Soil provides crucial nutrients and water for the growth of canola, which is one of the most essential economic crops for prairie province in Canada. Therefore, effective and efficient methods are required to modify soil properties to improve crop development. This study systematically analyzed the combined effects of tillage operation and crop residue management on soil features. Thus, the relationship between soil properties and crop yield was also evaluated. More specifically, Aftermarket chopper treatment could cause rela tively higher soil moisture and temperature, while the Original Equipment Manufacturer (OEM) treatment could also result in dramatically higher soil organic matter (SOM) loss than Aftermarket treatment. The significantly more soil water and slightly higher soil temperature created by Aftermarket treatment was beneficial for crop yield. Although OEM treatment could cause more SOM loss, the final crop yield through this method was still lower than that using Aftermarket treatment, implying that the influence of SOM loss on crop growth remained contestable. Meanwhile, Fourier-transform infrared (FTIR) spectra showed the peaks of amides and carboxylic acids was declined during the growth of canola, which indicated that these organic contents played an essential role in the crop development. Finally, the Aftermarket * Harrow treatment was more suitable for canola cultivation, with largest amount of crop harvest and short loss of soil organic contents in the meantime.
{"title":"Identification of Soil Properties and Their Effects on Crop Production under the Influence of Tillage and Residue Treatment in Western Canada","authors":"Y. Wu, X. Xin, J. Huang, K. Zhao","doi":"10.3808/jeil.202200091","DOIUrl":"https://doi.org/10.3808/jeil.202200091","url":null,"abstract":"Soil provides crucial nutrients and water for the growth of canola, which is one of the most essential economic crops for prairie province in Canada. Therefore, effective and efficient methods are required to modify soil properties to improve crop development. This study systematically analyzed the combined effects of tillage operation and crop residue management on soil features. Thus, the relationship between soil properties and crop yield was also evaluated. More specifically, Aftermarket chopper treatment could cause rela tively higher soil moisture and temperature, while the Original Equipment Manufacturer (OEM) treatment could also result in dramatically higher soil organic matter (SOM) loss than Aftermarket treatment. The significantly more soil water and slightly higher soil temperature created by Aftermarket treatment was beneficial for crop yield. Although OEM treatment could cause more SOM loss, the final crop yield through this method was still lower than that using Aftermarket treatment, implying that the influence of SOM loss on crop growth remained contestable. Meanwhile, Fourier-transform infrared (FTIR) spectra showed the peaks of amides and carboxylic acids was declined during the growth of canola, which indicated that these organic contents played an essential role in the crop development. Finally, the Aftermarket * Harrow treatment was more suitable for canola cultivation, with largest amount of crop harvest and short loss of soil organic contents in the meantime.","PeriodicalId":143718,"journal":{"name":"Journal of Environmental Informatics Letters","volume":"26 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116556274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Wu, Z. Chen, C. An, Kenneth Lee, B. Wang, M. Boufadel, Z. Asif, Kent Street Ottawa K C E Canada Oceans Canada
. Reliable information on the spreading of oil plume on water caused by massive oil spills is essential for making proper clean-up measures. Satellite remote sensing technology has advantages over other methods in terms of larger coverage and without ex-pensive operating costs to detect oil spills. In this study, an oil plume delineation method based on the Near-Infrared (NIR) satellite data is used to examine oil spill plume area and size for the BP Deepwater Horizon Oil Spill in the offshore water of Gulf of Mexico and for the recent Norilsk oil spill in a Northern inland water region. To get accurate results noise signals such as land from the data are masked out using SNAP based DEM data and Normalized Difference Water Index method, whereas cloud signals are removed using MODIS cloud masking. Cox-Munk model is used to compute the sun glint radiance. Results of DP oil spill case depicts a 4838.84 km 2 thicker oil plume along with the 20635.53 km 2 thinner portion of the oil slicks using MODIS NIR data at a 500-meter resolution. It is subsequently applied to the recent Norilsk Oil Spill using higher resolution Sentinel-2 NIR data to test the method for detecting spill plume in an inland river water system. Reasonable high-resolution results at 10 meter have been obtained for the smaller scale oil spill onto river water compared to larger offshore area, considering that the river site has complex conditions including shallow water and river reddish soil close to oil color. The developed method is suitable for detecting thick oil plume in ocean or deep inland water bodies.
