Application of Time Series-Exponential Smoothing Model on Urban Water Demand Forecasting

2011 ◽  
Vol 183-185 ◽  
pp. 1158-1162 ◽  
Author(s):  
Jun Liang Liu ◽  
Xu Chen ◽  
Tie Jian Zhang
Keyword(s):  
Time Series ◽  
Water Demand ◽  
Demand Forecasting ◽  
Original Data ◽  
Exponential Smoothing ◽  
Forecasting Model ◽  
Urban Water ◽  
Analysis Software ◽  
Urban Water Demand ◽  
Statistical Analysis Software

Based on the traditional time series methods, this paper researched a time series-exponential smoothing model that is built by SPSS statistical analysis software. In the application of the model, the original data of water consumption were in processed by a particular smoothing method first.Secondly, the processed data were used to build a time series-exponential smoothing model. On error test, we found that this forecasting model has advantages of better effect, high precision and minor error on urban water demand forecasing.

scholarly journals A Comparative Assessment of Variable Selection Methods in Urban Water Demand Forecasting

Water ◽  
2018 ◽  
Vol 10 (4) ◽  
pp. 419 ◽  
Author(s):  
Md Haque ◽  
Ataur Rahman ◽  
Dharma Hagare ◽  
Rezaul Chowdhury
Keyword(s):  
Variable Selection ◽  
Water Demand ◽  
Demand Forecasting ◽  
Comparative Assessment ◽  
Urban Water ◽  
Selection Methods ◽  
Water Demand Forecasting ◽  
Urban Water Demand

scholarly journals Artificial Neural Networks for Urban Water Demand Forecasting: A Case Study

2019 ◽  
Vol 1284 ◽  
pp. 012004 ◽  
Author(s):  
Leandro L Lorente-Leyva ◽  
Jairo F Pavón-Valencia ◽  
Yakcleem Montero-Santos ◽  
Israel D Herrera-Granda ◽  
Erick P Herrera-Granda ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Demand ◽  
Demand Forecasting ◽  
Urban Water ◽  
Water Demand Forecasting ◽  
Urban Water Demand ