{"title":"A Two-Stage Stochastic Fuzzy Mixed-Integer Linear Programming Approach for Water Resource Allocation under Uncertainty in Ajabshir Qaleh Chay Dam","authors":"J. Nematian","doi":"10.3808/jei.202300487","DOIUrl":"https://doi.org/10.3808/jei.202300487","url":null,"abstract":"","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76618628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Liu, M. Liu, G. Sun, Z. Q. Zhou, D. L. Wang, F. He, J. Li, J. Xie, R. Gettler, E. Brunson, J. Steevens, D. Xu
Measuring oil concentration in the aquatic environment is essential for determining the potential exposure, risk, or injury for oil spill response and natural resource damage assessment. Conventional analytical chemistry methods require samples to be collected in the field, shipped, and processed in the laboratory, which is also rather time-consuming, laborious, and costly. For rapid field response immediately after a spill, there is a need to estimate oil concentration in near real time. To make the oil analysis more portable, fast, and cost effective, we developed a plug-and-play device and a deep learning model to assess oil levels in water using fluorescent images of water samples. We constructed a 3D-printed device to collect fluorescent images of solvent-extracted water samples using an iPhone. We prepared approximately 1,300 samples of oil at different concentrations to train and test the deep learning model. The model comprises a convolutional neural network and a novel module of histogram bottleneck block with an attention mechanism to exploit the spectral features found in low-contrast images. This model predicts the oil concentration in weight per volume based on fluorescence image. We devised a confidence interval estimator by combining gradient boosting and polymodal regressor to provide a confidence assessment of our results. Our model achieved sufficient accuracy to predict oil levels for most environmental applications. We plan to improve the device and iPhone application as a near-real-time tool for oil spill responders to measure oil in water.
{"title":"Assessing Environmental Oil Spill Based on Fluorescence Images of Water Samples and Deep Learning","authors":"D. Liu, M. Liu, G. Sun, Z. Q. Zhou, D. L. Wang, F. He, J. Li, J. Xie, R. Gettler, E. Brunson, J. Steevens, D. Xu","doi":"10.3808/jei.202300491","DOIUrl":"https://doi.org/10.3808/jei.202300491","url":null,"abstract":"Measuring oil concentration in the aquatic environment is essential for determining the potential exposure, risk, or injury for oil spill response and natural resource damage assessment. Conventional analytical chemistry methods require samples to be collected in the field, shipped, and processed in the laboratory, which is also rather time-consuming, laborious, and costly. For rapid field response immediately after a spill, there is a need to estimate oil concentration in near real time. To make the oil analysis more portable, fast, and cost effective, we developed a plug-and-play device and a deep learning model to assess oil levels in water using fluorescent images of water samples. We constructed a 3D-printed device to collect fluorescent images of solvent-extracted water samples using an iPhone. We prepared approximately 1,300 samples of oil at different concentrations to train and test the deep learning model. The model comprises a convolutional neural network and a novel module of histogram bottleneck block with an attention mechanism to exploit the spectral features found in low-contrast images. This model predicts the oil concentration in weight per volume based on fluorescence image. We devised a confidence interval estimator by combining gradient boosting and polymodal regressor to provide a confidence assessment of our results. Our model achieved sufficient accuracy to predict oil levels for most environmental applications. We plan to improve the device and iPhone application as a near-real-time tool for oil spill responders to measure oil in water.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75588024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. Solovey, R. Janica, V. Harasymchuk, M. Przychodzka, L. Yanush
On the Polish–Ukrainian borderlands, there is the Lublin–Lviv transboundary groundwater aquifer system, which is of key importance in shaping strategic groundwater resources. Due to the particular importance of this aquifer system, the two neighboring countries are obliged to undertake joint actions to protect it. The integrated management of the Lublin–Lviv aquifer system seems difficult due to the significant spatial and temporal scale of groundwater flows in the region. To support internationally integrated management, a transboundary geological model was developed. Based on this model, a hydrogeological conceptual model has been developed, which allowed for a numerical model of groundwater flow to be calculated. The model research helped diagnose potential problems by determining the scope of the area with cross-border flows and quantifying the flows between Poland and Ukraine. In addition, the numerical model was used to define the optimal cross-border management unit and the conditions needed to sustainably exploit the Lublin–Lviv aquifer system. Basing on the research results it was concluded that groundwater flows in transboundary aquifers very on a regional scale and that the range of areas of importance for transboundary groundwater flows is much smaller than the pre-selected partial catchments of the Bug and San Rivers. The results of this study may significantly contribute to the preparation of joint water management plans.
