Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of blackfoot disease in Taiwan
The back-propagation (BP) artificial neural network (ANN) is applied to forecast the variation of the quality of groundwater in the blackfoot disease area in Taiwan. Three types of BP ANN models were established to evaluate their learning performance. Model A included five concentration parameters a...
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| Veröffentlicht in: | Water research (Oxford) 2004, Vol.38 (1), p.148-158 |
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| creator | Kuo, Yi-Ming Liu, Chen-Wuing Lin, Kao-Hung |
| description | The back-propagation (BP) artificial neural network (ANN) is applied to forecast the variation of the quality of groundwater in the blackfoot disease area in Taiwan. Three types of BP ANN models were established to evaluate their learning performance. Model A included five concentration parameters as input variables for seawater intrusion and three concentration parameters as input variables for arsenic pollutant, respectively, whereas models B and C used only one concentration parameter for each. Furthermore, model C used seasonal data from two seasons to train the ANN, whereas models A and C used only data from one season. The results indicate that model C outperforms models A and B. Model C can describe complex variation of groundwater quality and be used to perform reliable forecasting. Moreover, the number of hidden nodes does not significantly influence the performance of the ANN model in training or testing. |
| doi_str_mv | 10.1016/j.watres.2003.09.026 |
| format | Article |
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Three types of BP ANN models were established to evaluate their learning performance. Model A included five concentration parameters as input variables for seawater intrusion and three concentration parameters as input variables for arsenic pollutant, respectively, whereas models B and C used only one concentration parameter for each. Furthermore, model C used seasonal data from two seasons to train the ANN, whereas models A and C used only data from one season. The results indicate that model C outperforms models A and B. Model C can describe complex variation of groundwater quality and be used to perform reliable forecasting. 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Geothermics</subject><subject>Exact sciences and technology</subject><subject>Forecasting</subject><subject>Groundwater quality</subject><subject>Groundwaters</subject><subject>Humans</subject><subject>Natural water pollution</subject><subject>neural networks</subject><subject>Neural Networks (Computer)</subject><subject>Peripheral Nervous System Diseases - epidemiology</subject><subject>Pollution</subject><subject>Pollution, environment geology</subject><subject>Reproducibility of Results</subject><subject>Seasons</subject><subject>Seawater</subject><subject>Soil Pollutants - poisoning</subject><subject>Taiwan</subject><subject>Water Pollutants - poisoning</subject><subject>Water Supply</subject><subject>Water treatment and pollution</subject><issn>0043-1354</issn><issn>1879-2448</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2004</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkc9u1DAQhyMEotvCGyDIhd4SPI7jjS9IVVX-SJU40J6tiTMp3mbj1nZ21TfhcXE2K3qD08jjbz6N5pdl74CVwEB-2pR7jJ5CyRmrSqZKxuWLbAXNWhVciOZltmJMVAVUtTjJTkPYMMY4r9Tr7ASErBgAX2W_r3Y4TBitG3PX5_EX5djawcan-Yljjj7a3hqLQz7S5A8l7p2_z7euoyGPLscQKITD7A69_Su7824au7Ql-fxxwoPUjouUcCbaAc1971zMOxsIA83_N2j3OL7JXvU4BHp7rGfZ7Zerm8tvxfWPr98vL64LlEzFooG6R-g7qAmMqThniJKUIDLIW5Ky5lXqQ-pAyzoOhitoQalGdYqbpjrLzhfvg3ePE4WotzYYGgYcyU1Bg4Ra1WL9f1DIBkDUCRQLaLwLwVOvH7zdon_SwPQcnd7oJTo9R6eZ0im6NPb-6J_aLXXPQ8esEvDxCGAwOPQeR2PDM1dXlWqYStyHhevRabzzibn9yRkkC-NrJUUiPi8EpcPuLHkdjKXRUGc9mag7Z_-96x_4MMTn</recordid><startdate>2004</startdate><enddate>2004</enddate><creator>Kuo, Yi-Ming</creator><creator>Liu, Chen-Wuing</creator><creator>Lin, Kao-Hung</creator><general>Elsevier Ltd</general><general>Elsevier Science</general><scope>FBQ</scope><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>C1K</scope><scope>SOI</scope><scope>7QH</scope><scope>7QO</scope><scope>7TV</scope><scope>7UA</scope><scope>8FD</scope><scope>F1W</scope><scope>FR3</scope><scope>H96</scope><scope>L.G</scope><scope>P64</scope></search><sort><creationdate>2004</creationdate><title>Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of blackfoot disease in Taiwan</title><author>Kuo, Yi-Ming ; Liu, Chen-Wuing ; Lin, Kao-Hung</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a609t-815fa1fd15e1cc3220aa6e94eeca2be66523cc3194e1b0d21c291b19989d92c83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2004</creationdate><topic>Applied sciences</topic><topic>Arsenic Poisoning - epidemiology</topic><topic>Arsenic pollutant</topic><topic>Artificial neural network</topic><topic>Back-propagation</topic><topic>Disease Outbreaks</topic><topic>Earth sciences</topic><topic>Earth, ocean, space</topic><topic>Engineering and environment geology. 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| subjects | Applied sciences Arsenic Poisoning - epidemiology Arsenic pollutant Artificial neural network Back-propagation Disease Outbreaks Earth sciences Earth, ocean, space Engineering and environment geology. Geothermics Exact sciences and technology Forecasting Groundwater quality Groundwaters Humans Natural water pollution neural networks Neural Networks (Computer) Peripheral Nervous System Diseases - epidemiology Pollution Pollution, environment geology Reproducibility of Results Seasons Seawater Soil Pollutants - poisoning Taiwan Water Pollutants - poisoning Water Supply Water treatment and pollution |
| title | Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of blackfoot disease in Taiwan |
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