． 大量石油泄漏引起的油羽在水中扩散的可靠信息对于制定适当的清理措施至关重要。卫星遥感技术在探测石油泄漏的覆盖范围更大和不需要昂贵的操作费用方面比其他方法有优势。在本研究中，采用基于近红外(NIR)卫星数据的油羽圈定方法来检查墨西哥湾近海BP深水地平线漏油事件和最近北部内陆水域诺里尔斯克漏油事件的溢油羽面积和大小。为了获得准确的结果，使用基于SNAP的DEM数据和归一化差水指数方法掩盖数据中的土地等噪声信号，而使用MODIS云掩蔽来去除云信号。Cox-Munk模型用于计算太阳闪烁度。使用500米分辨率的MODIS近红外数据，DP溢油案例的结果显示了4838.84 km 2厚的油羽和20635.53 km 2薄的浮油部分。随后，该技术被应用于最近的诺里尔斯克石油泄漏事故，使用更高分辨率的Sentinel-2近红外数据来测试检测内陆河流水系中泄漏羽流的方法。考虑到河流场地水浅、河流红土接近油色等复杂条件，相对于较大的近海区域，较小规模的河流溢油得到了10米高分辨率的结果。该方法适用于海洋或内陆深水水体中厚油羽的探测。
{"title":"Examining an Oil Spill Plume Mapping Method based on Satellite NIR Data","authors":"C. Wu, Z. Chen, C. An, Kenneth Lee, B. Wang, M. Boufadel, Z. Asif, Kent Street Ottawa K C E Canada Oceans Canada","doi":"10.3808/JEIL.202100050","DOIUrl":"https://doi.org/10.3808/JEIL.202100050","url":null,"abstract":". Reliable information on the spreading of oil plume on water caused by massive oil spills is essential for making proper clean-up measures. Satellite remote sensing technology has advantages over other methods in terms of larger coverage and without ex-pensive operating costs to detect oil spills. In this study, an oil plume delineation method based on the Near-Infrared (NIR) satellite data is used to examine oil spill plume area and size for the BP Deepwater Horizon Oil Spill in the offshore water of Gulf of Mexico and for the recent Norilsk oil spill in a Northern inland water region. To get accurate results noise signals such as land from the data are masked out using SNAP based DEM data and Normalized Difference Water Index method, whereas cloud signals are removed using MODIS cloud masking. Cox-Munk model is used to compute the sun glint radiance. Results of DP oil spill case depicts a 4838.84 km 2 thicker oil plume along with the 20635.53 km 2 thinner portion of the oil slicks using MODIS NIR data at a 500-meter resolution. It is subsequently applied to the recent Norilsk Oil Spill using higher resolution Sentinel-2 NIR data to test the method for detecting spill plume in an inland river water system. Reasonable high-resolution results at 10 meter have been obtained for the smaller scale oil spill onto river water compared to larger offshore area, considering that the river site has complex conditions including shallow water and river reddish soil close to oil color. The developed method is suitable for detecting thick oil plume in ocean or deep inland water bodies.","PeriodicalId":143718,"journal":{"name":"Journal of Environmental Informatics Letters","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130000973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
X. Ye, Z. Zhu, F. Merlin, M. Yang, B. Chen, K. Lee, B. Zhang
Soaring oil demand, as a result of industrial development, boosts oil exploration and production activities at sea, even into deeper and icier waters. The transportation of the oils, as well as the potential spill accidents and associated pollutions are thus increased. There is an urgent call for contingency planning with effective and eco-friendly oil spill cleanup responses. Dispersant applications can facilitate the breaking up of oil slicks into small oil droplets, allowing their rapid dispersion, dissolution, dilution and biodegradation in the water column. Dispersants have been recognized as effective oil treating agents and well adopted. Nearly 7 million liters of chemical dispersants, mostly Corexit 9500A, were used after the Deepwater Horizon oil spill incident. However, debates over dispersants continued with major concerns about their environmental impacts and the ecological toxicity, which need to be well reviewed and tackled. Therefore, this study summarized the recent laband meso-scale studies and field trials on the ecological impact analysis of dispersants and the chemically dispersed oils. By providing an up-to-date review of the ecological toxicity and environmental impact assessment, this study would help to bridge the knowledge gaps in the field and facilitate future dispersant applications.
