A multivariate water quality parameter prediction model using recurrent neural network

TL;DR

This paper presents a multivariate water quality prediction model using LSTM neural networks, demonstrating high accuracy in forecasting water parameters to aid water resource management.

Contribution

It introduces a specialized LSTM-based model for water quality prediction using multivariate historical data, with improved accuracy over previous methods.

Findings

Single step model error of 0.01 mg/L

Multiple step model RMSE of 0.227 mg/L

Effective for timely water quality assessment

Abstract

The global degradation of water resources is a matter of great concern, especially for the survival of humanity. The effective monitoring and management of existing water resources is necessary to achieve and maintain optimal water quality. The prediction of the quality of water resources will aid in the timely identification of possible problem areas and thus increase the efficiency of water management. The purpose of this research is to develop a water quality prediction model based on water quality parameters through the application of a specialised recurrent neural network (RNN), Long Short-Term Memory (LSTM) and the use of historical water quality data over several years. Both multivariate single and multiple step LSTM models were developed, using a Rectified Linear Unit (ReLU) activation function and a Root Mean Square Propagation (RMSprop) optimiser was developed. The single step model attained an error of 0.01 mg/L, whilst the multiple step model achieved a Root Mean Squared Error (RMSE) of 0.227 mg/L.