scholarly journals Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition

2021 ◽  
Author(s):  
Xin Liu ◽  
Xuefeng Sang ◽  
Jiaxuan Chang ◽  
Yang Zheng
Keyword(s):  
Time Series ◽  
Water Supply ◽  
Water Demand ◽  
Coupling Model ◽  
T Test ◽  
Demand Prediction ◽  
Interval Prediction ◽  
Time Series Decomposition ◽  
Daily Water ◽  
Water Demand Prediction

Abstract The fluctuation of water supply is affected by the living habits and population mobility, so the daily water supply is significantly non-stationarity, which presents a great challenge to the water demand prediction based on data-driven model. To solve this problem, the Hodrick-Prescott (HP) and wavelet transform (WT) time series decomposition methods, and ensemble learning (EL) were introduced, coupling model bidirectional long short term memory (BLSTM), seasonal autoregressive integrated moving average (SARIMA) and Gaussian radial basis function neural network (GRBFNN) were developed, and interval prediction was carried out based on student's t-test (T-test). This research method was applied to the daily water demand prediction in Shenzhen and cross-validation was performed. It is found that the decomposed subseries has obvious law, and WT is superior to HP decomposition method. However, the maximum decomposition level (MDL) of WT should not be set too high, otherwise the trend characteristics of subseries will be weakened. The results show that the potential characteristics and quantitative relationships of historical data can be learned accurately based on WT and coupling model. Although the corona virus disease 2019 (COVID-19) outbreak in 2020 caused a variation in water supply law, this variation is still within the interval prediction. The WT and coupling model satisfactorily predicted water demand and provided the lowest mean square error (0.17%), mean relative error (0.1) and mean absolute error (3.32%) and the highest Nash-Sutcliffe efficiency (97.21%) and correlation coefficient (0.99) in testing set.

scholarly journals Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition

2021 ◽  
Author(s):  
Xin Liu ◽  
Xuefeng Sang ◽  
Jiaxuan Chang ◽  
Yang Zheng
Keyword(s):  
Water Supply ◽  
Water Demand ◽  
Short Term Memory ◽  
Prediction Interval ◽  
Virus Disease ◽  
Generalization Ability ◽  
Demand Prediction ◽  
Daily Data ◽  
Daily Water ◽  
Water Demand Prediction

AbstractThe water supply in megacities can be affected by the living habits and population mobility, so the fluctuation degree of daily water supply data is acute, which presents a great challenge to the water demand prediction. This is because that non-stationarity of daily data can have a large influence on the generalization ability of models. In this study, the Hodrick-Prescott (HP) and wavelet transform (WT) methods were used to carry out decomposition of daily data to solve the non-stationarity problem. The bidirectional long short term memory (BLSTM), seasonal autoregressive integrated moving average (SARIMA) and Gaussian radial basis function neural network (GRBFNN) were developed to carry out prediction of different subseries. The ensemble learning was introduced to improve the generalization ability of models, and prediction interval was generated based on student's t-test to cope with the variation of water supply laws. This study method was applied to the daily water demand prediction in Shenzhen and cross-validation was performed. The results show that WT is superior to HP decomposition method, but maximum decomposition level of WT should not be set too high, otherwise the trend characteristics of subseries will be weakened. Although the corona virus disease 2019 (COVID-19) outbreak caused a variation in water supply laws, this variation is still within the prediction interval. The WT and coupling models accurately predict water demand and provide the optimal mean square error (0.17%), Nash-Sutcliffe efficiency (97.21%), mean relative error (0.1), mean absolute error (3.32%), and correlation coefficient (0.99).