{"title":"Ecological Impact Analysis of Dispersants and Dispersed Oil: An Overview","authors":"X. Ye, Z. Zhu, F. Merlin, M. Yang, B. Chen, K. Lee, B. Zhang","doi":"10.3808/JEIL.202100058","DOIUrl":"https://doi.org/10.3808/JEIL.202100058","url":null,"abstract":"Soaring oil demand, as a result of industrial development, boosts oil exploration and production activities at sea, even into deeper and icier waters. The transportation of the oils, as well as the potential spill accidents and associated pollutions are thus increased. There is an urgent call for contingency planning with effective and eco-friendly oil spill cleanup responses. Dispersant applications can facilitate the breaking up of oil slicks into small oil droplets, allowing their rapid dispersion, dissolution, dilution and biodegradation in the water column. Dispersants have been recognized as effective oil treating agents and well adopted. Nearly 7 million liters of chemical dispersants, mostly Corexit 9500A, were used after the Deepwater Horizon oil spill incident. However, debates over dispersants continued with major concerns about their environmental impacts and the ecological toxicity, which need to be well reviewed and tackled. Therefore, this study summarized the recent laband meso-scale studies and field trials on the ecological impact analysis of dispersants and the chemically dispersed oils. By providing an up-to-date review of the ecological toxicity and environmental impact assessment, this study would help to bridge the knowledge gaps in the field and facilitate future dispersant applications.","PeriodicalId":143718,"journal":{"name":"Journal of Environmental Informatics Letters","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122800368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study proposed a dual-uncertainty two-stage fractional power system management (DUTSF-PSM) model to deal with uncertainties and dual objectives in the power management system of Ontario. This model integrates interval linear programming (ILP), chance-constrained programming (CCP), mixed-integer linear programming (MILP), and two-stage stochastic programming (TSP) methods into the framework of a linear fractional programming (LFP) model. Two-objective issues and capacity expansion schemes under multiple uncertainties can be addressed by the DUTSF-PSM model. Economic and environmental elements are considered in the objective function of the DUTSF-PSM model at the same time in order to get maximal system benefit with minimum environmental influence. This model can tackle effectively the tradeoff between the economic and environmental objectives. Through the DUTSF-PSM model for power systems in Ontario, the maximal system efficiency based on the least environmental influence under different levels of constraint-violation probabilities can be achieved. The results indicate that both hydroelectric and wind power have development potential when the economic and environmental factors are considered in the objective function at the same time. In addition, the results of factorial analyses reflected that the effect of CO2 emission of each power generation technology on the system revenue is most significant among the chosen three factors.