{"title":"Numerical Modeling of Transboundary Groundwater Flow in the Bug and San Catchment Areas for Integrated Water Resource Management (Poland–Ukraine)","authors":"T. Solovey, R. Janica, V. Harasymchuk, M. Przychodzka, L. Yanush","doi":"10.3808/jei.202300501","DOIUrl":"https://doi.org/10.3808/jei.202300501","url":null,"abstract":"On the Polish–Ukrainian borderlands, there is the Lublin–Lviv transboundary groundwater aquifer system, which is of key importance in shaping strategic groundwater resources. Due to the particular importance of this aquifer system, the two neighboring countries are obliged to undertake joint actions to protect it. The integrated management of the Lublin–Lviv aquifer system seems difficult due to the significant spatial and temporal scale of groundwater flows in the region. To support internationally integrated management, a transboundary geological model was developed. Based on this model, a hydrogeological conceptual model has been developed, which allowed for a numerical model of groundwater flow to be calculated. The model research helped diagnose potential problems by determining the scope of the area with cross-border flows and quantifying the flows between Poland and Ukraine. In addition, the numerical model was used to define the optimal cross-border management unit and the conditions needed to sustainably exploit the Lublin–Lviv aquifer system. Basing on the research results it was concluded that groundwater flows in transboundary aquifers very on a regional scale and that the range of areas of importance for transboundary groundwater flows is much smaller than the pre-selected partial catchments of the Bug and San Rivers. The results of this study may significantly contribute to the preparation of joint water management plans.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135497512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Satour, B. Benyacoub, N. El Moçayd, Z. Ennaimani, S. Niazi, N. Kassou, I. Kacimi
{"title":"Machine Learning Enhances Flood Resilience Measurement in a Coastal Area – Case Study of Morocco","authors":"N. Satour, B. Benyacoub, N. El Moçayd, Z. Ennaimani, S. Niazi, N. Kassou, I. Kacimi","doi":"10.3808/jei.202300497","DOIUrl":"https://doi.org/10.3808/jei.202300497","url":null,"abstract":"","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85342176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
X. Li, J. Zhang, W. Yu, L. Liu, W. Wang, Z. Cui, W. Wang, R. Wang, Y. Li
The water quality of a river can be considered a function of its surrounding landscape. Understanding the relationship between landscape patterns and river water quality is essential for optimizing landscape patterns to reduce watershed pollution and has not yet been solved. A multiscale geographically weighted regression (MGWR) model was used to explore the associations between the landscape patterns and water quality. Our results showed that landscape metrics have varied relationships with the water quality across spatial scales in different seasons. The strongest independent influencing variable for NO3–-N, NH4+-N, and TN was tea gardens, residential land, and varied seasonally, respectively. The impacts of the landscape metrics on the TP were relatively weak throughout the year at the watershed scale. The influence of landscape metrics on NO3–-N was more significant during the flood season, whereas that on NH4+-N was more notable during the non-flood season. Seasonal changes in the influencing landscape metrics of TN were not regular. Although landscape composition more significantly influenced water quality than configuration, the Shannon’s diversity index and patch density were important configuration indices that significantly impacted water quality. Therefore, with limited land availability, it is essential to optimize the landscape spatial configuration without changing the composition of the watershed to reduce the risk of river pollution. This study further indicated that the MGWR model can well quantify the effects of landscape pattern on water quality at the watershed scale.