Application of short-term water demand prediction model to Seoul

2002 ◽  
Vol 46 (6-7) ◽  
pp. 255-261 ◽  
Author(s):  
C.N. Joo ◽  
J.Y. Koo ◽  
M.J. Yu
Keyword(s):  
Water Demand ◽  
Model Building ◽  
Time Series Prediction ◽  
Social Factor ◽  
Maximum Temperature ◽  
Factors Affecting ◽  
Demand Prediction ◽  
Daily Water ◽  
Water Demand Prediction ◽  
Artificial Neural Network Ann

To predict daily water demand for Seoul, Korea, the artificial neural network (ANN) was used. For the cross correlation, the factors affecting water demand such as maximum temperature, humidity, and wind speed as natural factors, holidays as a social factor and daily demand 1 day before were used. From the results of learning using various hidden layers and units in order to establish the structure of optimal ANN, the case of 3 hidden layers and numbers of unit with the same number of input factors showed the best result and, therefore, it was applied to seasonal water demand prediction. The performance of ANN was compared with a multiple regression method. We discuss the representation ability of the model building process and the applicability of the ANN approach for the daily water demand prediction. ANN provided reasonable results for time series prediction.

scholarly journals Water demand prediction optimization method in Shenzhen based on the zero-sum game model and rolling revisions

Water Policy ◽  
2021 ◽  
Author(s):  
Xin Liu ◽  
Xuefeng Sang ◽  
Jiaxuan Chang ◽  
Yang Zheng
Keyword(s):  
Deep Learning ◽  
Water Supply ◽  
Water Demand ◽  
Learning Model ◽  
Generalization Ability ◽  
Demand Prediction ◽  
Water Demand Prediction ◽  
Predicted Values ◽  
Zero Sum ◽  
Deep Learning Model

Abstract In this study, a deep learning model based on zero-sum game (ZSG) was proposed for accurate water demand prediction. The ensemble learning was introduced to enhance the generalization ability of models, and the sliding average was designed to solve the non-stationarity problem of time series. To solve the problem that the deep learning model could not predict water supply fluctuations caused by emergencies, a hypothesis testing method combining Student's t-test and discrete wavelet transform was proposed to generate the envelope interval of the predicted values to carry out rolling revisions. The research methods were applied to Shenzhen, a megacity with extremely short water resources. The research results showed that the regular bidirectional models were superior to the unidirectional model, and the ZSG-based bidirectional models were superior to the regular bidirectional models. The bidirectional propagation was conducive to improving the generalization ability of the model, and ZSG could better guide the model to find the optimal solution. The fluctuations in water supply were mainly caused by the floating population, but the fluctuation was still within the envelope interval of the predicted values. The predicted values after rolling revisions were very close to the measured values.

scholarly journals Uncertain time series forecasting method for the water demand prediction in Beijing

2021 ◽  
Author(s):  
Haiyan Li ◽  
Xiaosheng Wang ◽  
Haiying Guo
Keyword(s):  
Time Series ◽  
Water Demand ◽  
Autoregressive Model ◽  
Performance Criteria ◽  
Water Supply Systems ◽  
Identification Algorithm ◽  
Demand Prediction ◽  
Uncertain Variables ◽  
Water Demand Prediction ◽  
Uncertain Time

Abstract Water demand prediction is crucial for effectively planning and management of water supply systems to handle the problem of water scarcity. Taking into account the uncertainties and imprecisions within the framework of water demand forecasting, the uncertain time series prediction method is introduced for the water demand prediction. Uncertain time series is a sequence of imprecisely observed values that are characterized by uncertain variables and the corresponding uncertain autoregressive model is employed to describe it for predicting the future values. The main contributions of this paper are shown as follows. Firstly, by defining the auto-similarity of uncertain time series, the identification algorithm of uncertain autoregressive model order is proposed. Secondly, a new parameter estimation method based on the uncertain programming is developed. Thirdly, the imprecisely observed values are assumed as the linear uncertain variables and a ratio-based method is presented for constructing the uncertain time series. Finally, the proposed methodologies are applied to model and forecast the Beijing's water demand under different confidence levels and compared with the traditional time series, i.e., ARIMA method. The experimental results are evaluated on the basis of performance criteria, which shows that the proposed method outperforms over the ARIMA method for water demand prediction.

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