A Hybrid Deep Stacked LSTM and GRU for Water Price Prediction

Abstract

Water pricing and freshwater scarcity is an emerging global issue, a topic of debate among researchers, households and water utility managers. This is due to the fact that, the process can provide early warning signs as well as assisting water utility managers to make proper decisions on control and management of the scarce water resources through implementing water pricing policies, ensuring proper water allocation, water-use restriction as well as water production. In this paper, we presented a two-step methodology coupled stacked LSTM+GRU models while analyzing their relative performance to our reference models i.e. stacked LSTM and GRU for long term water price Prediction. It is thought that, the coupled Stacked LSTM and GRU models to exploit building of higher level of representation of the input sequence data while creating a higher level of abstraction on the final results. The GRU on the other hand assists in solving the vanishing gradient problems. The experimental results obtained from this research work indicates our coupled (Stacked LSTM+GRU) with supervised learning to significantly outperform our reference models for water price Prediction.