{"title":"A Dual-Uncertainty Two-Stage Fractional Programming Model for Reginal Power Systems in the Province of Ontario, Canada","authors":"J. Huang, C. Huang, S. Nie","doi":"10.3808/jeil.202200090","DOIUrl":"https://doi.org/10.3808/jeil.202200090","url":null,"abstract":"This study proposed a dual-uncertainty two-stage fractional power system management (DUTSF-PSM) model to deal with uncertainties and dual objectives in the power management system of Ontario. This model integrates interval linear programming (ILP), chance-constrained programming (CCP), mixed-integer linear programming (MILP), and two-stage stochastic programming (TSP) methods into the framework of a linear fractional programming (LFP) model. Two-objective issues and capacity expansion schemes under multiple uncertainties can be addressed by the DUTSF-PSM model. Economic and environmental elements are considered in the objective function of the DUTSF-PSM model at the same time in order to get maximal system benefit with minimum environmental influence. This model can tackle effectively the tradeoff between the economic and environmental objectives. Through the DUTSF-PSM model for power systems in Ontario, the maximal system efficiency based on the least environmental influence under different levels of constraint-violation probabilities can be achieved. The results indicate that both hydroelectric and wind power have development potential when the economic and environmental factors are considered in the objective function at the same time. In addition, the results of factorial analyses reflected that the effect of CO2 emission of each power generation technology on the system revenue is most significant among the chosen three factors.","PeriodicalId":143718,"journal":{"name":"Journal of Environmental Informatics Letters","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123101781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study provides a brief review for uncertainty quantification in hydrological predictions. The major approaches for hydrologic predictions are firstly introduced, including the widely used data-driven and process-based modelling approaches. The major uncertainties resulting from inputs, model structures, parameters and outputs are then briefly illustrated. The major review is then conducted for various uncertainty quantification approaches. In detail, the approaches for quantifying uncertainties in model parameters, structures and states are mainly reviewed, such as the Markov chain Monte Carlo, sequential data assimilation and model average approaches. Potential issues to be addressed in future are then concluded, summarizing some unclear issues which may be further investigated in further studies.
{"title":"Uncertainty Quantification in Hydrologic Predictions: A Brief Review","authors":"Y. Fan","doi":"10.3808/jeil.201900019","DOIUrl":"https://doi.org/10.3808/jeil.201900019","url":null,"abstract":"This study provides a brief review for uncertainty quantification in hydrological predictions. The major approaches for hydrologic predictions are firstly introduced, including the widely used data-driven and process-based modelling approaches. The major uncertainties resulting from inputs, model structures, parameters and outputs are then briefly illustrated. The major review is then conducted for various uncertainty quantification approaches. In detail, the approaches for quantifying uncertainties in model parameters, structures and states are mainly reviewed, such as the Markov chain Monte Carlo, sequential data assimilation and model average approaches. Potential issues to be addressed in future are then concluded, summarizing some unclear issues which may be further investigated in further studies.","PeriodicalId":143718,"journal":{"name":"Journal of Environmental Informatics Letters","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130853027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In practice, environmental informatics involves a com-bination of computers, computation, mathematical modelling, and system science to address real-world environmental pro-blems. This special issue includes a number of applied computational analytics papers that either create new methods or provide innovative applications of existing methods for assis-ting with environmental decision-making applications using informatics. In line with the aims and scope of the special issue, the diversity of applications in the papers highlights a wide spectrum of both practical relevance and methodological contributions to research in environmental decision-making and analysis. The contributions contained in this issue all demon-strate novel approaches of computational analytics as applied to environmental decision-making – be this on the side of modelling, computational solution procedures, visual analytics, and/or technologies. to ,
