A multivariate water quality parameter prediction model using recurrent neural network
This addresses water resource management for environmental monitoring, but is incremental as it applies existing methods to a specific domain.
The researchers tackled water quality prediction by developing multivariate LSTM models, achieving errors as low as 0.01 mg/L for single-step and 0.227 mg/L RMSE for multiple-step predictions.
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.