Prediction of irrigation water suitability using geospatial computing approach: a case study of Agartala city, India

Mallik, Santanu; Chakraborty, Abhigyan; Mishra, Umesh; Paul, Niladri

doi:10.1007/s11356-022-21232-8

Cited by 6 publications

(2 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The quality of irrigation water and its suitability for various crops are assessed using irrigation water quality indices (Mallik et al, 2022). There are several different irrigation water indicators; however, in this research, four indices are used for the management of irrigation water, including sodium adsorption ratio (SAR), sodium percentage (Na%), permeability index (PI), and potential salinity (PS).…”

Section: Irrigation Indicesmentioning

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.

View full text Add to dashboard Cite

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.

show abstract

Section: Irrigation Indicesmentioning

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.

View full text Add to dashboard Cite

show abstract

“…As part of the "zero thirsty" program administered by the Sudanese government, the Khartoum State Water Corporation collected 37 groundwater samples between October 2018 and December 2020. The groundwater samples were collected from public and privately owned groundwater wells with a depth ranging from 100 to 250 m [18]. The groundwater samples were analyzed in the labs of Groundwater and Wadies Directorate for ten physiochemical parameters.…”

Section: Groundwater Samplingmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Water indices for surface water extraction using geospatial techniques: a brief review

Purnam,

Prasad,

Ganasala

2024

Sustain. Water Resour. Manag.

View full text Add to dashboard Cite

Prediction of irrigation water suitability using geospatial computing approach: a case study of Agartala city, India

Cited by 6 publications

References 60 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

Water indices for surface water extraction using geospatial techniques: a brief review

Contact Info

Product

Resources

About