## Author Contributions

Methodology, A.D., H.G.A. and H.A.; writing—original draft preparation, A.D., S.S.M.G., E.A., A.A., A.A.A.D. and H.G.A.; writing—review and editing, A.D., S.S.M.G., H.A., A.A., E.A. and H.G.A. All authors have read and agreed to the published version of the manuscript.

Figure 2.

SVM algorithm indicates the margin separating two classes.

Figure 2.

SVM algorithm indicates the margin separating two classes.

Figure 3.

KNN classifier based on k-parts where the red hexagon and the green triangle represent class 1 and 2, respectively.

Figure 3.

KNN classifier based on k-parts where the red hexagon and the green triangle represent class 1 and 2, respectively.

Figure 4.

Geospatial distribution of IWQI in the study area.

Figure 4.

Geospatial distribution of IWQI in the study area.

Figure 5.

Scatter plot of cubic SVM with standardized data.

Figure 5.

Scatter plot of cubic SVM with standardized data.

Figure 6.

Confusion matrices: (a) the number of correct and incorrect observations, (b) true positive rate/false negative rate for cubic SVM based on standardized data, where the green and red cells refer to the correct and incorrect prediction, respectively.

Figure 6.

Confusion matrices: (a) the number of correct and incorrect observations, (b) true positive rate/false negative rate for cubic SVM based on standardized data, where the green and red cells refer to the correct and incorrect prediction, respectively.

Figure 7.

The ROC result of cubic SVM for standardized data.

Figure 7.

The ROC result of cubic SVM for standardized data.

Figure 8.

Confusion matrices: (a) the number of correct and incorrect observations, (b) normalized cubic SVM true positive rate/false negative rate, where the green and red cells refer to the correct and incorrect prediction, respectively.

Figure 8.

Confusion matrices: (a) the number of correct and incorrect observations, (b) normalized cubic SVM true positive rate/false negative rate, where the green and red cells refer to the correct and incorrect prediction, respectively.

Figure 9.

The ROC result of cubic SVM for normalized data.

Figure 9.

The ROC result of cubic SVM for normalized data.

Figure 10.

Scatter plot of linear SVM with raw data.

Figure 10.

Scatter plot of linear SVM with raw data.

Figure 11.

Confusion matrices: (a) the number of correct and incorrect observations, (b) linear SVM true positives/false negatives based on raw data, where the green and red cells refer to the correct and incorrect prediction, respectively.

Figure 11.

Confusion matrices: (a) the number of correct and incorrect observations, (b) linear SVM true positives/false negatives based on raw data, where the green and red cells refer to the correct and incorrect prediction, respectively.

Figure 12.

The confusion matrix of KNN with normalized data.

Figure 12.

The confusion matrix of KNN with normalized data.

Figure 13.

KNN confusion matrix for positive predictive values and negative predictive values. The green-colored cells represent the correct prediction while the red-colored cells represent incorrect prediction.

Figure 13.

KNN confusion matrix for positive predictive values and negative predictive values. The green-colored cells represent the correct prediction while the red-colored cells represent incorrect prediction.

Table 1.

Descriptive statistics of physicochemical parameters of irrigation water.

Table 1.

Descriptive statistics of physicochemical parameters of irrigation water.

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https://www.mdpi.com/2071-1050/15/12/9687

? Read More Sustainability & LEED ?…] [CrossRef] Asadi, E.; Isazadeh, M.; Samadianfard, S.; Ramli, M.F.; Mosavi, A.; Nabipour, N.; Shamshirband, S.; Hajnal, E.; Chau, K.-W. Groundwater quality assessment for sustainable drinking and irrigation. Sustainability 2019, 12, 177. [Google Scholar] [CrossRef] Hamdi, L.; Defaflia, N.; Merghadi, A.; Fehdi, C.; Yunus, A.P.; Dou, J.; Pham…. Groundwater resources sustainability: Past, present, and future. Water Resour. Manag. 2013, 27, 4409–4424. [Google Scholar] [CrossRef] Oweis, T.; Hachum, A. Water harvesting and supplemental irrigation for improved water productivity of dry farming systems in West Asia and North Africa. Agric. Water Manag. 2006, 80, 57–73. [Google Scholar] [CrossRef… mdpi.com Total Engagement: 0

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