WaterQualityNeT: Prediction of Seasonal Water Quality of Nepal Using Hybrid Deep Learning Models

Abstract

Ensuring a safe and uncontaminated water supply is contingent upon the monitoring of water quality, especially in developing countries such as Nepal, where water sources are susceptible to pollution. This paper presents a hybrid deep learning model for predicting Nepal's seasonal water quality using a small dataset with many water quality parameters. The model integrates convolutional neural networks (CNN) and recurrent neural networks (RNN) to exploit temporal and spatial patterns in the data. The results demonstrate significant improvements in forecast accuracy over traditional methods, providing a reliable tool for proactive control of water quality. The model that used WQI parameters to classify people into good, poor, and average groups performed 92% of the time in testing. Similarly, the R2 score was 0.97 and the root mean square error was 2.87 when predicting WQI values using regression analysis. Additionally, a multifunctional application that uses both a regression and a classification approach is built to predict WQI values.