Integrated Spectral Reflectance Band Recognition and Artificial Neural Network Intelligence Model for monitoring water quality

Abstract Due to high costs and site accessibility, it is sometimes impractical to monitor water quality indicators in waterbodies or isolated watersheds. An approach that utilizes remote sensing and is cost-effective was created to forecast water quality metrics across a vast and logistically challenging region. In order to monitor water quality parameters for the Euphrates River in Ramadi, Anbar Governorate, Iraq, this study integrated survey data and remote sensing with artificial neural networks (ANNs). Water quality parameters, such as pH, alkalinity (ALK), orthophosphate (PO4), nitrate (NO3), sulfate (SO4), chloride (Cl), total hardness (TH), calcium (Ca), magnesium (Mg), total suspended solid (TSS), temperature, turbidity, and electrical conductivity (EC), during the wet and dry seasons were all quantified using a neural network model. The outcomes show that a good link between the simulated and observed water quality metrics was provided by the neural network model that was created. Calculations were made to show how beneficial this approach is for predicting water quality parameters in complicated ecosystems. These calculations included the quantities of chemical and physical parameters that were stored in the column of water.