Prediction of Water Quality using Artificial Neural Network

Abstract

Water quality prediction plays a significant role in the management and planning of water resources. It is critical for identifying the status of water bodies, obtaining information regarding the implementation of specified interventions, and assessing the effectiveness of implemented measures. Additionally, increase in urbanisation, construction, agricultural operations, industrial uses, and natural processes have harmed the quality of surface water and groundwater around the world, as well as its consequences on human health. Utilizing the water quality characteristics as input, an artificial neural network (ANN) technique is used to forecast water quality metrics. To find the best predictions, different input combinations and performance criteria are attempted. The results show that the measured and calculated parameters have low error and strong correlation values. These findings suggest that ANNs have a lot of potential for predicting water quality metrics.