Water Demand Forecasting Model for the Metropolitan Area of São Paulo, Brazil (original) (raw)
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Prediction of Water Demand Using Artificial Neural Networks Models and Statistical Model
International Journal of Intelligent Systems and Applications
The prediction of future water demand will help water distribution companies and government to plan the distribution process of water, which impacts on sustainable development planning. In this paper, we use a linear and nonlinear models to predict water demand, for this purpose, we will use different types of Artificial Neural Networks (ANNs) with different learning approaches to predict the water demand, compared with a known type of statistical methods. The dataset depends on sets of collected data (extracted from municipalities databases) during a specific period of time and hence we proposing a nonlinear model for predicting the monthly water demand and finally provide the more accurate prediction model compared with other linear and nonlinear methods. The applied models capable of making an accurate prediction for water demand in the future for the Jenin city at the north of Palestine. This prediction is made with a time horizon month, depending on the extracted data, this data will be used to feed the neural network model to implement mechanisms and system that can be employed to predicts a short-term for water demands. Two applied models of artificial neural networks are used; Multilayer Perceptron NNs (MLPNNs) and Radial Basis Function NNs (RBFNNs) with different learning and optimization algorithms Levenberg Marquardt (LM) and Genetic Algorithms (GAs), and one type of linear statistical method called Autoregressive integrated moving average ARIMA are applied to the water demand data collected from Jenin city to predict the water demand in the future. The execution results appear that the MLPNNs-LM type is outperformed the RBFNN-GAs and ARIMA models in the prediction the water demand values.
International Journal of Advanced Computer Science and Applications, 2018
Artificial Neural Network (ANN) is a widely used machine learning pattern recognition technique in predicting water resources based on historical data. ANN has the ability to forecast close to accurate prediction given the appropriate training algorithm and transfer function along with the model's learning rate and momentum. In this study, using the Neuroph Studio platform, six models having different combination of training algorithms, namely, Backpropagation, Backpropagation with Momentum and Resilient Propagation and transfer functions, namely, Sigmoid and Gaussian were compared. After determining the ANN model's input, hidden and output neurons from its respective layers, this study compared data normalization techniques and showed that Min-Max normalization yielded better results in terms of Mean Square Error (MSE) compared to Max normalization. Out of the six models tested, Model 1 which was composed of Backpropagation training algorithm and Sigmoid transfer function yielded the lowest MSE. Moreover, learning rate and momentum value for the models of 0.2 and 0.9 respectively resulted to very minimal error in terms of MSE. The results obtained in this research clearly suggest that ANN can be a viable forecasting technique for medium-term water consumption forecasting.
Water Resources Management, 2011
The water demand of a city is a complex and non linear function of climatic, socioeconomic, institutional and management variables. Identifying the prominent variables among these is essential in order to adequately predict water demand, and to plan and manage water resources and the supply systems. Further, the need for such identification becomes more pronounced when data constraints arise. The objective of this study was to establish, using correlation and sensitivity analyses, a minimum set of variables required to predict water demand with significant accuracy. Artificial Neural Networks (ANN) models were developed to predict short-term (daily) and medium-term (monthly) demands for Bangkok. Using meteorological and water utility variables for short-term prediction, and different ANN architecture, 16 sets of models with a 1-, 2-and 3-day lead period were developed. Although the best fit models for the three lead periods used different input variables, prediction accuracies over 98% were achieved by using only the historic daily demand (HDD) as the explanatory variable. Similarly, for medium-term prediction, 11 sets of models with lead periods of 1-, 2-and 6-months were developed, using meteorological, water utility and socioeconomic variables. The best fit models for the three lead periods used all explanatory variables but prediction accuracies of more than 98% were obtained by downsizing the variable set. The meteorological variables have a greater influence on medium-term prediction as compared to short-term prediction, suggesting that future water demand in Bangkok could be significantly affected by climate change.
