Development of Irrigation Water Quality Index Using Artificial Neural Network
The data-driven Artificial Intelligence (AI) techniques revealed specific relevance for the treatment of nonlinear relations and predicting the behaviour of complex systems, as a promising application in hydrology and water quality problems. The goal of this study is to build a developed model to f...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Ain Shams University, Faculty of Women for Arts, Science & Education
2023-12-01
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| Series: | Journal of Scientific Research in Science |
| Subjects: | |
| Online Access: | https://jsrs.journals.ekb.eg/article_331805.html |
| _version_ | 1797319721657303040 |
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| author | Nema Mohamed Kandil Raafat Ahmed Rayan Mostafa A. Sadek |
| author_facet | Nema Mohamed Kandil Raafat Ahmed Rayan Mostafa A. Sadek |
| author_sort | Nema Mohamed Kandil |
| collection | DOAJ |
| description | The data-driven Artificial Intelligence (AI) techniques revealed specific relevance for the treatment of nonlinear relations and predicting the behaviour of complex systems, as a promising application in hydrology and water quality problems. The goal of this study is to build a developed model to forecast the quality of irrigation water by estimating its Water Quality Index using Artificial Neural Network (ANN). The developed model is applied to predict a data-based Irrigation Water Quality Index (IWQI) for groundwater usability in a desert reach pilot area in Egypt. The raw data for the model were the results of the main ion-causing irrigation hazards: (Salinity & Infiltration rate& Specific Toxics and Miscellaneous effects) for seventy-seven groundwater samples. The effectiveness of the model was achieved through the standardized coefficient of input variables. Revealing that the developed ANN model has a high agreement between measured and calculated IWQI (R2= 0.963, RMSE=0.0693) and becomes satisfactory verified for predicting the overall quality of groundwater in the research region, which is based on individual measurements rated according to their sensitivity. Moreover, the newly developed model can overcome the problem of missing some sample index parameters when one or more of the parameters are missing. |
| first_indexed | 2024-03-08T04:11:02Z |
| format | Article |
| id | doaj.art-e9afa92bbd8b40a2bafd8dbd1fe27d76 |
| institution | Directory Open Access Journal |
| issn | 2356-8364 2356-8372 |
| language | English |
| last_indexed | 2024-03-08T04:11:02Z |
| publishDate | 2023-12-01 |
| publisher | Ain Shams University, Faculty of Women for Arts, Science & Education |
| record_format | Article |
| series | Journal of Scientific Research in Science |
| spelling | doaj.art-e9afa92bbd8b40a2bafd8dbd1fe27d762024-02-08T19:05:12ZengAin Shams University, Faculty of Women for Arts, Science & EducationJournal of Scientific Research in Science2356-83642356-83722023-12-014018610110.21608/JSRS.2023.331805Development of Irrigation Water Quality Index Using Artificial Neural Network Nema Mohamed Kandil0Raafat Ahmed Rayan1Mostafa A. Sadek2Department of Sitting and Environmental, Nuclear and Radiological Safety Research Centre, Cairo, Egypt. Egyptian Atomic Energy Authority Department of Sitting and Environmental, Nuclear and Radiological Safety Research Centre, Cairo, Egypt. Egyptian Atomic Energy Authority Department of Sitting and Environmental, Nuclear and Radiological Safety Research Centre, Cairo, Egypt. Egyptian Atomic Energy Authority The data-driven Artificial Intelligence (AI) techniques revealed specific relevance for the treatment of nonlinear relations and predicting the behaviour of complex systems, as a promising application in hydrology and water quality problems. The goal of this study is to build a developed model to forecast the quality of irrigation water by estimating its Water Quality Index using Artificial Neural Network (ANN). The developed model is applied to predict a data-based Irrigation Water Quality Index (IWQI) for groundwater usability in a desert reach pilot area in Egypt. The raw data for the model were the results of the main ion-causing irrigation hazards: (Salinity & Infiltration rate& Specific Toxics and Miscellaneous effects) for seventy-seven groundwater samples. The effectiveness of the model was achieved through the standardized coefficient of input variables. Revealing that the developed ANN model has a high agreement between measured and calculated IWQI (R2= 0.963, RMSE=0.0693) and becomes satisfactory verified for predicting the overall quality of groundwater in the research region, which is based on individual measurements rated according to their sensitivity. Moreover, the newly developed model can overcome the problem of missing some sample index parameters when one or more of the parameters are missing.https://jsrs.journals.ekb.eg/article_331805.htmlirrigation water quality indexpredictionartificial neural networkregressionsgroundwater. |
| spellingShingle | Nema Mohamed Kandil Raafat Ahmed Rayan Mostafa A. Sadek Development of Irrigation Water Quality Index Using Artificial Neural Network Journal of Scientific Research in Science irrigation water quality index prediction artificial neural network regressions groundwater. |
| title | Development of Irrigation Water Quality Index Using Artificial Neural Network |
| title_full | Development of Irrigation Water Quality Index Using Artificial Neural Network |
| title_fullStr | Development of Irrigation Water Quality Index Using Artificial Neural Network |
| title_full_unstemmed | Development of Irrigation Water Quality Index Using Artificial Neural Network |
| title_short | Development of Irrigation Water Quality Index Using Artificial Neural Network |
| title_sort | development of irrigation water quality index using artificial neural network |
| topic | irrigation water quality index prediction artificial neural network regressions groundwater. |
| url | https://jsrs.journals.ekb.eg/article_331805.html |
| work_keys_str_mv | AT nemamohamedkandil developmentofirrigationwaterqualityindexusingartificialneuralnetwork AT raafatahmedrayan developmentofirrigationwaterqualityindexusingartificialneuralnetwork AT mostafaasadek developmentofirrigationwaterqualityindexusingartificialneuralnetwork |