Publication: An advanced deep learning model for predicting water quality index
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Date
2024
Authors
Ehteram M.
Ahmed A.N.
Sherif M.
El-Shafie A.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier B.V.
Abstract
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)
Description
Keywords
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