A cumulative-risk assessment method based on an artificial neural network model for the water environment
To analyze the cumulative risks to the water environment, the backpropagation artificial neural network (BP-ANN), a self-adapting algorithm, was proposed in this study. A new comprehensive indicator of cumulative risks was formed by combining the water risk assessment tool proposed by the World Wide...
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| Veröffentlicht in: | Environmental science and pollution research international 2021-09, Vol.28 (34), p.46176-46185 |
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| creator | Shi, En Shang, Yanchen Li, Yafeng Zhang, Miao |
| description | To analyze the cumulative risks to the water environment, the backpropagation artificial neural network (BP-ANN), a self-adapting algorithm, was proposed in this study. A new comprehensive indicator of cumulative risks was formed by combining the water risk assessment tool proposed by the World Wide Fund for Nature or World Wildlife Fund (WWF), Deutsche Investitions und Entwicklungsgesellschaft mbH (DEG), and the cumulative environmental risk assessment system proposed by the US Environmental Protection Agency (USEPA). Eleven training algorithms were selected and optimized based on the mean square error (MSE) of prediction results. Data concerning evaluating indicators and cumulative risk indexes of the Liao River collected from 2005 to 2017 in the cities of Tieling, Shenyang, and Panjin, China, were used as input and output data to train, validate, and test the BP-ANN. Levenberg Marquardt backpropagation was the most accurate algorithm, with an MSE of 3.33 × 10
−6
. After optimization, there were six hidden layers in the model. The correlation coefficient of the BP-ANN with LM exceeded 80%. These findings suggest that the BP-ANN model is applicable to prediction of cumulative risks to the water environment. The model was sensitive to the number of wastewater treatment facilities and the wastewater treatment rate along the river. Based on the sensitivity analysis, the contributing factors can be controlled to reduce the cumulative risk. |
| doi_str_mv | 10.1007/s11356-021-12540-6 |
| format | Article |
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−6
. After optimization, there were six hidden layers in the model. The correlation coefficient of the BP-ANN with LM exceeded 80%. These findings suggest that the BP-ANN model is applicable to prediction of cumulative risks to the water environment. The model was sensitive to the number of wastewater treatment facilities and the wastewater treatment rate along the river. Based on the sensitivity analysis, the contributing factors can be controlled to reduce the cumulative risk.</description><identifier>ISSN: 0944-1344</identifier><identifier>EISSN: 1614-7499</identifier><identifier>DOI: 10.1007/s11356-021-12540-6</identifier><identifier>PMID: 33492592</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Aquatic Pollution ; Artificial neural networks ; Atmospheric Protection/Air Quality Control/Air Pollution ; Back propagation ; Back propagation networks ; Correlation coefficient ; Correlation coefficients ; Earth and Environmental Science ; Ecotoxicology ; Environment ; Environmental assessment ; Environmental Chemistry ; Environmental Concerns and Pollution control in the Context of Developing Countries ; Environmental Health ; Environmental protection ; Environmental risk ; Environmental science ; Neural networks ; Optimization ; Risk assessment ; Rivers ; Sensitivity analysis ; Waste Water Technology ; Wastewater treatment ; Water Management ; Water Pollution Control ; Water treatment ; Water treatment plants ; Wildlife</subject><ispartof>Environmental science and pollution research international, 2021-09, Vol.28 (34), p.46176-46185</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021</rights><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-ef950d79f130eb905ee2be4a860b60f5f6ce54d3576d5843253a52df666b7b6d3</citedby><cites>FETCH-LOGICAL-c375t-ef950d79f130eb905ee2be4a860b60f5f6ce54d3576d5843253a52df666b7b6d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11356-021-12540-6$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11356-021-12540-6$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33492592$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Shi, En</creatorcontrib><creatorcontrib>Shang, Yanchen</creatorcontrib><creatorcontrib>Li, Yafeng</creatorcontrib><creatorcontrib>Zhang, Miao</creatorcontrib><title>A cumulative-risk assessment method based on an artificial neural network model for the water environment</title><title>Environmental science and pollution research international</title><addtitle>Environ Sci Pollut Res</addtitle><addtitle>Environ Sci Pollut Res Int</addtitle><description>To analyze the cumulative risks to the water environment, the backpropagation artificial neural network (BP-ANN), a self-adapting algorithm, was proposed in this study. A new comprehensive indicator of cumulative risks was formed by combining the water risk assessment tool proposed by the World Wide Fund for Nature or World Wildlife Fund (WWF), Deutsche Investitions und Entwicklungsgesellschaft mbH (DEG), and the cumulative environmental risk assessment system proposed by the US Environmental Protection Agency (USEPA). Eleven training algorithms were selected and optimized based on the mean square error (MSE) of prediction results. Data concerning evaluating indicators and cumulative risk indexes of the Liao River collected from 2005 to 2017 in the cities of Tieling, Shenyang, and Panjin, China, were used as input and output data to train, validate, and test the BP-ANN. Levenberg Marquardt backpropagation was the most accurate algorithm, with an MSE of 3.33 × 10
