An advanced deep learning model for predicting water quality index
Predicting a water quality index (WQI) is important because it serves as an important metric for assessing the overall health and safety of water bodies. Our paper develops a new hybrid model for predicting the WQI. The study uses a combination of a convolutional neural network (CNN), clockwork recu...
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my.uniten.dspace-366922025-03-03T15:43:57Z An advanced deep learning model for predicting water quality index Ehteram M. Ahmed A.N. Sherif M. El-Shafie A. 57113510800 57214837520 7005414714 16068189400 Malaysia Analysis of variance (ANOVA) Convolutional neural networks Learning systems Quality assurance Recurrent neural networks Water management Water quality Convolutional neural network Deep learning model General linear model analysis Health and safety Hybrid model Learning models Water quality indexes Water quality parameters Water resources management Waterbodies error analysis index method machine learning parameterization variance analysis water management water quality water resource Forecasting Predicting a water quality index (WQI) is important because it serves as an important metric for assessing the overall health and safety of water bodies. Our paper develops a new hybrid model for predicting the WQI. The study uses a combination of a convolutional neural network (CNN), clockwork recurrent neural network (Clockwork RNN), and M5 Tree (CNN-CRNN-M5T) to predict a WQI. The M5T model lacks advanced operators for extracting meaningful data from water quality parameters, so the new model enhances its ability to analyze intricate patterns. The general linear model analysis of variance (GLM-ANOVA) is an improved version of the ANOVA. Our study uses the GLM-ANOVA to determine significant inputs. As all input variables had p < 0.050, they were defined as significant variables. Results showed that NH-NL and PH had the highest and lowest impact, respectively. Our study used the CNN-CRNN-M5T, CNN-CRNN, CRNN-M5T, CNN-M5T, CRNN, CNN, and M5T models to predict the WQI of a large basin in Malaysia. The CNN-CRNN decreased testing mean absolute error (MAE) of the CRNN, CNN, and M5T models by 2.1 %, 12 %, and 15 %, respectively. The CNN-CRNN-M5T model increased Nash?Sutcliffe efficiency coefficient of the other models by 4?20 % and 2.1?19 %, respectively. The CNN-CRNN-M5T model was a reliable tool for spatial and temporal predictions of WQI. ? 2024 The Author(s) Final 2025-03-03T07:43:57Z 2025-03-03T07:43:57Z 2024 Article 10.1016/j.ecolind.2024.111806 2-s2.0-85186598296 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186598296&doi=10.1016%2fj.ecolind.2024.111806&partnerID=40&md5=643e3bac28383ec6c8e939060b501577 https://irepository.uniten.edu.my/handle/123456789/36692 160 111806 All Open Access; Gold Open Access Elsevier B.V. Scopus |
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Malaysia Analysis of variance (ANOVA) Convolutional neural networks Learning systems Quality assurance Recurrent neural networks Water management Water quality Convolutional neural network Deep learning model General linear model analysis Health and safety Hybrid model Learning models Water quality indexes Water quality parameters Water resources management Waterbodies error analysis index method machine learning parameterization variance analysis water management water quality water resource Forecasting |
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Malaysia Analysis of variance (ANOVA) Convolutional neural networks Learning systems Quality assurance Recurrent neural networks Water management Water quality Convolutional neural network Deep learning model General linear model analysis Health and safety Hybrid model Learning models Water quality indexes Water quality parameters Water resources management Waterbodies error analysis index method machine learning parameterization variance analysis water management water quality water resource Forecasting Ehteram M. Ahmed A.N. Sherif M. El-Shafie A. An advanced deep learning model for predicting water quality index |
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Predicting a water quality index (WQI) is important because it serves as an important metric for assessing the overall health and safety of water bodies. Our paper develops a new hybrid model for predicting the WQI. The study uses a combination of a convolutional neural network (CNN), clockwork recurrent neural network (Clockwork RNN), and M5 Tree (CNN-CRNN-M5T) to predict a WQI. The M5T model lacks advanced operators for extracting meaningful data from water quality parameters, so the new model enhances its ability to analyze intricate patterns. The general linear model analysis of variance (GLM-ANOVA) is an improved version of the ANOVA. Our study uses the GLM-ANOVA to determine significant inputs. As all input variables had p < 0.050, they were defined as significant variables. Results showed that NH-NL and PH had the highest and lowest impact, respectively. Our study used the CNN-CRNN-M5T, CNN-CRNN, CRNN-M5T, CNN-M5T, CRNN, CNN, and M5T models to predict the WQI of a large basin in Malaysia. The CNN-CRNN decreased testing mean absolute error (MAE) of the CRNN, CNN, and M5T models by 2.1 %, 12 %, and 15 %, respectively. The CNN-CRNN-M5T model increased Nash?Sutcliffe efficiency coefficient of the other models by 4?20 % and 2.1?19 %, respectively. The CNN-CRNN-M5T model was a reliable tool for spatial and temporal predictions of WQI. ? 2024 The Author(s) |
| author2 |
57113510800 |
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57113510800 Ehteram M. Ahmed A.N. Sherif M. El-Shafie A. |
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Article |
| author |
Ehteram M. Ahmed A.N. Sherif M. El-Shafie A. |
| author_sort |
Ehteram M. |
| title |
An advanced deep learning model for predicting water quality index |
| title_short |
An advanced deep learning model for predicting water quality index |
| title_full |
An advanced deep learning model for predicting water quality index |
| title_fullStr |
An advanced deep learning model for predicting water quality index |
| title_full_unstemmed |
An advanced deep learning model for predicting water quality index |
| title_sort |
advanced deep learning model for predicting water quality index |
| publisher |
Elsevier B.V. |
| publishDate |
2025 |
| _version_ |
1825816145452597248 |
| score |
13.244413 |