{"title":"How Landscape Patterns Affect River Water Quality Spatially and Temporally: A Multiscale Geographically Weighted Regression Approach","authors":"X. Li, J. Zhang, W. Yu, L. Liu, W. Wang, Z. Cui, W. Wang, R. Wang, Y. Li","doi":"10.3808/jei.202300503","DOIUrl":"https://doi.org/10.3808/jei.202300503","url":null,"abstract":"The water quality of a river can be considered a function of its surrounding landscape. Understanding the relationship between landscape patterns and river water quality is essential for optimizing landscape patterns to reduce watershed pollution and has not yet been solved. A multiscale geographically weighted regression (MGWR) model was used to explore the associations between the landscape patterns and water quality. Our results showed that landscape metrics have varied relationships with the water quality across spatial scales in different seasons. The strongest independent influencing variable for NO3–-N, NH4+-N, and TN was tea gardens, residential land, and varied seasonally, respectively. The impacts of the landscape metrics on the TP were relatively weak throughout the year at the watershed scale. The influence of landscape metrics on NO3–-N was more significant during the flood season, whereas that on NH4+-N was more notable during the non-flood season. Seasonal changes in the influencing landscape metrics of TN were not regular. Although landscape composition more significantly influenced water quality than configuration, the Shannon’s diversity index and patch density were important configuration indices that significantly impacted water quality. Therefore, with limited land availability, it is essential to optimize the landscape spatial configuration without changing the composition of the watershed to reduce the risk of river pollution. This study further indicated that the MGWR model can well quantify the effects of landscape pattern on water quality at the watershed scale.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135497514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Tian, H. Ren, X. Li, X. D. Zhang, X. Xu, S. Wang
{"title":"Specificality, Quality Variation, Assessment and Treatment of Estuarine Water in the Pearl River Delta, South China","authors":"H. Tian, H. Ren, X. Li, X. D. Zhang, X. Xu, S. Wang","doi":"10.3808/jei.202300496","DOIUrl":"https://doi.org/10.3808/jei.202300496","url":null,"abstract":"","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84033767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Big data generated by remote sensing, ground-based measurements, models and simulations, social media and crowdsourcing, and a wide range of structured and unstructured sources necessitates significant data and knowledge management efforts. Innovations and developments in information technology over the last couple of decades have made data and knowledge management possible for an insurmountable amount of data collected and generated over the last decades. This enabled open knowledge networks to be built that led to new ideas in scientific research and the business world. To design and develop open knowledge networks, ontologies are essential since they form the backbone of conceptualization of a given knowledge domain. A systematic literature review was conducted to examine research involving ontologies related to hydrological processes and water resource management. Ontologies in the hydrology domain support the comprehension, monitoring, and representation of the hydrologic cycle’s complex structure, as well as the predictions of its processes. They contribute to the development of ontology-based information and decision support systems; understanding of environmental and atmospheric phenomena; development of climate and water resiliency concepts; creation of educational tools with artificial intelligence; and strengthening of related cyberinfrastructures. This review provides an explanation of key issues and challenges in ontology development based on hydrologic processes to guide the development of next generation artificial intelligence applications. The study also discusses future research prospects in combination with artificial intelligence and hydroscience.