−6
. After optimization, there were six hidden layers in the model. The correlation coefficient of the BP-ANN with LM exceeded 80%. These findings suggest that the BP-ANN model is applicable to prediction of cumulative risks to the water environment. The model was sensitive to the number of wastewater treatment facilities and the wastewater treatment rate along the river. Based on the sensitivity analysis, the contributing factors can be controlled to reduce the cumulative risk.</description><subject>Algorithms</subject><subject>Aquatic Pollution</subject><subject>Artificial neural networks</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Back propagation</subject><subject>Back propagation networks</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Earth and Environmental Science</subject><subject>Ecotoxicology</subject><subject>Environment</subject><subject>Environmental assessment</subject><subject>Environmental Chemistry</subject><subject>Environmental Concerns and Pollution control in the Context of Developing Countries</subject><subject>Environmental Health</subject><subject>Environmental protection</subject><subject>Environmental risk</subject><subject>Environmental science</subject><subject>Neural networks</subject><subject>Optimization</subject><subject>Risk assessment</subject><subject>Rivers</subject><subject>Sensitivity analysis</subject><subject>Waste Water Technology</subject><subject>Wastewater treatment</subject><subject>Water Management</subject><subject>Water Pollution Control</subject><subject>Water treatment</subject><subject>Water treatment 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cumulative-risk assessment method based on an artificial neural network model for the water environment</title><author>Shi, En ; Shang, Yanchen ; Li, Yafeng ; Zhang, Miao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-ef950d79f130eb905ee2be4a860b60f5f6ce54d3576d5843253a52df666b7b6d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Aquatic Pollution</topic><topic>Artificial neural networks</topic><topic>Atmospheric Protection/Air Quality Control/Air Pollution</topic><topic>Back propagation</topic><topic>Back propagation networks</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Earth and Environmental Science</topic><topic>Ecotoxicology</topic><topic>Environment</topic><topic>Environmental assessment</topic><topic>Environmental Chemistry</topic><topic>Environmental Concerns and Pollution control in 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analyze the cumulative risks to the water environment, the backpropagation artificial neural network (BP-ANN), a self-adapting algorithm, was proposed in this study. A new comprehensive indicator of cumulative risks was formed by combining the water risk assessment tool proposed by the World Wide Fund for Nature or World Wildlife Fund (WWF), Deutsche Investitions und Entwicklungsgesellschaft mbH (DEG), and the cumulative environmental risk assessment system proposed by the US Environmental Protection Agency (USEPA). Eleven training algorithms were selected and optimized based on the mean square error (MSE) of prediction results. Data concerning evaluating indicators and cumulative risk indexes of the Liao River collected from 2005 to 2017 in the cities of Tieling, Shenyang, and Panjin, China, were used as input and output data to train, validate, and test the BP-ANN. Levenberg Marquardt backpropagation was the most accurate algorithm, with an MSE of 3.33 × 10
−6
. After optimization, there were six hidden layers in the model. The correlation coefficient of the BP-ANN with LM exceeded 80%. These findings suggest that the BP-ANN model is applicable to prediction of cumulative risks to the water environment. The model was sensitive to the number of wastewater treatment facilities and the wastewater treatment rate along the river. Based on the sensitivity analysis, the contributing factors can be controlled to reduce the cumulative risk.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>33492592</pmid><doi>10.1007/s11356-021-12540-6</doi><tpages>10</tpages></addata></record> |
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| subjects | Algorithms Aquatic Pollution Artificial neural networks Atmospheric Protection/Air Quality Control/Air Pollution Back propagation Back propagation networks Correlation coefficient Correlation coefficients Earth and Environmental Science Ecotoxicology Environment Environmental assessment Environmental Chemistry Environmental Concerns and Pollution control in the Context of Developing Countries Environmental Health Environmental protection Environmental risk Environmental science Neural networks Optimization Risk assessment Rivers Sensitivity analysis Waste Water Technology Wastewater treatment Water Management Water Pollution Control Water treatment Water treatment plants Wildlife |
| title | A cumulative-risk assessment method based on an artificial neural network model for the water environment |
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