An Artificial Neural Network Model for Water Quality Prediction in the Amoju Hydrographic Subbasin, Cajamarca-Peru

Abstract

Water quality is crucial for sustaining life, and accurate prediction models are essential for effective management. This study introduces an Artificial Neural Network (ANN) model designed to predict the Water Quality Index (WQI) in the Amoju Hydrographic Subbasin, Cajamarca-Peru. The model was developed using key water quality parameters, including electrical conductivity (EC), total dissolved solids (TDS), calcium carbonate (CaCO3), and phosphate (PO3-4), identified through Pearson correlation analysis. Data from water samples collected over six months were used to train and validate the model. Results revealed that the ANN model achieved high predictive accuracy, with a significant correlation between WQI and the aforementioned parameters. The model's performance outstrips traditional methods demonstrating its capability to effectively capture complex interdependencies among water quality indicators. This research emphasizes the potential of AI-driven approaches for enhancing predictive accuracy in environmental monitoring. Future studies should consider incorporating additional variables, such as heavy metals and microbial indicators, and consider the application of real-time AI-driven monitoring systems to further refine water quality management strategies. The ANN model presented here offers a promising tool for decision-makers, providing a reliable method for predicting water quality in similar hydrographic basins and contributing to the broader field of AI in environmental science.