{"title":"Computational Analytics for Supporting Environmental Decision-Making and Analysis: An Introduction","authors":"J. Yeomans","doi":"10.3808/JEIL.202000040","DOIUrl":"https://doi.org/10.3808/JEIL.202000040","url":null,"abstract":"In practice, environmental informatics involves a com-bination of computers, computation, mathematical modelling, and system science to address real-world environmental pro-blems. This special issue includes a number of applied computational analytics papers that either create new methods or provide innovative applications of existing methods for assis-ting with environmental decision-making applications using informatics. In line with the aims and scope of the special issue, the diversity of applications in the papers highlights a wide spectrum of both practical relevance and methodological contributions to research in environmental decision-making and analysis. The contributions contained in this issue all demon-strate novel approaches of computational analytics as applied to environmental decision-making – be this on the side of modelling, computational solution procedures, visual analytics, and/or technologies. to ,","PeriodicalId":143718,"journal":{"name":"Journal of Environmental Informatics Letters","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131065859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Giri, N. Ojha, S. Subedi, S. Rana, Y. Bhandari, A. Khanal
Ophiocordyceps sinensis (Yarsagumba) is complex combination of fungus and dead caterpillar having high medicinal and economic value. From the ancient time, it is being used as traditional medicine in countries like Nepal and India. It naturally grows in the Himalayan alpine pastures of Nepal, India and Bhutan. However, there are limited literatures which explores the people’s interaction with the medicinal plants. This study focuses on availability and usage of Yarsagumba in Nepal, India and Bhutan. For this, systematic literature review was conducted to gather information from online resources using different keywords. Ophiocordyceps sinensis is largely used for brain and body nourishment to improve the immune system and used as a renoprotective, anti-inflammatory, anti-metastatic, and neuroprotective agent. Despite of major opportunity, India, Nepal, and Bhutan have been only contributing 1.6, 1.4, and 0.5%, respectively of the total annual production of Ophiocordyceps sinensis. This paper has explored details on the ethnobotany and use of medicinal plants in the context of Nepal, India, and Bhutan. Apart from this, the production, benefits, and usage of Ophiocordyceps sinensis have also been discussed in this paper.
{"title":"Ethnobotany of the Medicinal Plants: Case of Ophiocordyceps sinensis (Yarsagumba) and Its Benefits for Nepal, India, and Bhutan","authors":"S. Giri, N. Ojha, S. Subedi, S. Rana, Y. Bhandari, A. Khanal","doi":"10.3808/jeil.202300103","DOIUrl":"https://doi.org/10.3808/jeil.202300103","url":null,"abstract":"Ophiocordyceps sinensis (Yarsagumba) is complex combination of fungus and dead caterpillar having high medicinal and economic value. From the ancient time, it is being used as traditional medicine in countries like Nepal and India. It naturally grows in the Himalayan alpine pastures of Nepal, India and Bhutan. However, there are limited literatures which explores the people’s interaction with the medicinal plants. This study focuses on availability and usage of Yarsagumba in Nepal, India and Bhutan. For this, systematic literature review was conducted to gather information from online resources using different keywords. Ophiocordyceps sinensis is largely used for brain and body nourishment to improve the immune system and used as a renoprotective, anti-inflammatory, anti-metastatic, and neuroprotective agent. Despite of major opportunity, India, Nepal, and Bhutan have been only contributing 1.6, 1.4, and 0.5%, respectively of the total annual production of Ophiocordyceps sinensis. This paper has explored details on the ethnobotany and use of medicinal plants in the context of Nepal, India, and Bhutan. Apart from this, the production, benefits, and usage of Ophiocordyceps sinensis have also been discussed in this paper.","PeriodicalId":143718,"journal":{"name":"Journal of Environmental Informatics Letters","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114757572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The state of Iowa has long been recognized as a significant contributor of nitrogen loads to the Mississippi river basin. The nitrogen loads are mainly in the form of nitrates arising from high yield agriculture and animal agriculture. With excessive water flowing through the water system of Iowa, the surplus nitrogen in the soil gets carried into the Mississippi river basin and ultimately to the Gulf of Mexico, resulting in the generation of a hypoxic zone having a detrimental impact on the environment. Iowa is a leading producer of corn, soybean, animal products, and ethanol; hence, agriculture and animal agriculture are well rooted in its economy. With increasing ethanol demands, high yield agricultural practices, growing animal agriculture, and a connected economy, there is a need to understand the interdependencies of the Iowa food-energy-water (IFEW) nexus. In this work, a model of the IFEW system interdependencies is proposed and used as the basis for a computational system model, which can be used to guide decision-makers for improved policy formation to mitigate adverse impacts of the nitrogen export on the environment and economy. Global sensitivity analysis of the proposed IFEW system model reveals that the commercial nitrogen application rate for corn and corn yield are the critical parameters affecting nitrogen surplus in soil.
