O presente trabalho apresenta a previsão de demanda de água em sistemas urbanos de abastecimento através de Rede Neural Artificial (RNA) utilizando dados de consumo de água e variáveis meteorológicas e socioambientais. A RNA utilizada foi uma de três camadas chamada de rede de múltiplas camadas alimentadas adiante com o algoritmo de treinamento LLSSIM (Hsu et al., 1996). Neste estudo, foram utilizados os dados de consumo de água (SABESP) e meteorológicos (IAG/USP) para o período de 2001 a 2005 para Região Metropolitana de São Paulo (RMSP). As variáveis socioambientais e meteorológicas que podem afetar o consumo de água foram analisadas. A ETA Cantareira e o setor Itaim Paulista foram utilizados para avaliar a relação entre o consumo e as variáveis antrópicas e meteorológicas para o ano de 2005. Esses conjuntos de dados foram utilizados para o treinamento, o teste e a previsão da RNA. Para a ETA Cantareira, foram criados 8 modelos e para o setor Itaim Paulista 57, sendo que os modelos 9 a 57 correspondem à previsão ideal. O desempenho dos modelos foi avaliado pelo o erro médio, erro médio absoluto, erro médio quadrático, o coeficiente de correlação, exatidão, viés, POD, FAR, CSI e POFD. Para a ETA Cantareira o melhor desempenho ocorreu para a média de 12 horas e para o Itaim Paulista a média de 6 horas. Na previsão ideal observou-se que a memória do sistema é um fator importante, principalmente quando se tem dois intervalos de tempo anterior. Os resultados mostraram a importância da memória, pois ela ajuda a melhorar o desempenho da previsão A previsão horária foi obtida com níveis de erros aceitáveis. Comparando os resultados de todas as configurações dos modelos, observou-se que há uma tendência para pequenos erros. Finalmente, conclui-se que o método proposto pode ser utilizado para previsão de consumo obtendo uma boa previsão. / This work is concerned with the prediction of water demand in urban water supply systems using water consumption, meteorological and socioenvironmental variables in an Artificial Neural Network (ANN) system. The ANN is a three layer feed-forward network with the LLSSIM training algorithm (Hsu et. al., 1996). In this study, water consumption (SABESP) and meteorological (IAG USP) data sets between 2001 and 2005 were used for studying the Metropolitan Area São Paulo (MASP). Possible socio-environmental and meteorological conditions affecting water consumption in the MASP were analyzed. Two water treatment stations (ETA), namely, Cantareira and the Itaim Paulista were used to evaluate the relationship between water consumption against anthropic and meteorological conditions for the year 2005. These data sets were also used for training, testing and forecasting of the water consumption model with the ANN. For the Cantareira ETA, 8 model configurations were tested and 57 for the Itaim Paulista ETA. In this late case, configurations 9 to 57 were for ideal forecasts. The various model configurations were evaluated by the mean error, mean absolute error and mean square root error, correlation coefficient, bias, POD, FAR, CSI e POFD. The best performance for the Cantareira ETA was obtained for a 12-hour average of the input variables, and for the Itaim Paulista ETA, for the 6-hour average. The ANN model configurations fed with variables of previous three times steps (memory) performed best, followed by two previous time steps. The results indicate the importance of these memory to improving the performance of the forecasting. The hourly forecasting was obtained with acceptable error levels. Comparing the results of all model configurations, there is an overall tendency for minor errors. The proposed method can be used to demand forecast a good prediction.

O presente trabalho apresenta a previsão de demanda de água em sistemas urbanos de abastecimento através de Rede Neural Artificial (RNA) utilizando dados de consumo de água e variáveis meteorológicas e socioambientais. A RNA utilizada foi uma de três camadas chamada de rede de múltiplas camadas alimentadas adiante com o algoritmo de treinamento LLSSIM (Hsu et al., 1996). Neste estudo, foram utilizados os dados de consumo de água (SABESP) e meteorológicos (IAG/USP) para o período de 2001 a 2005 para Região Metropolitana de São Paulo (RMSP). As variáveis socioambientais e meteorológicas que podem afetar o consumo de água foram analisadas. A ETA Cantareira e o setor Itaim Paulista foram utilizados para avaliar a relação entre o consumo e as variáveis antrópicas e meteorológicas para o ano de 2005. Esses conjuntos de dados foram utilizados para o treinamento, o teste e a previsão da RNA. Para a ETA Cantareira, foram criados 8 modelos e para o setor Itaim Paulista 57, sendo que os modelos 9 a 57 correspondem à previsão ideal. O desempenho dos modelos foi avaliado pelo o erro médio, erro médio absoluto, erro médio quadrático, o coeficiente de correlação, exatidão, viés, POD, FAR, CSI e POFD. Para a ETA Cantareira o melhor desempenho ocorreu para a média de 12 horas e para o Itaim Paulista a média de 6 horas. Na previsão ideal observou-se que a memória do sistema é um