In this research, a multilayer perceptron neural network model with multiactivation function called (MLP-MAF) model has been developed for municipal water demand forecasting. The developed model uses different activation functions in the hidden layer neurons. Different combinations of the linear, logistic, tangent hyperbolic, exponential, sine and cosine activation functions were used in the hidden layer neurons. In order to assess the credibility of developed model results, the model was run over the available data which include the time series of daily and monthly municipal water consumption for fourteen years (1/1/1992-31/12/2004) of Tampa city, USA. Each time series was divided into two subsets: the estimate subset for fitting the model and the holdout subset for evaluating the forecasting ability of the model. Additionally, three statistical measurements, namely the coefficient of determination (R 2), the root mean square error (RMSE) and the mean absolute percent error (MAPE) and two hypothesis tests, namely the t-test and F-test have been reported for examining the forecasting accuracy of the developed model. The results show that the combination of linear, sine and cosine functions is better than other combinations. Furthermore, the effectiveness assessment of this model shows that this approach is considerably more accurate and performs better than the traditional multilayer perceptron (MLP) and radial basis function (RBF) neural networks.
Short-term water demand forecast modeling techniques-CONVENTIONAL METHODS VERSUS AI
Journal - American Water Works Association, 2002
Short-term water demand forecast modeling techniques-CONVENTIONAL METHODS VERSUS AI A variety of forecast modeling techniques, from conventional techniques such as regression and time series analyses to relatively new artificial intelligence (AI) techniques such as expert systems and artificial neural networks (ANNs), were investigated for use in short-term water demand forecasting. Daily water demand, daily maximum air temperature, and daily total rainfall data from Lexington, Ky., for 1982-92 were used to develop and test several forecast models. The performance of each model was evaluated using two standard statistical parameters. On the basis of the measured statistical parameters, the AI models outperformed the conventional models. Both expert system and ANN technologies should be further explored by water utility engineers and managers because these techniques have the potential to enhance the operational performance of various water supply and delivery systems. BY ASHU JAIN AND LINDELL E. ORMSBEE ater utilities use long-range water demand forecast modeling to design their facilities and plan for future water needs. As water supply systems become stressed because of population growth, industrialization, and other socioeconomic factors, water utilities must optimize the operation and management of their existing water supply systems. In addition, water utilities need to improve their predictions of peak water demands to avoid costly overdesign of facilities. One critical aspect for optimizing water supply system operation and management is the accurate prediction of short-term water demands. In this study, several forecast modeling techniques were evaluated for use in short-term water demand forecasting, including conventional techniques, such as regression and time series analyses, and the relatively new artificial intelligence (AI) techniques, expert systems, and artificial neural networks (ANNs). METHODS Model data. Water use depends on many weather-related factors, such as temperature and rainfall, that vary from day to day; therefore, a time period of one
Short-Term Urban Water Demand Prediction Considering Weather Factors
Water Resources Management
Accurate and reliable forecasting plays a key role in the planning and designing of municipal water supply infrastructures. Recent studies related to water demand prediction have shown that water demand is driven by weather variables, but the results do not clearly show to what extent. The principal aim of this research was to better understand the effects of weather variables on water demand. Additionally, it aimed to offer an appropriate and reliable technique to predict municipal water demand by using the Gravitational Search Algorithm (GSA) and Backtracking Search Algorithm (BSA) with Artificial Neural Network (ANN). Moreover, eight weather factors were adopted to evaluate their impact on the water demand. The principal findings of this research are that the hybrid GSA-ANN (Agent=40) model is superior in terms of fitness function (based on RMSE) for yearly and seasonal phases. In addition, it is evidently clear from the findings that the GSA-ANN model has the ability to simulate both seasonal and yearly patterns for daily data water consumption.
Prediction of water consumption using Artificial Neural Networks modelling (ANN)
MATEC Web of Conferences, 2019
This paper presents an application of Artificial Neural Network models (ANN) to predict the water consumption at two scales: i) District Metered Area (DMA) located in the Scientific Campus of Lille University and ii) End user representing a restaurant inside this DMA. Data are collected from Automated Meter Readers (AMRs) that measure in near real-time the water consumption. The models are trained at both daily and hourly time intervals using historical values and the variation between the hour and the type of days. The paper shows that the ANN-based models can well predict the water consumption including peak values.