Application of Water Quality Indices, Machine Learning Approaches, and GIS to Identify Groundwater Quality for Irrigation Purposes: A Case Study of Sahara Aquifer, Doucen Plain, Algeria

被引：71

作者：

Gaagai, Aissam ^{[1
]}

Aouissi, Hani Amir ^{[1
,2
,3
]}

Bencedira, Selma ^{[2
,4
]}

Hinge, Gilbert ^{[5
]}

Athamena, Ali ^{[6
]}

Haddam, Salim ^{[7
]}

Gad, Mohamed ^{[8
]}

Elsherbiny, Osama ^{[9
]}

Elsayed, Salah ^{[10
]}

Eid, Mohamed Hamdy ^{[11
,12
]}

Ibrahim, Hekmat ^{[13
]}

机构：

[1] Sci & Tech Res Ctr Arid Reg CRSTRA, Biskra 07000, Algeria

[2] Badji Mokhtar Annaba Univ, Environm Res Ctr CRE, Annaba 23000, Algeria

[3] Univ Sci & Technol USTHB, Lab Rech & Etud Amenagement & Urbanisme LREAU, Algiers 16000, Algeria

[4] Univ Badji Mokhtar Annaba, Fac Technol, Dept Proc Engn, Lab LGE, Annaba 23000, Algeria

[5] Natl Inst Technol Durgapur, Dept Civil Engn, Durgapur 713209, India

[6] Univ Batna 2, Inst Earth & Universe Sci, Dept Geol, Fesdis 05078, Algeria

[7] Univ 20 Aout 1955, Fac Sci, Agron Dept, Hydraul Div, Skikda 21000, Algeria

[8] Univ Sadat City, Environm Studies & Res Inst, Evaluat Nat Resources Dept, Hydrogeol, Menoufia 32897, Egypt

[9] Mansoura Univ, Fac Agr, Agr Engn Dept, Mansoura 35516, Egypt

[10] Univ Sadat City, Environm Studies & Res Inst, Evaluat Nat Resources Dept, Agr Engn, Menoufia 32897, Egypt

[11] Univ Miskolc, Inst Environm Management, Fac Earth Sci, H-3515 Miskolc, Hungary

[12] Beni Suef Univ, Fac Sci, Geol Dept, Bani Suwayf 65211, Egypt

[13] Menoufia Univ, Fac Sci, Geol Dept, Shibin Al Kawm 51123, Minufiya, Egypt

来源：

WATER | 2023年 / 15卷 / 02期

关键词：

geographic information system (GIS); artificial neural network (ANN); multivariate analysis; gradient boosting regression (GBR); Sahara aquifer; Algeria; MULTIVARIATE STATISTICAL-METHODS; SODIUM ADSORPTION RATIO; HYDROCHEMICAL CHARACTERIZATION; SURFACE-WATER; CLASSIFICATION; SUITABILITY; MECHANISMS; DISTRICT; DRINKING; NETWORK;

D O I：

10.3390/w15020289

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

In order to evaluate and project the quality of groundwater utilized for irrigation in the Sahara aquifer in Algeria, this research employed irrigation water quality indices (IWQIs), artificial neural network (ANN) models, and Gradient Boosting Regression (GBR), alongside multivariate statistical analysis and a geographic information system (GIS), to assess and forecast the quality of groundwater used for irrigation in the Sahara aquifer in Algeria. Twenty-seven groundwater samples were examined using conventional analytical methods. The obtained physicochemical parameters for the collected groundwater samples showed that Ca2+ > Mg2+ > Na+ > K+, and Cl- > SO42- > HCO3- > NO3-, owing to the predominance of limestone, sandstone, and clay minerals under the effects of human activity, ion dissolution, rock weathering, and exchange processes, which indicate a Ca-Cl water type. For evaluating the quality of irrigation water, the IWQIs values such as irrigation water quality index (IWQI), sodium adsorption ratio (SAR), Kelly index (KI), sodium percentage (Na%), permeability index (PI), and magnesium hazard (MH) showed mean values of 47.17, 1.88, 0.25, 19.96, 41.18, and 27.87, respectively. For instance, the IWQI values revealed that 33% of samples were severely restricted for irrigation, while 67% of samples varied from moderate to high restriction for irrigation, indicating that crops that are moderately to highly hypersensitive to salt should be watered in soft soils without any compressed layers. Two-machine learning models were applied, i.e., the ANN and GBR for IWQI, and the ANN model, which surpassed the GBR model. The findings showed that ANN-2F had the highest correlation between IWQI and exceptional features, making it the most accurate prediction model. For example, this model has two qualities that are critical for the IWQI prediction. The outputs' R-2 values for the training and validation sets are 0.973 (RMSE = 2.492) and 0.958 (RMSE = 2.175), respectively. Finally, the application of physicochemical parameters and water quality indices supported by GIS methods, machine learning, and multivariate modeling is a useful and practical strategy for evaluating the quality and development of groundwater.

引用

页数：23