Hybrid Artificial Neural Network-Geostatistics Model for Urban Water Consumption Prediction. A Case Study: Osku City

Abstract

The prediction of water consumption in urban basins is of immense importance for the management of water resources, especially in arid and semiarid countries. The lack of strong predictive tools, or perhaps the lack of experienced users to those tools, may contribute to problems in data interpretation and failure to reach consensus about the need for key water management actions. Therefore, it is extremely important to comprehend the spatiotemporal variations of the water demand for the management of water in such urban areas. In this paper, a hybrid, artificial neural network – geostatistics, model is presented for spatiotemporal prediction of water consumptions. The proposed model contains two individual stages. In the first stage, an artificial neural network is trained for each station for time series modeling of water demands, so that the model can predict the water demands in the next month. At the second stage, the predicted values of water demands at different stations are imposed to a calibrated geostatistics model in order to estimate water demands at any desired point in the city. This methodology is applied for the Osku city, in East Azerbaijan Province, Iran. The most appropriate set of input variables to the model are selected through a combination of domain knowledge and available data series. The results suggested that the hybrid model is a good choice for predicting water demands in the study area.

Keywords

• prediction
• Water Consumption
• Hybrid Model
• Artificial Neural Network
• Geostatistic
• Osku City