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Applied Water Science
Springer
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Abstract: |
Assessing irrigation water quality is one of the most critical challenges in improving water resource management strategies. The objective of this work was to predict the irrigation water quality index of the Bahr El-Baqr, Egypt, based on nonexpensive approaches that requires simple parameters. To achieve this goal, three artifcial intelligence (AI) models (Support
vector machine, SVM; extreme gradient boosting, XGB; Random Forest, RF) and four multiple regression models (Stepwise
Regression, SW; Principal Components Regression, PCR; Partial least squares regression, PLS; Ordinary least squares
regression, OLS) were applied and validated for predicting six irrigation water quality criteria (soluble sodium percentage, SSP; sodium adsorption ratio, SAR; residual sodium carbonate, RSC; potential of salinity, PS; permeability index, PI;
Kelly’s ratio, KR). Electrical conductivity (EC), sodium (Na+), calcium (Ca2+) and bicarbonate (HCO3−) were used as input
exploratory variables for the models. The results indicated the water source is not suitable for irrigation without treatment.
A good soil drainage system and salinity control measures are required to avoid salt accumulation within the soil. Based on
the performance statistics of the root mean square error (RMSE) and the scatter index (SI), SW emerged as the best (0.21%
and 0.03%) followed by PCR and PLS with RMSE 0.22% and 0.21% for SAR, respectively. Based on the classifcation of
the SI, all models applied having values less than 0.1 indicate good prediction performance for all the indices except RSC.
These results highlight potential of using multiple regressions and the developed machine learning methods in predicting
the index of irrigation water quality, and can be rapid decision tools for modelling irrigation water quality.
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