{"title":"A Comprehensive Review of Ontologies in the Hydrology Towards Guiding Next Generation Artificial Intelligence Applications","authors":"Ö. Baydaroğlu, S. Yeşilköy, Y. Sermet, I. Demir","doi":"10.3808/jei.202300500","DOIUrl":"https://doi.org/10.3808/jei.202300500","url":null,"abstract":"Big data generated by remote sensing, ground-based measurements, models and simulations, social media and crowdsourcing, and a wide range of structured and unstructured sources necessitates significant data and knowledge management efforts. Innovations and developments in information technology over the last couple of decades have made data and knowledge management possible for an insurmountable amount of data collected and generated over the last decades. This enabled open knowledge networks to be built that led to new ideas in scientific research and the business world. To design and develop open knowledge networks, ontologies are essential since they form the backbone of conceptualization of a given knowledge domain. A systematic literature review was conducted to examine research involving ontologies related to hydrological processes and water resource management. Ontologies in the hydrology domain support the comprehension, monitoring, and representation of the hydrologic cycle’s complex structure, as well as the predictions of its processes. They contribute to the development of ontology-based information and decision support systems; understanding of environmental and atmospheric phenomena; development of climate and water resiliency concepts; creation of educational tools with artificial intelligence; and strengthening of related cyberinfrastructures. This review provides an explanation of key issues and challenges in ontology development based on hydrologic processes to guide the development of next generation artificial intelligence applications. The study also discusses future research prospects in combination with artificial intelligence and hydroscience.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135497511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The river–lake ecotone supports diverse aquatic life, but its food web structure and topology are poorly understood. Baiyangdian Lake, northern China’s largest shallow lake, depends on external environmental flows, of which the Fu River provides the most stable water supply. Here, we used stable isotopes and topological analysis to explore the food web structure along a spatial gradient using data from field surveys from 2018 to 2019. Carbon and nitrogen stable isotopes and the food web structure were associated with environmental factors for four ecosystem types (river, river mouth, lake mouth, lake). Detritus, phytoplankton, and zooplankton δ13C values became more depleted along the gradient from the river to the lake, whereas δ13C of submerged macrophytes was enriched in the ecotones compared to the river and lake. Higher δ15N values for basal resources and zooplankton occurred at the lake mouth and river mouth. The top consumers were omnivorous fish: Hemiculter leucisculus (trophic level [TL] = 3.85 ± 0.89) in the river and Pseudorasbora parva (TL = 4.54 ± 0.58) in the river mouth. Carnivorous Erythroculter dabryi occupied the highest TL (3.61 ± 0.36 and 4.46 ± 0.36, respectively) in the lake mouth and lake. These results together led to a change from a detritus-based to phytoplankton-based food web along the gradient from the river to the lake. The species richness, number of trophic links, link density, and mean food chain length all are greatest in the lake, followed by the lake mouth, and the lowest were in the river. Our results provide a holistic view of the ecotone ecosystem and its food web, suggesting that it supports a more diverse species assemblage and more complex food web structure than the adjacent river ecosystem, rather than the adjacent lake ecosystem. Therefore, management should emphasize the combined effects of altered hydrological regimes and poor water quality on the ecotone food webs to manage the river and lake more sustainably.
