Allawi, Mohammed Falah and Al-Ani, Yasir and Jalal, Arkan Dhari and Ismael, Zainab Malik and Sherif, Mohsen and El-Shafie, Ahmed (2024) Groundwater quality parameters prediction based on data-driven models. Engineering Applications of Computational Fluid Mechanics, 18 (1). p. 2364749. ISSN 1994-2060, DOI https://doi.org/10.1080/19942060.2024.2364749.
Full text not available from this repository.Abstract
Groundwater quality assessment is essential for achieving safe and sustainable water resources, specifically in regions that rely mainly on groundwater. This study focuses on evaluating groundwater quality metrics in the Alnekheeb basin located in Iraq to obtain a more suitable and sustainable water source, which plays a pivotal role in policy development and strategies for more efficient utilization of groundwater. In this regard, three groundwater water quality metrics presented in hardness, sodium absorption ratio (SAR), and salinity are purportedly predicted using two AI-driven models, namely the Radial Basis Neural Network (RBF-NN) and the Probabilistic Neural Network (PNN). Furthermore, this study investigates the influence of input parameters on the performance of the proposed models. Several water quality parameters, including SO4, Cl, NO3, Ca, Mg, Na, HCO3, and CO3, are used for the development modelling. The effectiveness of the proposed models is assessed using various statistical indicators and graphical presentations. According to the evaluation results, adding more input variables can sometimes increase the efficacy of the proposed models with regard to prediction accuracy. Moreover, the findings show that the PNN model provides a promising performance in predicting the groundwater's water quality (WQ) matrices, showing superior performance compared to the RBFNN model.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Water quality; Prediction; Groundwater; Artificial intelligence |
| 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: | 13 Sep 2024 04:16 |
| Last Modified: | 13 Sep 2024 04:21 |
| URI: | http://eprints.um.edu.my/id/eprint/45081 |
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