{"title":"System Modeling and Sensitivity Analysis of the Iowa Food-Water-Energy Nexus","authors":"Vishal Raul, Leifur Þ. Leifsson, A. Kaleita","doi":"10.3808/JEIL.202000044","DOIUrl":"https://doi.org/10.3808/JEIL.202000044","url":null,"abstract":"The state of Iowa has long been recognized as a significant contributor of nitrogen loads to the Mississippi river basin. The nitrogen loads are mainly in the form of nitrates arising from high yield agriculture and animal agriculture. With excessive water flowing through the water system of Iowa, the surplus nitrogen in the soil gets carried into the Mississippi river basin and ultimately to the Gulf of Mexico, resulting in the generation of a hypoxic zone having a detrimental impact on the environment. Iowa is a leading producer of corn, soybean, animal products, and ethanol; hence, agriculture and animal agriculture are well rooted in its economy. With increasing ethanol demands, high yield agricultural practices, growing animal agriculture, and a connected economy, there is a need to understand the interdependencies of the Iowa food-energy-water (IFEW) nexus. In this work, a model of the IFEW system interdependencies is proposed and used as the basis for a computational system model, which can be used to guide decision-makers for improved policy formation to mitigate adverse impacts of the nitrogen export on the environment and economy. Global sensitivity analysis of the proposed IFEW system model reveals that the commercial nitrogen application rate for corn and corn yield are the critical parameters affecting nitrogen surplus in soil.","PeriodicalId":143718,"journal":{"name":"Journal of Environmental Informatics Letters","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130160019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The current research discusses the applications of Bayesian linear regression to predict the uncertainty of remote sensing data. To predict the uncertainty, the study considered the SENTINEL-2 satellite data of agricultural fields of Uttar Pradesh state of India. Using the stratified sampling method in Google Earth Engine, the random points generated are mapped to agricultural fields. Data was collected in the form of maximum Normalized Difference Vegetation Index (NDVI) values of each agricultural field. The dynamics of the time series predictions were explored with Bayesian linear regression, a probabilistic deep learning method. The model uncertainty defined as epistemic uncertainty is evaluated with the prior and posterior probability parameters of Bayesian statistics in linear regression. The number of regression lines predicted for the same data shows evidence of uncertainty. The Bayesian linear regression models show evidence of high uncertainty for the predicted NDVI values. The variation in model uncertainty is measured by dividing the dataset into samples and it is observed that with increase in data the uncertainty is reduced. Also, with the increase in data, the posterior density becomes sharper which corresponds to a decrease in variance. Further, the study extended the concept of regression analysis with Gaussian basis functions to determine the effect of model uncertainty with an increase in data. The analysis has shown the same result in knowing the effect of uncertainty with the increase in data. Further, a nonlinear polynomial regression model with a Gaussian distribution as a basis function was developed to evaluate the marginal probabilities of the evidence function in capturing the uncertainty with varying degrees of freedom. The polynomial regression with a Gaussian distribution using Bayesian statistics has captured the uncertainty and confirmed that the uncertainty is captured at lower degrees of freedom.
