Water Demand Forecasting Model for the Metropolitan Area of São Paulo, Brazil

This work is concerned with forecasting water demand in the metropolitan area of Sao Paulo (MASP) through water consumption, meteorological and socio-environmental variables using an Artificial Neural Network (ANN) system. Possible socio-environmental and meteorological conditions affecting water consumption at Cantareira water treatment station (WTS) in the MASP, Brazil were analyzed for the year 2005. Eight model configurations were developed and used for the Cantareira WTS. The best performance was obtained for 12-h average of the input variables. The ANN model performed best with three times steps in advance. The hourly forecasting was obtained with acceptable error levels. Model results indicate an overall tendency for small errors. The proposed method is useful tool for water demand forecasting and water systems management. The paper is an important contribution since it takes into account weather variables and introduces some diagnostic studies on water consumption in one of the largest urban environments of the planet with its unique peculiarities such as anthropic affects on weather and climate that feeds back into the water consumption. The averaging is a low pass filter indeed and we used it to improve Signal to Noise Ratio (SNR).