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

• Published in: Water resources management Vol. 28; no. 13; pp. 4401 - 4414
• Main Authors: dos Santos, Cláudia Cristina, Pereira Filho, Augusto José
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01-10-2014; Springer; Springer Nature B.V
• Subjects: Atmospheric Sciences; Brazil; Civil Engineering; Decision making; Demand; Earth and Environmental Science; Earth Sciences; Earth, ocean, space; Environment; Exact sciences and technology; Forecasting; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Hydrology; Hydrology. Hydrogeology; Hydrology/Water Resources; Learning theory; Mathematical models; Metropolitan areas; Neural networks; Rain; Reservoirs; Sanitation; Stochastic models; Studies; Time series; Trends; Urban environments; Variables; Water consumption; Water demand; Water mains; Water resources; Water resources management; Water shortages; Water supply; Water supply engineering; Water treatment; Weather forecasting; South America; Brazil; Sao Paulo Brazil; Brazil, Sao Paulo; Water consumption; Water supply system; Urban system; Artificial neural network; Forecasting; Water resources; models; urban environment; neural networks; water supply; consumption; climate; urban areas; surface water; water resources; water treatment; noise; water resource management; errors; methodology
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-014-0743-7

This work is concerned with forecasting water demand in the metropolitan area of São 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).