Performance comparison of techniques for water demand forecasting

Abstract

There is an ever growing demand of water due to the factors like global warming, urbanization and population growth. The situation demands to use more efficient planning which can be attained by technological advancement like Internet of things and smart systems. The cost related to water management system can be optimized by using prediction. The future demand for water could be better modeled with forecasting techniques. A collection of techniques (Artificial Neural Network (ANN), Deep Neural Network (DNN), Extreme Learning Machines (ELM), Least Square Support Vector Machine (LSSVM), Gaussian process regression (GPR), Random Forest (RF), multiple regression have been applied to analyze the performance in water demand forecasting using the common evaluation criteria. The work is aimed at short term prediction using hourly and daily intervals. A good performance was obtained through the ANN model for all short term predictions.