fator importante, principalmente quando se tem dois intervalos de tempo anterior. Os resultados mostraram a importância da memória, pois ela ajuda a melhorar o desempenho da previsão A previsão horária foi obtida com níveis de erros aceitáveis. Comparando os resultados de todas as configurações dos modelos, observou-se que há uma tendência para pequenos erros. Finalmente, conclui-se que o método proposto pode ser utilizado para previsão de consumo obtendo uma boa previsão. / This work is concerned with the prediction of water demand in urban water supply systems using water consumption, meteorological and socioenvironmental variables in an Artificial Neural Network (ANN) system. The ANN is a three layer feed-forward network with the LLSSIM training algorithm (Hsu et. al., 1996). In this study, water consumption (SABESP) and meteorological (IAG USP) data sets between 2001 and 2005 were used for studying the Metropolitan Area São Paulo (MASP). Possible socio-environmental and meteorological conditions affecting water consumption in the MASP were analyzed. Two water treatment stations (ETA), namely, Cantareira and the Itaim Paulista were used to evaluate the relationship between water consumption against anthropic and meteorological conditions for the year 2005. These data sets were also used for training, testing and forecasting of the water consumption model with the ANN. For the Cantareira ETA, 8 model configurations were tested and 57 for the Itaim Paulista ETA. In this late case, configurations 9 to 57 were for ideal forecasts. The various model configurations were evaluated by the mean error, mean absolute error and mean square root error, correlation coefficient, bias, POD, FAR, CSI e POFD. The best performance for the Cantareira ETA was obtained for a 12-hour average of the input variables, and for the Itaim Paulista ETA, for the 6-hour average. The ANN model configurations fed with variables of previous three times steps (memory) performed best, followed by two previous time steps. The results indicate the importance of these memory to improving the performance of the forecasting. The hourly forecasting was obtained with acceptable error levels. Comparing the results of all model configurations, there is an overall tendency for minor errors. The proposed method can be used to demand forecast a good prediction.

Wong, Jefferson See. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 143-151). / Abstracts in English and Chinese. / LIST OF FIGURES --- p.i / LIST OF TABLES --- p.iv / Chapter CHAPTER ONE --- INTRODUCTION --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- Research Background --- p.1 / Chapter 1.3 --- Study Area --- p.8 / Chapter 1.3.1 --- Geographical setting --- p.8 / Chapter 1.3.2 --- Climate --- p.9 / Chapter 1.3.3 --- Water demand and supply in Hong Kong --- p.10 / Chapter 1.4 --- Objectives of the Study --- p.16 / Chapter 1.5 --- Significance of the Study --- p.16 / Chapter 1.6 --- Outline of Thesis --- p.17 / Chapter CHAPTER TWO --- LITERATURE REVIEW --- p.18 / Chapter 2.1 --- Introduction --- p.18 / Chapter 2.2 --- Concept of Urban Water Consumption / Water Use --- p.19 / Chapter 2.3 --- Urban Water Consumption Patterns --- p.19 / Chapter 2.4 --- Factors Influencing Urban Water Consumption --- p.22 / Chapter 2.5 --- Model Formulation of Urban Water Consumption --- p.29 / Chapter 2.6 --- Methods of Forecasting Urban Water Consumption --- p.37 / Chapter 2.7 --- Conclusion --- p.42 / Chapter CHAPTER THREE --- DATA AND METHODOLOGY --- p.44 / Chapter 3.1 --- Introduction --- p.44 / Chapter 3.2 --- Water Consumption and Climatic Data --- p.44 / Chapter 3.3 --- Modeling Framework and Procedure --- p.49 / Chapter 3.4 --- Base Water Use --- p.52 / Chapter 3.4.1 --- Long-term trend --- p.52 / Chapter 3.5 --- Seasonal Water Use --- p.53 / Chapter 3.5.1 --- Seasonal cycle --- p.54 / Chapter 3.5.2 --- Climatic effect --- p.58 / Chapter 3.6 --- Calendrical Water Use --- p.61 / Chapter 3.6.1 --- Day-of-the-week effect --- p.62 / Chapter 3.6.2 --- Holiday effect --- p.63 / Chapter 3.6.3 --- Persistence component --- p.64 / Chapter 3.7 --- Summary --- p.65 / Chapter CHAPTER FOUR --- RESULTS AND DISCUSSION --- p.67 / Chapter 4.1 --- Introduction --- p.67 / Chapter 4.2 --- Model Fitting and Parameterization --- p.68 / Chapter 4.3 --- Long-term Trend in Base Water Use --- p.69 / Chapter 4.4 --- Seasonal Water Use --- p.76 / Chapter 4.4.1 --- Seasonal cycle --- p.76 / Chapter 4.4.2 --- Climatic effect --- p.81 / Chapter 4.5 --- Calendrical Water Use --- p.86 / Chapter 4.5.1 --- Day-of-the-week effect --- p.86 / Chapter 4.5.2 --- Holiday effect --- p.90 / Chapter 4.5.3 --- Persistence component --- p.98 / Chapter 4.6 --- Evaluation of Model Performance --- p.112 / Chapter 4.7 --- Relative Contribution of Various Components of Water Consumption --- p.128 / Chapter CHAPTER FIVE --- CONCLUSION --- p.136 / Chapter 5.1 --- Introduction --- p.136 / Chapter 5.2 --- Summary of Findings --- p.137 / Chapter 5.3 --- Limitations of the Study --- p.141 / Chapter 5.4 --- Recommendations for Future Studies --- p.142 / REFERENCE LIST --- p.143