosystems are sensitive to environmental stresses, including direct and indirect impacts of climate stresses. A total of 297 naturalized alien plant species belonging to 65 families in the IHR have already been reported. Of the total 297 naturalized alien plant species in IHR, the maximum species occur in Himachal Pradesh (232; 78.1%), followed by Jammu & Kashmir (192; 64.6%) and Uttarakhand (181; 60.90%). The present study reports the spread of some invasive and existing weed species like Ageratum conyzoides, Bidens pilosa, Chromolaena odorata, Lantana camara, Brossnetia papyrifera, Oxalis corniculata, Galinsoga parviflora, Panicum maximum at an extent that they are not only invading the agricultural fields but are also replacing the native plant species and degrading the existing grassland quality. Moreover, the degradation of grassland has led to the dry fodder shortage for livestock in the lower Shivalik ranges of the state of Himachal Pradesh and has also encouraged the use of herbicides at an extensive scale. This article provides a mapping of the current spread of some of these species at the block level to allow the development of appropriate management strategies and policy planning for addressing issues pertaining to plant invasion, agricultural fields, and grasslands across the IHR states.

Keywords: climate change, invasive alien species, agriculture, grassland, IHR

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Authors: Aswathi Thrivikraman, S. Advaith

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The study is aimed at designing encoders for deciphering intricacies in time series data by redescribing the dynamics operating on a lower-dimensional manifold. A semi-supervised LSTM autoencoder is devised and investigated to see if the latent representation of the time series data can better forecast the data. End-to-end training of the LSTM autoencoder, together with another LSTM network that is connected to the latent space, forces the hidden states of the encoder to represent the most meaningful latent variables relevant for forecasting. Furthermore, the study compares the predictions with those of a traditional seq2seq model.

Keywords: LSTM, autoencoder, forecasting, seq2seq model

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Authors: Maria C. Mariani, Md Al Masum Bhuiyan, Osei K. Tweneboah, Hector G. Huizar

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This work is devoted to the study of modeling geophysical time series. A stochastic technique with time-varying parameters is used to forecast the volatility of data arising in geophysics. In this study, the volatility is defined as a logarithmic first-order autoregressive process. We observe that the inclusion of log-volatility into the time-varying parameter estimation significantly improves forecasting which is facilitated via maximum likelihood estimation. This allows us to conclude that the estimation algorithm for the corresponding one-step-ahead suggested volatility (with ±2 standard prediction errors) is very feasible since it possesses good convergence properties.

Keywords: Augmented Dickey Fuller Test, geophysical time series, maximum likelihood estimation, stochastic volatility model

Procedia PDF Downloads 287

Authors: Claus N. Holm, Oliver F. Grooss, Robert A. Alphinas

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This research strives to predict the remaining product life cycle of a song on radio after it has been played for one or two months. The best results were achieved using a k-d tree to calculate the most similar songs to the test songs and use a Random Forest model to forecast radio plays. An 82.78% and 83.44% accuracy is achieved for the two time periods, respectively. This explorative research leads to over 4500 test metrics to find the best combination of models and pre-processing techniques. Other algorithms tested are KNN, MLP and CNN. The features only consist of daily radio plays and use no musical features.

Keywords: hit song science, product life cycle, machine learning, radio

Procedia PDF Downloads 134

Authors: Thohidul Islam, Md. Hamidul Haque, Robin Kumar Biswas

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Floods are one of the deadliest natural disasters which are very complex to model; however, machine learning is opening the door for more reliable and accurate flood prediction. In this research, a multilayer perceptron neural network (MLP) is developed to model the rainfall-water level relation, in a subtropical monsoon climatic region of the Bangladesh-India border. Our experiments show promising empirical results to forecast the water level for 1 day lead time. Our best performing MLP model achieves 98.7% coefficient of determination with lower model complexity which surpasses previously reported results on similar forecasting problems.

Keywords: flood forecasting, machine learning, multilayer perceptron network, regression

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Authors: Gizelle K. Vianna, Gabriel V. Cunha, Gustavo S. Oliveira

Abstract:

Foliage diseases in plants can cause a reduction in both quality and quantity of agricultural production. Intelligent detection of plant diseases is an essential research topic as it may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. This work investigates ways to recognize the late blight disease from the analysis of tomato digital images, collected directly from the field. A pair of multilayer perceptron neural network analyzes the digital images, using data from both RGB and HSL color models, and classifies each image pixel. One neural network is responsible for the identification of healthy regions of the tomato leaf, while the other identifies the injured regions. The outputs of both networks are combined to generate the final classification of each pixel from the image and the pixel classes are used to repaint the original tomato images by using a color representation that highlights the injuries on the plant. The new images will have only green, red or black pixels, if they came from healthy or injured portions of the leaf, or from the background of the image, respectively. The system presented an accuracy of 97% in detection and estimation of the level of damage on the tomato leaves caused by late blight.

