Application of neural network and time series modeling to study the suitability of drain water quality for irrigation: a
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RESEARCH ARTICLE
Application of neural network and time series modeling to study the suitability of drain water quality for irrigation: a case study from Egypt Mohamed K. Abdel-Fattah 1 & Ali Mokhtar 2,3 & Ahmed I. Abdo 1,4 Received: 19 May 2020 / Accepted: 16 August 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Limited water resources are one of the major challenges facing Egypt during the current stage. The agricultural drainage water is an important water resource which can be reused for agriculture. Thus, the current study aims to assess the quality of drainage water for irrigation purpose through monitoring and predicting its suitability for irrigation. The chemical composition of Bahr ElBaqr water drain, especially salinity, as well as ions are mainly involved in calculating indicators of water suitability for irrigation, i.e., Ca2+, Mg2+, Na+, K+, HCO−3, Cl−, and SO42−. Further analysis was carried out to evaluate the irrigation water quality index (IWQI) through integrated approaches and artificial neural network (ANN) model. Further, ARIMA models were developed to forecast IWQI of Bahr El-Baqr drain in Egypt. The results indicated that the computed IWQI values ranged between 46 and 81. Around 11% of the samples were classified as excellent water, while 89% of the samples were categorized as good water. The results of IWQI showed a standard deviation of 8.59 with a mean of 62.25, indicating that IWQI varied by 13.79% from the average. ANN model showed much higher prediction accuracy in IWQI modeling with R2 value greater than 0.98 during training, testing and validation. A relatively good correlation was obtained, between the actual and forecasted IWQI based on the Akaike information criterion (AIC); the best fit models were ARIMA (1,0) (0,0) without seasonality. The determination coefficient (R2) of ARIMA models was 0.23. Accordingly, 23% of IWQI variability could be explained by different model parameters. These findings will support the water resources managers and decision-makers to manage the irrigation water resources that can be implemented in the future. Keywords Water resources . IWQI . Artificial neural networks (ANN) . ARIMA . Time series . Egypt
Introduction Responsible editor: Xianliang Yi * Mohamed K. Abdel-Fattah [email protected] * Ali Mokhtar [email protected] 1
Soil Science Department, Faculty of Agriculture, Zagazig University, Zagazig 44511, Egypt
2
State of Key Laboratory of Soil Erosion and Dryland Farming on Loess Plateau, Institute of Soil and Water Conservation, Northwest Agriculture and Forestry University, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China
3
Department of Agricultural Engineering, Faculty of Agriculture, Cairo University, Giza 12613, Egypt
4
College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, Shaanxi, China
Water is one of life’s most important elements and is one of the most abundant materials on surface and the interior of the Earth (Dev a
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