Investigating effect of socio-economic and climatic variables in urban water consumption prediction via Gaussian process regression approach

Due to the importance of forecasting urban water demand (WD), the present study investigated the capability of Gaussian process regression (GPR) using different features as input data. WD could be represented as a function of various variables such as climatic, socioeconomic, institutional and management factors to understand it better. Therefore, in the present study, GPR was used to predict daily WD by taking advantage of several socio-economic and climatic variables for Hamadan city, located in the west of Iran. The selected variables were comprised of daily weather data such as rainfall (R), maximum temperature (T_max), mean temperature (T_mean), min temperature (T_min) and relative humidity, Socioeconomic variables such as average monthly water bill (MWB), population (P), number of households (NH), gross national product, and inflation rate (I). The results indicated that GPR could predict the daily WD accurately in terms of statistical evaluations criteria. Sensitivity analysis among various climatic and socio-economic data showed the best input structure in water consumption prediction via GPR. Accordingly, the results approved the substantial impression of WD with three day-lag, I, MWB and T_max as the input dataset.