Keywords: artificial neural networks, digital image processing, pattern recognition, phytosanitary

Procedia PDF Downloads 309

Authors: Szymon Kuczynski

Abstract:

Poland is characterized by the presence of numerous sedimentary basins and hydrocarbon provinces. Since 2006 exploration for hydrocarbons in Poland become gradually more focus on new unconventional targets, particularly on the shale gas potential of the Upper Ordovician and Lower Silurian in the Baltic-Podlasie-Lublin Basin. The first forecast prepared by US Energy Information Administration in 2011 indicated to 5.3 Tcm of natural gas. In 2012, Polish Geological Institute presented its own forecast which estimated maximum reserves on 1.92 Tcm. The difference in the estimates was caused by problems with calculations of the initial amount of adsorbed, as well as free, gas trapped in shale rocks (GIIP - Gas Initially in Place). This value is dependent from sorption capacity, gas saturation and mutual interactions between gas, water, and rock. Determination of the reservoir type in the initial exploration phase brings essential knowledge, which has an impact on decisions related to the production. The study of porosity impact for phase envelope shift eliminates errors and improves production profitability. Confinement phenomenon affects flow characteristics, fluid properties, and phase equilibrium. The thermodynamic behavior of confined fluids in porous media is subject to the basic considerations for industrial applications such as hydrocarbons production. In particular the knowledge of the phase equilibrium and the critical properties of the contained fluid is essential for the design and optimization of such process. In pores with a small diameter (nanopores), the effect of the wall interaction with the fluid particles becomes significant and occurs in shale formations. Nano pore size is similar to the fluid particles’ diameter and the area of particles which flow without interaction with pore wall is almost equal to the area where this phenomenon occurs. The molecular simulation studies have shown an effect of confinement to the pseudo critical properties. Therefore, the critical parameters pressure and temperature and the flow characteristics of hydrocarbons in terms of nano-scale are under the strong influence of fluid particles with the pore wall. It can be concluded that the impact of a single pore size is crucial when it comes to the nanoscale because there is possible the above-described effect. Nano- porosity makes it difficult to predict the flow of reservoir fluid. Research are conducted to explain the mechanisms of fluid flow in the nanopores and gas extraction from porous media by desorption.

Keywords: adsorption, capillary condensation, phase envelope, nanopores, unconventional natural gas

Procedia PDF Downloads 320

Authors: Saahith M. S., Sivakami R.

Abstract:

In the realm of football analytics, particularly focusing on predicting football player performance, the ability to forecast player success accurately is of paramount importance for teams, managers, and fans. This study introduces an elaborate examination of predicting football player performance through the integration of data visualization methods and machine learning algorithms. The research entails the compilation of an extensive dataset comprising player attributes, conducting data preprocessing, feature selection, model selection, and model training to construct predictive models. The analysis within this study will involve delving into feature significance using methodologies like Select Best and Recursive Feature Elimination (RFE) to pinpoint pertinent attributes for predicting player performance. Various machine learning algorithms, including Random Forest, Decision Tree, Linear Regression, Support Vector Regression (SVR), and Artificial Neural Networks (ANN), will be explored to develop predictive models. The evaluation of each model's performance utilizing metrics such as Mean Squared Error (MSE) and R-squared will be executed to gauge their efficacy in predicting player performance. Furthermore, this investigation will encompass a top player analysis to recognize the top-performing players based on the anticipated overall performance scores. Nationality analysis will entail scrutinizing the player distribution based on nationality and investigating potential correlations between nationality and player performance. Positional analysis will concentrate on examining the player distribution across various positions and assessing the average performance of players in each position. Age analysis will evaluate the influence of age on player performance and identify any discernible trends or patterns associated with player age groups. The primary objective is to predict a football player's overall performance accurately based on their individual attributes, leveraging data-driven insights to enrich the comprehension of player success on the field. By amalgamating data visualization and machine learning methodologies, the aim is to furnish valuable tools for teams, managers, and fans to effectively analyze and forecast player performance. This research contributes to the progression of sports analytics by showcasing the potential of machine learning in predicting football player performance and offering actionable insights for diverse stakeholders in the football industry.

Keywords: football analytics, player performance prediction, data visualization, machine learning algorithms, random forest, decision tree, linear regression, support vector regression, artificial neural networks, model evaluation, top player analysis, nationality analysis, positional analysis

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Authors: S. K. Ashiquer Rahman

Abstract:

Recently, oil has become more influential in almost every economic sector as a key material. As can be seen from the news, when there are some changes in an oil price or OPEC announces a new strategy, its effect spreads to every part of the economy directly and indirectly. That’s a reason why people always observe the oil price and try to forecast the changes of it. The most important factor affecting the price is its supply which is determined by the number of wildcats drilled. Therefore, a study about the number of wellheads and other economic variables may give us some understanding of the mechanism indicated by the amount of oil supplies. In this paper, we will consider a relationship between the number of wellheads and three key factors: the price of the wellhead, domestic output, and GNP constant dollars. We also add trend variables in the models because the consumption of oil varies from time to time. Moreover, this paper will use an econometrics method to estimate parameters in the model, apply some tests to verify the result we acquire, and then conclude the model.

Keywords: price, domestic output, GNP, trend variable, wildcat activity

Procedia PDF Downloads 34