@Article{SantosPere:2014:WaDeFo,
               author = "Santos, Cl{\'a}udia Cristina dos and Pereira Filho, Augusto 
                         Jos{\'e}",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Water Demand Forecasting Model for the Metropolitan Area of 
                         S{\~a}o Paulo, Brazil",
              journal = "Water Resources Management",
                 year = "2014",
               volume = "28",
               number = "13",
                pages = "4401--4414",
             keywords = "water consumption, forecasting, artificial neural network, water 
                         supply system, urban system, water resources.",
             abstract = "This work is concerned with forecasting water demand in the 
                         metropolitan area of S{\~a}o 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 CantareiraWTS. 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).",
                  doi = "10.1007/s11269-014-0743-7",
                  url = "http://dx.doi.org/10.1007/s11269-014-0743-7",
                 issn = "0920-4741 and 1573-1650",
                label = "lattes: 4781997141262229 1 SantosPere:2014:WaDeFo",
             language = "en",
           targetfile = "art%3A10.1007%2Fs11269-014-0743-7.pdf",
                  url = "http://link.springer.com/article/10.1007%2Fs11269-014-0743-7",
        urlaccessdate = "28 abr. 2024"
}