Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios

Shiri, Naser; Shiri, Jalal; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher; Kim, Sungwon; Chung, Il-Moon; Nourani, Vahid; Zounemat‐Kermani, Mohammad

doi:10.1371/journal.pone.0251510

Cited by 36 publications

(12 citation statements)

References 70 publications

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“…The groundwater resources are considered an adequate substitute for surface water, which increases the importance of investigating it [19,20]. The current study affirmative that the groundwater resource in the western Iraqi desert determines its properties for irrigation or another usage [21].…”

Section: Introductionsupporting

confidence: 63%

Groundwater Quality and Sustainability Evaluation for Irrigation Purposes: A Case Study in an Arid Region, Iraq

Khudair¹,

Kamel²,

Sulaiman³

et al. 2022

IJSDP

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Water resources are of great importance in the world's agriculture, especially regarding the scarcity of these resources. That calls for attention appropriate for analysis, study, and research in all issues and aspects that would contribute to the development and maintenance of those resources and achieve the maximum possible levels of quality and efficiency of use. The study was conducted during the fall season of 2020 to study groundwater quality (well water) in the city of Al-Qaim of Anbar Province to explain its suitability for agricultural exploitation in the region. The study included seven sites in Al-Qaim (Rtemi, Medicis, Eastern Akash, Okesha, Sawab, Albu-Hayat, and Al-Karah) to assess the validity of irrigation. The pH, Electrical conductivity (EC), positive ions (K+, Na+, Mg2+, Ca2+), and negative ions (HCO3, Cl-2, SO4) and CO3 were measured. The results showed that the studied well water is acceptable for irrigation purposes in terms of pH value, as for the electrical connection, it was six wells within the class (C2), which is adequate for irrigation for medium-salty crops, except for one well, which is a fine well within class C3 suitable for irrigation of high salinity crops. The total hardness values were low and did not pose any risk; as for the positive and negative ions, they were within the permissible limits within the specified classifications. By measuring the positive ions, the Sodium Adsorption Ratio (SAR) value was calculated and determined to be within the class S1, which means that the groundwater in the region is suitable for irrigation.

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Section: Introductionsupporting

confidence: 63%

Groundwater Quality and Sustainability Evaluation for Irrigation Purposes: A Case Study in an Arid Region, Iraq

Khudair¹,

Kamel²,

Sulaiman³

et al. 2022

IJSDP

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“…The ways remote sensing, geospatial modeling, and/or machine learning are used in hydrologic studies depends on the question being addressed; the spatial and temporal scale of the question; and the type, amount, and quality of the available data [24][25][26]. Nevertheless, these tools have been incorporated into strategies to forecast groundwater levels [27][28][29][30], groundwater quality [31][32][33], saltwater intrusion and groundwater salinity [34], and groundwater resource availability [35,36]. Using these approaches to better understand and predict groundwater discharge is particularly challenging (e.g., [22,23]).…”

Section: Introductionmentioning

confidence: 99%

Using Remote Sensing and Machine Learning to Locate Groundwater Discharge to Salmon-Bearing Streams

et al. 2021

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We hypothesized topographic features alone could be used to locate groundwater discharge, but only where diagnostic topographic signatures could first be identified through the use of limited field observations and geologic data. We built a geodatabase from geologic and topographic data, with the geologic data only covering ~40% of the study area and topographic data derived from airborne LiDAR covering the entire study area. We identified two types of groundwater discharge: shallow hillslope groundwater discharge, commonly manifested as diffuse seeps, and aquifer-outcrop groundwater discharge, commonly manifested as springs. We developed multistep manual procedures that allowed us to accurately predict the locations of both types of groundwater discharge in 93% of cases, though only where geologic data were available. However, field verification suggested that both types of groundwater discharge could be identified by specific combinations of topographic variables alone. We then applied maximum entropy modeling, a machine learning technique, to predict the prevalence of both types of groundwater discharge using six topographic variables: profile curvature range, with a permutation importance of 43.2%, followed by distance to flowlines, elevation, topographic roughness index, flow-weighted slope, and planform curvature, with permutation importance of 20.8%, 18.5%, 15.2%, 1.8%, and 0.5%, respectively. The AUC values for the model were 0.95 for training data and 0.91 for testing data, indicating outstanding model performance.

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“…AI has been extensively employed in science, engineering design, energy, robotics, and economics [16][17][18][19][20][21]. AI-based models (e.g., artificial neural network (ANN) and support vector machine (SVM)) have been used by some researchers worldwide for groundwater quality assessment and prediction [22][23][24][25][26]. The ANN model has been used to measure the groundwater salinity pattern in island aquifers [27].…”

Section: Literature Reviewmentioning

confidence: 99%

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

Benaafi

Yassin

Usman³

et al. 2022

Sustainability

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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. 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.

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Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios

Cited by 36 publications

References 70 publications

Groundwater Quality and Sustainability Evaluation for Irrigation Purposes: A Case Study in an Arid Region, Iraq

Groundwater Quality and Sustainability Evaluation for Irrigation Purposes: A Case Study in an Arid Region, Iraq

Using Remote Sensing and Machine Learning to Locate Groundwater Discharge to Salmon-Bearing Streams

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

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