Industrializations and population growth are the key factors for water deficiency, without forgetting other anthropogenic activities such as agriculture, mining and fishing can result in water quality deterioration.
(Ömer Faruk 2010) States that Mankind abstracts water from the hydrologic cycle for their essential economic usage and returns it to the corresponding cycle after using it. Materials that mix with water throughout the cycle can generate a notion of water pollution since they can change the chemical, physical and biological properties of water after natural purifications (Maier, Morgan, and Chow 2004), So to understand the change of these properties water quality assessment and forecasting should be done for comprehending the state of water and its suitability to serve a certain intention, for example, drinking or Irrigation. So far large amounts of domestic and industrial waste continue to be directed to the water sources (Palmer 2001), hence due to that water quality for irrigation might be deteriorated and plant productivity of any region can be affected.
In the present study assessment of irrigation water quality is done using a combination of back-propagation learning neural network algorithm and the United States salinity laboratory method (USSL). USSL method normally considers two parameters namely Sodium Absorption ratio (SAR) and Electrical conductivity (EC), the SAR can be estimated using Sodium, Calcium, and Magnesium concentrations present in water for irrigation(Weiner 2008). A higher amount of Sodium reduces the infiltration rate of the soil, decrease soil stability, and increases the Sodium accumulation in the leaf tissue (Micke 1996), surplus content of SAR can affect the leaf of the plants like avocado, stone fruits, and almond (Bouwer and Idelovitch 1987), soil permeability can also be affected by a higher amount of SAR. Electrical conductivity (EC) is defined as the capacity of water to transmit electrical current. It is directly proportional to the dissolved ions in the water and their charge, EC can affect crop growth directly by toxicity or deficiency, or indirectly way through changing plant availability of nutrients (Goher et al. 2014). The combined techniques of Irrigation water quality index (IWQI) and Artificial neural network (ANN) could be used in assessing the state of water for Irrigation purposes because these techniques can convert the complex data that have to be performed into an understandable way for the policymakers and public can be simply informed on the quality of water. In present years various models including traditional mechanistic applications have been involved so that water quality can be managed. Several of these models requires different input data which cannot be easily available and make it an extremely expensive and time-consuming process ANN is the appropriate approach for water quality modeling (Banejad and Olyaie 2011).
While there is much research on the development of models in the prediction of sodium absorption ratio, and electrical conductivity such as (Singh 2020),(“Evaluation of Soil Salinity of the Fetzara Lake Region (North-East Algeria) *” 2022),(Sciences 2021) but there is little research on the deployment of the models using a graphical user interface (App), that’s why this study is more potential and necessary.
The main objective of the present work is to develop and deploy an artificial neural network (ANN) model using a graphical user interface (App) to understand the classification of Sodium absorption ratio (SAR) and Electrical conductivity (EC) in water for irrigation of Oued-Hammam watershed, so that it can be used to predict and real-time monitoring of water quality and hence soil and crop production improvement.