Neurocomputing Modelling of Hydrochemical and Physical Properties of Groundwater Coupled with Spatial Clustering, GIS, and Statistical Techniques

Author:

Benaafi Mohammed,Yassin Mohamed A.ORCID,Usman A. G.,Abba S. I.

Abstract

Groundwater (GW) is a critical freshwater resource for billions of individuals worldwide. Rapid anthropogenic exploitation has increasingly deteriorated GW quality and quantity. Reliable estimation of complex hydrochemical properties of GW is crucial for sustainable development. Real field and experimental studies in an agricultural area from the significant sandstone aquifers (Wajid Aquifer) were conducted. For the modelling purpose, three types of computational models, including the emerging Hammerstein–Wiener (HW), back propagation neural network (BPNN), and statistical multi-variate regression (MVR), were developed for the multi-station estimation of total dissolved solids (TDS) (mg/L) and total hardness (TH) (mg/L). A geographic information system (GIS) was used for the spatial variability assessment of 32 hydrochemical and physical properties of the GW aquifer. A comprehensive visualized literature review spanning several decades was conducted in order to gain an understanding of the existing research and debates relevant to a particular GW and artificial intelligence (AI) study. The experimental data, pre-processing, and feature selection were conducted to determine the most dominant variables for AI-based modelling. The estimation results were evaluated using determination coefficient (DC), mean bias error (MBE), mean square error (MSE), and root mean square error (RMSE). The outcomes proved that TDS (mg/L) and TH (mg/L) correlated more than 90% and 70–85% with Ca2+, Cl−, Br−, NO3−, and Fe, and Na+, SO42−, Mg2+, and F− combinations, respectively. HW-M1 justified promising among all the models with MBE = 1.41 × 10−11, 1.14 × 10−14, and MSE = 7.52 × 10−2, 3.88 × 10−11 for TDS (mg/L), TH (mg/L), respectively. The accuracy proved merit for the overall development of and practical estimation of hydrochemical variables (TDS, TH) (mg/L) and decision-making benchmarks.

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development

Reference53 articles.

1. Hydrochemical and Isotopic Investigation of the Groundwater from Wajid Aquifer in Wadi Al-Dawasir, Southern Saudi Arabia

2. Machine Learning Algorithms for the Forecasting of Wastewater Quality Indicators

3. Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction

4. Groundwater level forecasting with artificial neural networks: A comparison of LSTM, CNN and NARX;Wunsch;Hydrol. Earth Syst. Sci. Discuss.,2020

5. Simulation of Groundwater Level Variations Using Wavelet Combined with Neural Network, Linear Regression and Support Vector Machine;Ebrahimi,2017

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3