Ehteram, Mohammad and Ahmed, Ali Najah and Sherif, Mohsen and El-Shafie, Ahmed (2024) An advanced deep learning model for predicting water quality index. Ecological Indicators, 160. p. 111806. ISSN 1470-160X, DOI https://doi.org/10.1016/j.ecolind.2024.111806.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Deep Learning model; Water resource management; Water quality index; Hybrid models |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 28 Oct 2024 07:53 |
| Last Modified: | 28 Oct 2024 07:53 |
| URI: | http://eprints.um.edu.my/id/eprint/45544 |
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