Some of the benefits of AI models include their nonlinear structure, capacity to anticipate complicated events, handling large datasets at diverse sizes, and handling missing data. Therefore, attempts are being made to develop a non-physical approach based on artificial intelligence (AI) to predict the water quality index (Gaya et al., 2020 Lu and Ma, 2020).ĪI-based modelling is a useful tool for rapid prediction of the water quality indices.

These indices include soluble sodium percentage (SSP) (Todd and Mays, 2004), sodium adsorption ratio (SAR) (Ayers and Westcot, 1985), residual sodium carbonate (RSC) (Richards, 1954a), potential of salinity (PS) (Doneen, 1964), permeability index (PI) (Doneen, 1964 Gholami and Srikantaswamy, 2009) and Kelley’s ratio (KR) (Kelley, 1963). A variety of metrics are used to assess the quality of irrigation water given by several organization and agencies (El Bilali and Taleb, 2020). The reuse of this drainage water without proper treatment may have negative impacts on the soil, crop, and irrigation system. However, there are some concerns about the quality of the reuse water. Due to Egypt's limited water supplies, agricultural drainage water must be reused for irrigation (Abdel-Fattah and Helmy, 2015 Abdel-Fattah et al., 2020). The most promising approach for increasing irrigation water availability is agricultural drainage water reuse (Assar et al., 2020). Unless proper strategies are put in place, the use of poor quality irrigation water could lead to problems with soil salinity, irritation rate decline, plant growth toxicity, and other associated problems (Ayers and Westcot, 1985). This means that new irrigation projects, as well as the existing looking for additional or supplemental supplies, may have to rely on low-quality salt-laden water from unfavourable sources (Pande and Moharir, 2018). Nowadays, water quality is becoming an issue because of the intensive and competitive use of water. Water quality issues were frequently overlooked in the past due to widely available high-quality water supplies (Ayers and Westcot, 1985). Irrigated agriculture needs an adequate supply of usable water. This is particularly so for arid and semi-arid areas, like Egypt that depends on irrigated agriculture (Elbeltagi et al., 2021a, 2021b, 2020a, 2021c, 2021d, 2020b Moharir et al., 2019). Thus, water shortage is a global concern and is going to get worse as indicated by climate change projections (Pleguezuelo et al., 2018). As a result, improved water resource quality significantly reduces the cost of water treatment for irrigation and boosts agricultural yield (Kouadri et al., 2021a, 2021b). Water resources are critical in the drinking, industrial, and agricultural sectors. 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. Based on the classification of the SI, all models applied having values less than 0.1 indicate good prediction performance for all the indices except RSC. 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. A good soil drainage system and salinity control measures are required to avoid salt accumulation within the soil. The results indicated the water source is not suitable for irrigation without treatment. Electrical conductivity (EC), sodium (Na +), calcium (Ca 2+) and bicarbonate (HCO 3−) were used as input exploratory variables for the models. To achieve this goal, three artificial 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). The objective of this work was to predict the irrigation water quality index of the Bahr El-Baqr, Egypt, based on non-expensive approaches that requires simple parameters. Assessing irrigation water quality is one of the most critical challenges in improving water resource management strategies.

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