{"title":"Spatial Heterogeneity of Food Webs in A River-Lake Ecotone under Flow Regulation – A Case Study in Northern China","authors":"W. Yang, X. Fu, X. X. Li, B. Cui, X. Yin","doi":"10.3808/jei.202300490","DOIUrl":"https://doi.org/10.3808/jei.202300490","url":null,"abstract":"The river–lake ecotone supports diverse aquatic life, but its food web structure and topology are poorly understood. Baiyangdian Lake, northern China’s largest shallow lake, depends on external environmental flows, of which the Fu River provides the most stable water supply. Here, we used stable isotopes and topological analysis to explore the food web structure along a spatial gradient using data from field surveys from 2018 to 2019. Carbon and nitrogen stable isotopes and the food web structure were associated with environmental factors for four ecosystem types (river, river mouth, lake mouth, lake). Detritus, phytoplankton, and zooplankton δ13C values became more depleted along the gradient from the river to the lake, whereas δ13C of submerged macrophytes was enriched in the ecotones compared to the river and lake. Higher δ15N values for basal resources and zooplankton occurred at the lake mouth and river mouth. The top consumers were omnivorous fish: Hemiculter leucisculus (trophic level [TL] = 3.85 ± 0.89) in the river and Pseudorasbora parva (TL = 4.54 ± 0.58) in the river mouth. Carnivorous Erythroculter dabryi occupied the highest TL (3.61 ± 0.36 and 4.46 ± 0.36, respectively) in the lake mouth and lake. These results together led to a change from a detritus-based to phytoplankton-based food web along the gradient from the river to the lake. The species richness, number of trophic links, link density, and mean food chain length all are greatest in the lake, followed by the lake mouth, and the lowest were in the river. Our results provide a holistic view of the ecotone ecosystem and its food web, suggesting that it supports a more diverse species assemblage and more complex food web structure than the adjacent river ecosystem, rather than the adjacent lake ecosystem. Therefore, management should emphasize the combined effects of altered hydrological regimes and poor water quality on the ecotone food webs to manage the river and lake more sustainably.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82125954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An automatic identification and classification of rice diseases are very important in the domain of agriculture. Deep learning (DL) is an effective research area in the identification of agriculture pattern identification where it can effectively resolve the issues of diseases identification. In this paper, a hybrid optimization algorithm is developed to categorize the plant diseases. The pre-processing is made using Region of Interest (ROI) extraction and the input image is created by combining the Rice plant dataset, and Rice disease dataset. The segmentation is accomplished using Deep fuzzy clustering. The features, like statistical features, entropy, Convolutional Neural Network (CNN) features, Local Optimal-Oriented Pattern (LOOP), and Local Gabor XOR Pattern (LGXP) is considered for extracting the appropriate features for further processing. The data augmentation is employed to enlarge the volume of extracted features. Then, the first level classification is made by deep neuro-fuzzy network (DNFN), which is trained using Rider Henry Gas Solubility Optimization (RHGSO) that categories into healthy and unhealthy plants. The RHGSO is the integration of Rider Optimization Algorithm (ROA) and Henry gas solubility optimization (HGSO). After that, second-level classification is made by a Deep residual network (DRN) that is tuned by RHGSO. Thus, the RHGSO-based DRN categorizes the unhealthy plants into Bacterial Leaf Blight (BLB), Blast, and Brown spot. Thus, the implementation of the proposed RHGSO-based deep learning approach offered better accuracy, sensitivity, specificity, and F1-score of 0.9304, 0.9459, 0.8383, and 0.9142.
水稻病害的自动识别与分类在农业领域具有十分重要的意义。深度学习是农业模式识别中一个有效的研究领域，它可以有效地解决病害识别问题。本文提出了一种植物病害分类的混合优化算法。利用感兴趣区域(Region of Interest, ROI)进行预处理，并结合水稻植物数据集和水稻病害数据集生成输入图像。使用深度模糊聚类实现分割。考虑了统计特征、熵、卷积神经网络(CNN)特征、局部最优导向模式(LOOP)和局部Gabor XOR模式(LGXP)等特征，以提取适当的特征进行进一步处理。数据增强是为了扩大提取的特征量。然后，采用基于Rider Henry气体溶解度优化(RHGSO)的深度神经模糊网络(DNFN)进行第一级分类，该网络将植物分为健康植物和不健康植物。RHGSO是Rider优化算法(ROA)和Henry气体溶解度优化算法(HGSO)的结合。然后，通过RHGSO调优的深度残差网络(Deep residual network, DRN)进行二级分类。因此，基于rhgso的DRN将有害植物分为细菌性叶枯病(BLB)、Blast和Brown spot。因此，基于rhgso的深度学习方法具有更好的准确率、灵敏度、特异性，f1评分分别为0.9304、0.9459、0.8383和0.9142。
{"title":"Rice Plant Leaf Disease Detection and Classification Using Optimization Enabled Deep Learning","authors":"T. Daniya, S. Vigneshwari","doi":"10.3808/jei.202300492","DOIUrl":"https://doi.org/10.3808/jei.202300492","url":null,"abstract":"An automatic identification and classification of rice diseases are very important in the domain of agriculture. Deep learning (DL) is an effective research area in the identification of agriculture pattern identification where it can effectively resolve the issues of diseases identification. In this paper, a hybrid optimization algorithm is developed to categorize the plant diseases. The pre-processing is made using Region of Interest (ROI) extraction and the input image is created by combining the Rice plant dataset, and Rice disease dataset. The segmentation is accomplished using Deep fuzzy clustering. The features, like statistical features, entropy, Convolutional Neural Network (CNN) features, Local Optimal-Oriented Pattern (LOOP), and Local Gabor XOR Pattern (LGXP) is considered for extracting the appropriate features for further processing. The data augmentation is employed to enlarge the volume of extracted features. Then, the first level classification is made by deep neuro-fuzzy network (DNFN), which is trained using Rider Henry Gas Solubility Optimization (RHGSO) that categories into healthy and unhealthy plants. The RHGSO is the integration of Rider Optimization Algorithm (ROA) and Henry gas solubility optimization (HGSO). After that, second-level classification is made by a Deep residual network (DRN) that is tuned by RHGSO. Thus, the RHGSO-based DRN categorizes the unhealthy plants into Bacterial Leaf Blight (BLB), Blast, and Brown spot. Thus, the implementation of the proposed RHGSO-based deep learning approach offered better accuracy, sensitivity, specificity, and F1-score of 0.9304, 0.9459, 0.8383, and 0.9142.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76397552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
COVID-19 lockdown has caused a reduction in traffic volume and industrial activities which are the main sources of air pollution in whole of the world. As tropospheric NO2 pollutant and nighttime light (NTL) are the representative of human activities, this study focused to quantify the annual and monthly change of NO2 concentration and NTL in 14 metropolises of Iran before, during and after the lockdown months such as March, April, October and November. TROPOMI images of Sentinel-5p were used for investigation of NO2 column density in 2019, 2020 and 2021, and the variation of NTL was monitored by VIIRS images. The findings showed the majority of metropolises have an increase of NO2 concentration in March and October and a decrease in April and November in 2020 but a significant increase in 2021. The similar pattern of NTL change as NO2 was observed in the most metropolises. The correlation coefficient between NO2 concentration and NTL was calculated from 0.66 to 0.75. So, in majority of metropolises, the reduction of NO2 was observed with reduction of NTL. According to the results, reducing traffic volume as mobile source does not has an effective contribution in NO2 emission in some metropolises of Iran which the stationary sources are dominant such as Isfahan. Tehran as the capital of Iran showed the highest annual mean NO2 reduction in lockdown, this finding showed the important role of traffic volume on air quality of Tehran compared to industrial activities. The integrated application of TROPOMI and NTL data will help to better decision making for controlling and managing of air quality in country's urban area.
{"title":"Spatiotemporal Variation of Nitrogen Dioxide and Nighttime Light Dataset of Iranian Metropolises in the COVID-19 Outbreak","authors":"S. Sangi, S. Falahatkar, M. Gholamalifard","doi":"10.3808/jei.202300488","DOIUrl":"https://doi.org/10.3808/jei.202300488","url":null,"abstract":"COVID-19 lockdown has caused a reduction in traffic volume and industrial activities which are the main sources of air pollution in whole of the world. As tropospheric NO2 pollutant and nighttime light (NTL) are the representative of human activities, this study focused to quantify the annual and monthly change of NO2 concentration and NTL in 14 metropolises of Iran before, during and after the lockdown months such as March, April, October and November. TROPOMI images of Sentinel-5p were used for investigation of NO2 column density in 2019, 2020 and 2021, and the variation of NTL was monitored by VIIRS images. The findings showed the majority of metropolises have an increase of NO2 concentration in March and October and a decrease in April and November in 2020 but a significant increase in 2021. The similar pattern of NTL change as NO2 was observed in the most metropolises. The correlation coefficient between NO2 concentration and NTL was calculated from 0.66 to 0.75. So, in majority of metropolises, the reduction of NO2 was observed with reduction of NTL. According to the results, reducing traffic volume as mobile source does not has an effective contribution in NO2 emission in some metropolises of Iran which the stationary sources are dominant such as Isfahan. Tehran as the capital of Iran showed the highest annual mean NO2 reduction in lockdown, this finding showed the important role of traffic volume on air quality of Tehran compared to industrial activities. The integrated application of TROPOMI and NTL data will help to better decision making for controlling and managing of air quality in country's urban area.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80716805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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