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.
Highlights
Introduction published maps and institutional affilGroundwater is the primary source of fresh water in many arid and semi-arid regions with low or negligible recharge due to the prolonged duration of drought and a small amount of rainfall
The motivation of this study demonstrated excellent artificial intelligence (AI) techniques for predictions of GW-based modelling have developed to a higher degree in these countries mentioned earlier but still need more attention in Saudi Arabia
determination coefficient (DC) is used to evaluate the suitability of a statistical model for the given data; they provide information regarding how well a model fits the data
Summary
Introduction published maps and institutional affilGroundwater is the primary source of fresh water in many arid and semi-arid regions with low or negligible recharge due to the prolonged duration of drought and a small amount of rainfall. The majority of groundwater is a non-renewable resource. The demand for groundwater has been dramatically increased due to the increase in population and expansion of urban, industrial, and agricultural activities [1]. All these factors play a significant role in increasing non-renewable groundwater abstraction. Groundwater quality has been deteriorated due to the aforementioned human developments. There is a need to model the spatial distribution of water quality parameters (e.g., TDS) to help the decision-maker have a sustainable water resources development plan and secure the most precious water resource [2,3]
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