{"title":"Modeling Uncertainty Quantification of NDVI of Agricultural Fields through Bayesian Linear Regression in Time Series Prediction","authors":"M. Srinivas, P. Prasad","doi":"10.3808/jeil.202300098","DOIUrl":"https://doi.org/10.3808/jeil.202300098","url":null,"abstract":"The current research discusses the applications of Bayesian linear regression to predict the uncertainty of remote sensing data. To predict the uncertainty, the study considered the SENTINEL-2 satellite data of agricultural fields of Uttar Pradesh state of India. Using the stratified sampling method in Google Earth Engine, the random points generated are mapped to agricultural fields. Data was collected in the form of maximum Normalized Difference Vegetation Index (NDVI) values of each agricultural field. The dynamics of the time series predictions were explored with Bayesian linear regression, a probabilistic deep learning method. The model uncertainty defined as epistemic uncertainty is evaluated with the prior and posterior probability parameters of Bayesian statistics in linear regression. The number of regression lines predicted for the same data shows evidence of uncertainty. The Bayesian linear regression models show evidence of high uncertainty for the predicted NDVI values. The variation in model uncertainty is measured by dividing the dataset into samples and it is observed that with increase in data the uncertainty is reduced. Also, with the increase in data, the posterior density becomes sharper which corresponds to a decrease in variance. Further, the study extended the concept of regression analysis with Gaussian basis functions to determine the effect of model uncertainty with an increase in data. The analysis has shown the same result in knowing the effect of uncertainty with the increase in data. Further, a nonlinear polynomial regression model with a Gaussian distribution as a basis function was developed to evaluate the marginal probabilities of the evidence function in capturing the uncertainty with varying degrees of freedom. The polynomial regression with a Gaussian distribution using Bayesian statistics has captured the uncertainty and confirmed that the uncertainty is captured at lower degrees of freedom.","PeriodicalId":143718,"journal":{"name":"Journal of Environmental Informatics Letters","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125851132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, an inexact mixed-integer interval stochastic fractional model (IMSFP) is developed for Shandong’s sustainable power system management under uncertainties. Shandong has a high proportion of fossil-fuel power, which has resulted in significant greenhouse gas emissions. Future is an essential period for energy structure transition. Developed IMSFP can effectively tackle dual objective, system efficiency represented as output/input ratios, as well as uncertainties described as interval values and probability distributions in the constraints and objectives. The results indicate that the clean power transition and capacity expansion scheme are sensitive to different constraint-violation risk levels. Obtained interval solutions can provide flexible strategies for resource allocation and expansion capacities under multiple complexities. An economic single objective model (IMCLP) is also developed, which aims at minimizing the system cost. The comparative results illustrate that the IMSFP model can better characterize the real-world power system problems through optimizing a ratio between clean energy utilization and system cost. Biomass and wind power would be major developed electricity forms in the future, and solar energy has great development potential. In short, the proposed IMSFP model is advantageous in balancing conflicting dual objectives and reflecting complicated interactions among system efficiency, economic cost, system reliability, and constraint-violation scenarios.
{"title":"Development of a Chance-Constrained Dual-Objective Fractional Programming for Shandong’s Clean Power Transition","authors":"M. N. Li, G. Huang, X. Y. Zhang, J. Chen","doi":"10.3808/jeil.202200088","DOIUrl":"https://doi.org/10.3808/jeil.202200088","url":null,"abstract":"In this study, an inexact mixed-integer interval stochastic fractional model (IMSFP) is developed for Shandong’s sustainable power system management under uncertainties. Shandong has a high proportion of fossil-fuel power, which has resulted in significant greenhouse gas emissions. Future is an essential period for energy structure transition. Developed IMSFP can effectively tackle dual objective, system efficiency represented as output/input ratios, as well as uncertainties described as interval values and probability distributions in the constraints and objectives. The results indicate that the clean power transition and capacity expansion scheme are sensitive to different constraint-violation risk levels. Obtained interval solutions can provide flexible strategies for resource allocation and expansion capacities under multiple complexities. An economic single objective model (IMCLP) is also developed, which aims at minimizing the system cost. The comparative results illustrate that the IMSFP model can better characterize the real-world power system problems through optimizing a ratio between clean energy utilization and system cost. Biomass and wind power would be major developed electricity forms in the future, and solar energy has great development potential. In short, the proposed IMSFP model is advantageous in balancing conflicting dual objectives and reflecting complicated interactions among system efficiency, economic cost, system reliability, and constraint-violation scenarios.","PeriodicalId":143718,"journal":{"name":"Journal of Environmental Informatics Letters","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134291992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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