Water Quality Prediction Using Artificial Intelligence Algorithms
During the last years, water quality has been threatened by various pollutants. Therefore, modeling and predicting water quality have become very important in controlling water pollution. In this work, advanced artificial intelligence (AI) algorithms are developed to predict water quality index (WQI...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Applied Bionics and Biomechanics |
| Online Access: | http://dx.doi.org/10.1155/2020/6659314 |
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| author | Theyazn H. H Aldhyani Mohammed Al-Yaari Hasan Alkahtani Mashael Maashi |
| author_facet | Theyazn H. H Aldhyani Mohammed Al-Yaari Hasan Alkahtani Mashael Maashi |
| author_sort | Theyazn H. H Aldhyani |
| collection | DOAJ |
| description | During the last years, water quality has been threatened by various pollutants. Therefore, modeling and predicting water quality have become very important in controlling water pollution. In this work, advanced artificial intelligence (AI) algorithms are developed to predict water quality index (WQI) and water quality classification (WQC). For the WQI prediction, artificial neural network models, namely nonlinear autoregressive neural network (NARNET) and long short-term memory (LSTM) deep learning algorithm, have been developed. In addition, three machine learning algorithms, namely, support vector machine (SVM), K-nearest neighbor (K-NN), and Naive Bayes, have been used for the WQC forecasting. The used dataset has 7 significant parameters, and the developed models were evaluated based on some statistical parameters. The results revealed that the proposed models can accurately predict WQI and classify the water quality according to superior robustness. Prediction results demonstrated that the NARNET model performed slightly better than the LSTM for the prediction of the WQI values and the SVM algorithm has achieved the highest accuracy (97.01%) for the WQC prediction. Furthermore, the NARNET and LSTM models have achieved similar accuracy for the testing phase with a slight difference in the regression coefficient (RNARNET=96.17% and RLSTM=94.21%). This kind of promising research can contribute significantly to water management. |
| format | Article |
| id | doaj-art-d321ae1ec5704ff8ad4aec53ba8fb7e9 |
| institution | Kabale University |
| issn | 1176-2322 1754-2103 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Bionics and Biomechanics |
| spelling | doaj-art-d321ae1ec5704ff8ad4aec53ba8fb7e92025-08-20T03:24:12ZengWileyApplied Bionics and Biomechanics1176-23221754-21032020-01-01202010.1155/2020/66593146659314Water Quality Prediction Using Artificial Intelligence AlgorithmsTheyazn H. H Aldhyani0Mohammed Al-Yaari1Hasan Alkahtani2Mashael Maashi3Community College of Abqaiq, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi ArabiaChemical Engineering Department, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi ArabiaCollege of Computer Science and Information Technology, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi ArabiaSoftware Engineering Department, King Saud University, Riyadh 11543, Saudi ArabiaDuring the last years, water quality has been threatened by various pollutants. Therefore, modeling and predicting water quality have become very important in controlling water pollution. In this work, advanced artificial intelligence (AI) algorithms are developed to predict water quality index (WQI) and water quality classification (WQC). For the WQI prediction, artificial neural network models, namely nonlinear autoregressive neural network (NARNET) and long short-term memory (LSTM) deep learning algorithm, have been developed. In addition, three machine learning algorithms, namely, support vector machine (SVM), K-nearest neighbor (K-NN), and Naive Bayes, have been used for the WQC forecasting. The used dataset has 7 significant parameters, and the developed models were evaluated based on some statistical parameters. The results revealed that the proposed models can accurately predict WQI and classify the water quality according to superior robustness. Prediction results demonstrated that the NARNET model performed slightly better than the LSTM for the prediction of the WQI values and the SVM algorithm has achieved the highest accuracy (97.01%) for the WQC prediction. Furthermore, the NARNET and LSTM models have achieved similar accuracy for the testing phase with a slight difference in the regression coefficient (RNARNET=96.17% and RLSTM=94.21%). This kind of promising research can contribute significantly to water management.http://dx.doi.org/10.1155/2020/6659314 |
| spellingShingle | Theyazn H. H Aldhyani Mohammed Al-Yaari Hasan Alkahtani Mashael Maashi Water Quality Prediction Using Artificial Intelligence Algorithms Applied Bionics and Biomechanics |
| title | Water Quality Prediction Using Artificial Intelligence Algorithms |
| title_full | Water Quality Prediction Using Artificial Intelligence Algorithms |
| title_fullStr | Water Quality Prediction Using Artificial Intelligence Algorithms |
| title_full_unstemmed | Water Quality Prediction Using Artificial Intelligence Algorithms |
| title_short | Water Quality Prediction Using Artificial Intelligence Algorithms |
| title_sort | water quality prediction using artificial intelligence algorithms |
| url | http://dx.doi.org/10.1155/2020/6659314 |
| work_keys_str_mv | AT theyaznhhaldhyani waterqualitypredictionusingartificialintelligencealgorithms AT mohammedalyaari waterqualitypredictionusingartificialintelligencealgorithms AT hasanalkahtani waterqualitypredictionusingartificialintelligencealgorithms AT mashaelmaashi waterqualitypredictionusingartificialintelligencealgorithms |