Time and Space Domain Prediction of Water Quality Parameters of Bagmati River Using Deep Learning Methods

Abstract

Bagmati river is biologically, geologically, religiously and historically significant among the river systems of the Kathmandu Valley. The river is affected by five major tributaries, including Manohara, Dhobi Khola, Tukucha, Bishnumati, and Balkhu Khola, which significantly impact the water chemistry inside the Kathmandu Valley. The data of water quality parameters pH, dissolved oxygen, turbidity, temperature, oxygen reduction potential, conductivity, total dissolved solids, salinity among others was collected using fixed sensors (in period of 5 seconds) and mobile sensors (with latitude and longitude) along the river. The observation is important for two reasons, one because it was collected in real-time and fine scale, which is not normally possible with traditional ways, and next such observation was done for the first time in Bagmati River. The aim of this study was to predict water quality parameters of the Bagmati River using machine learning time series models, specifically ARIMA and LSTM. The LSTM model was designed with one input layer, one encoder layer, one repeat layer, one decoder layer, and one output dense layer to separate the output into temporal slices. Additionally, a DNN model was employed for location-based prediction, utilizing two input layers for latitude and longitude and seven output layers for the seven water quality parameters considered for study. The models demonstrated promising performance, but further data collection and parameter variation are recommended for continued optimization.