Assessing data mining algorithms to predict the quality of groundwater resources for determining irrigation hazard

Masoudi, Reza; Mousavi, Seyed Roohollah; Rahimabadi, Pouyan Dehghan; Panahi, Mehdi; Rahmani, Asghar

doi:10.1007/s10661-022-10909-9

Cited by 7 publications

(2 citation statements)

References 44 publications

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“…However, the calculation of these indices is often lengthy and timeconsuming (Nouraki et al, 2021); therefore, AI techniques are proposed to reduce the calculation time and avoid calculation errors (Nabiollahi et al, 2021). Because AI models can evaluate vast amounts of data and produce precise forecasts, their usage in irrigation water management has increased recently (Yu et al, 2022;Masoudi et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Application of GIS-based machine learning algorithms for prediction of irrigational groundwater quality indices

Mohammed,

Kaya,

Mohamed

et al. 2023

Front. Earth Sci.

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Agriculture is considered one of the primary elements for socioeconomic stability in most parts of Sudan. Consequently, the irrigation water should be properly managed to achieve sustainable crop yield and soil fertility. This research aims to predict the irrigation indices of sodium adsorption ratio (SAR), sodium percentage (Na%), permeability index (PI), and potential salinity (PS) using innovative machine learning (ML) techniques, including K-nearest neighbor (KNN), random forest (RF), support vector regression (SVR), and Gaussian process regression (GPR). Thirty-seven groundwater samples are collected and analyzed for twelve physiochemical parameters (TDS, pH, EC, TH, Ca+2, Mg+2, Na+, HCO3−, Cl, SO4−2, and NO3−) to assess the hydrochemical characteristics of groundwater and its suitability for irrigation purposes. The primary investigation indicated that the samples are dominated by Ca-Mg-HCO3 and Na-HCO3 water types resulted from groundwater recharge and ion exchange reactions. The observed irrigation indices of SAR, Na%, PI, and PS showed average values of 7, 42.5%, 64.7%, and 0.5, respectively. The ML modeling is based on the ion’s concentration as input and the observed values of the indices as output. The data is divided into two sets for training (70%) and validation (30%), and the models are validated using a 10-fold cross-validation technique. The models are tested with three statistical criteria, including mean square error (MSE), root means square error (RMSE), and correlation coefficient (R2). The SVR algorithm showed the best performance in predicting the irrigation indices, with the lowest RMSE value of 1.45 for SAR. The RMSE values for the other indices, Na%, PI, and PS, were 6.70, 7.10, and 0.55, respectively. The models were applied to digital predictive data in the Nile River area of Khartoum state, and the uncertainty of the maps was estimated by running the models 10 times iteratively. The standard deviation maps were generated to assess the model’s sensitivity to the data, and the uncertainty of the model can be used to identify areas where a denser sampling is needed to improve the accuracy of the irrigation indices estimates.

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Section: Introductionmentioning

confidence: 99%

Application of GIS-based machine learning algorithms for prediction of irrigational groundwater quality indices

Mohammed,

Kaya,

Mohamed

et al. 2023

Front. Earth Sci.

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“…However, the calculation of these indices is often lengthy and time-consuming [4]; therefore, Artificial Intelligence (AI) techniques are proposed to reduce the calculation time and avoid calculation errors. The use of AI models in irrigation water management has been growing in recent years due to their ability to analyze large amounts of data and make accurate predictions [5][6][7]. For instance, [8] used support vector regression (SVR) and random forest (RF) to model the irrigation water quality of potential salinity, sodium percentage and permeability index in Bahr El-Baqr, Egypt.…”

Section: Introductionmentioning

confidence: 99%

Management of Agricultural Groundwater in Sudan: The Use of Artificial Intelligence Algorithms in Khartoum State

Mohammed,

Abba,

Szabó

et al. 2023

Geosciences and Engineering

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This research aims to predict the irrigation indices of sodium adsorption ratio (SAR) and sodium percentage (Na%) using innovative machine learning (ML) techniques, including support vector regression (SVR) and Gaussian process regression (GPR). Thirty-seven groundwater samples were collected, and the primary investigation indicated that Ca-Mg-HCO3 and Na-HCO3 water types dominate the samples. The data is divided into two sets for training (70%) and validation (30%), and the models are tested with three statistical criteria, including mean square error (MSE), root mean square error (RMSE), and determination coefficient (R2). The GPR algorithm showed better performance in predicting SAR and Na% than SVR since it provided the lowest errors. The implemented approach proved efficient for the sustainable management of agricultural water.

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Analyses of water dependency of Haloxylon ammodendron in arid regions of Iran using stable isotope technique

Dehghan Rahimabadi,

Azarnivand,

Faghihi

et al. 2024

Environ Monit Assess

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Assessing data mining algorithms to predict the quality of groundwater resources for determining irrigation hazard

Cited by 7 publications

References 44 publications

Application of GIS-based machine learning algorithms for prediction of irrigational groundwater quality indices

Application of GIS-based machine learning algorithms for prediction of irrigational groundwater quality indices

Management of Agricultural Groundwater in Sudan: The Use of Artificial Intelligence Algorithms in Khartoum State

Analyses of water dependency of Haloxylon ammodendron in arid regions of Iran using stable isotope technique

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