Estimation of irrigation water quality index in a semi-arid environment using data-driven approach

M’nassri, Soumaia; Amri, Amsal; Nasri, Nessrine; Majdoub, Rajouene

doi:10.2166/ws.2022.157

Cited by 13 publications

(5 citation statements)

References 52 publications

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“…The choice of the best model should be influenced by specific application requirements and priorities, taking into account predictive accuracy, model complexity, and stability. The results obtained from this study agree with research studies that have applied similar approaches for IWQI, which indicated the high performance and stability of machine learning models for IWQI prediction [35,[69][70][71]. Lap et al [72] indicated that the random forest (RF) model excels in accurately forecasting WQI values for the An Hai irrigation system in Vietnam, achieving a good Similarity score of 0.94.…”

Section: Discussionsupporting

confidence: 85%

Groundwater Quality Assessment and Irrigation Water Quality Index Prediction Using Machine Learning Algorithms

Hussein,

Derdour,

Zerouali

et al. 2024

Water

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The evaluation of groundwater quality is crucial for irrigation purposes; however, due to financial constraints in developing countries, such evaluations suffer from insufficient sampling frequency, hindering comprehensive assessments. Therefore, associated with machine learning approaches and the irrigation water quality index (IWQI), this research aims to evaluate the groundwater quality in Naama, a region in southwest Algeria. Hydrochemical parameters (cations, anions, pH, and EC), qualitative indices (SAR,RSC,Na%,MH,and PI), as well as geospatial representations were used to determine the groundwater’s suitability for irrigation in the study area. In addition, efficient machine learning approaches for forecasting IWQI utilizing Extreme Gradient Boosting (XGBoost), Support vector regression (SVR), and K-Nearest Neighbours (KNN) models were implemented. In this research, 166 groundwater samples were used to calculate the irrigation index. The results showed that 42.18% of them were of excellent quality, 34.34% were of very good quality, 6.63% were good quality, 9.64% were satisfactory, and 4.21% were considered unsuitable for irrigation. On the other hand, results indicate that XGBoost excels in accuracy and stability, with a low RMSE (of 2.8272 and a high R of 0.9834. SVR with only four inputs (Ca2+, Mg2+, Na+, and K) demonstrates a notable predictive capability with a low RMSE of 2.6925 and a high R of 0.98738, while KNN showcases robust performance. The distinctions between these models have important implications for making informed decisions in agricultural water management and resource allocation within the region.

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Section: Discussionsupporting

confidence: 85%

Groundwater Quality Assessment and Irrigation Water Quality Index Prediction Using Machine Learning Algorithms

Hussein,

Derdour,

Zerouali

et al. 2024

Water

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“…The presence of undesired dissolved salts or components is the primary factor used to determine water suitability for various uses (Sener et al 2017;Megahed et al 2022;Kamboj and Kamboj 2019). There are many methods utilized to evaluate the water quality for use in irrigation and for drinking such as the water quality index (WQI) (Oregon 2001;CCME1999) and the irrigation water quality index (IWQI) (Yıldız and Karakuş 2020;Soumaia et al 2022).…”

Section: Water Quality Index (Wqi)mentioning

confidence: 99%

Assessing surface water uses by water quality index: application of Qalyubia Governorate, Southeast Nile Delta, Egypt

Gabr,

Soussa

2023

Appl Water Sci

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In this study, water quality index (WQI) was applied at one of the most crowded Nile reaches surroundings in Egypt to investigate its impacts on different water uses. Twelve sites are selected for monitoring (S1–S12) within the irrigation system network of the Qalyubia Governorate, and water samples from these locations in 2021 were collected, analyzed, assessed, and compared to analyze water quality data collected in 2014 in light of the Egyptian permissible water quality limits regarding the drinking and irrigation uses. These two main sets of data were chosen to emphasize the change in WQI within this period, denoting the time before the epidemic COVID19 and after the lockdown of the epidemic in 2021 in the study area. Fifteen physicochemical water parameters were measured. The results show that the WQI for the surface water monitoring sites is classified as good quality except for S8, which showed a poor water quality type. In addition, S2 has recorded a BOD concentration of 6.4 mg/L, which is slightly above the Egyptian limit (6 mg/L). Also, S8 recorded a slight increase in Fe, Mn, and F compared to permissible concentrations. TDS varies between 258.08, 318, and (249 ± 18.2) mg/L for minimum, maximum, and average ± standard deviation, respectively, indicating suitable irrigation water. The study suggests that surface water can be used to irrigate crops, but it should be purified before being used for drinking purposes. A comparison of the water quality parameters for the years 2014 and 2021 for the sites S1, S4, S5, S7, S9, and S10 reveals an increase in water quality from poor (2014) to good (2021). Therefore, the poor water quality in 2014 can be referred the effect of inadequate sewage treatment, industrial wastewater flows, and urban runoff discharge, which had been decreased with the decrease in different activities during the epidemic period and lockdown.

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“…Ahmed et al [27] predicted the irrigation water quality index for irrigation purposes in Bangladesh by using ANN and SVR models. M'nassri et al [28] estimated IWQI using ANN and multiple linear regression (MLR) models in Sidi El Hani in Tunisia. Haider et al [29] proposed a hierarchical-based fuzzy technique to address the uncertainties associated with the absence of long observations and inaccurate measurements of groundwater data in Qassim, Saudi Arabia.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Groundwater Quality Index Using Classification Techniques in Arid Environments

Derdour

Abdo²,

Almohamad

et al. 2023

Sustainability

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Assessing water quality is crucial for improving global water resource management, particularly in arid regions. This study aims to assess and monitor the status of groundwater quality based on hydrochemical parameters and by using artificial intelligence (AI) approaches. The irrigation water quality index (IWQI) is predicted by using support vector machine (SVM) and k-nearest neighbors (KNN) classifiers in Matlab’s classification learner toolbox. The classifiers are fed with the following hydrochemical input parameters: sodium adsorption ratio (SAR), electrical conductivity (EC), bicarbonate level (HCO3), chloride concentration (Cl), and sodium concentration (Na). The proposed methods were used to assess the quality of groundwater extracted from the desertic region of Adrar in Algeria. The collected groundwater samples showed that 9.64% of samples were of very good quality, 12.05% were of good quality, 21.08% were satisfactory, and 57.23% were considered unsuitable for irrigation. The IWQI prediction accuracies of the classifiers with the standardized, normalized, and raw data were 100%, 100%, and 90%, respectively. The cubic SVM with the normalized data develops the highest prediction accuracy for training and testing samples (94.2% and 100%, respectively). The findings of this work showed that the multiple regression model and machine learning could effectively assess water quality in desert zones for sustainable water management.

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Estimation of irrigation water quality index in a semi-arid environment using data-driven approach

Cited by 13 publications

References 52 publications

Groundwater Quality Assessment and Irrigation Water Quality Index Prediction Using Machine Learning Algorithms

Groundwater Quality Assessment and Irrigation Water Quality Index Prediction Using Machine Learning Algorithms

Assessing surface water uses by water quality index: application of Qalyubia Governorate, Southeast Nile Delta, Egypt

Prediction of Groundwater Quality Index Using Classification Techniques in Arid Environments

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