Development of Irrigation Water Quality Index Using Artificial Neural Network
Kandil, N., rayan, R., Sadek, M. (2023). Development of Irrigation Water Quality Index Using Artificial Neural Network. EKB Journal Management System, 40(1), 86-101. doi: 10.21608/jsrs.2023.331805
Nema Mohamed Kandil; rafat rayan; Mostafa A. Sadek. "Development of Irrigation Water Quality Index Using Artificial Neural Network". EKB Journal Management System, 40, 1, 2023, 86-101. doi: 10.21608/jsrs.2023.331805
Kandil, N., rayan, R., Sadek, M. (2023). 'Development of Irrigation Water Quality Index Using Artificial Neural Network', EKB Journal Management System, 40(1), pp. 86-101. doi: 10.21608/jsrs.2023.331805
Kandil, N., rayan, R., Sadek, M. Development of Irrigation Water Quality Index Using Artificial Neural Network. EKB Journal Management System, 2023; 40(1): 86-101. doi: 10.21608/jsrs.2023.331805

Development of Irrigation Water Quality Index Using Artificial Neural Network

Journal of Scientific Research in Science
Volume 40, Issue 1, 2023, Page 86-101  XML PDF (1.32 MB)
Document Type: Original Article
DOI: 10.21608/jsrs.2023.331805
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Authors
Nema Mohamed Kandilorcid ; rafat rayan email ; Mostafa A. Sadek
Department of Sitting and Environmental, Nuclear and Radiological Safety Research Centre, Cairo, Egypt. Egyptian Atomic Energy Authority
Abstract
 The data-driven Artificial Intelligence (AI) techniques revealed specific relevance for the treatment of nonlinear relations and predicting the behaviour of complex systems, as a promising application in hydrology and water quality problems. The goal of this study is to build a developed model to forecast the quality of irrigation water by estimating its Water Quality Index using Artificial Neural Network (ANN). The developed model is applied to predict a data-based Irrigation Water Quality Index (IWQI) for groundwater usability in a desert reach pilot area in Egypt. The raw data for the model were the results of the main ion-causing irrigation hazards: (Salinity & Infiltration rate& Specific Toxics and Miscellaneous effects) for seventy-seven groundwater samples. The effectiveness of the model was achieved through the standardized coefficient of input variables. Revealing that the developed ANN model has a high agreement between measured and calculated IWQI (R2= 0.963, RMSE=0.0693) and becomes satisfactory verified for predicting the overall quality of groundwater in the research region, which is based on individual measurements rated according to their sensitivity. Moreover, the newly developed model can overcome the problem of missing some sample index parameters when one or more of the parameters are missing.
Keywords
Irrigation Water Quality Index; Prediction; Artificial Neural Network; regressions; groundwater
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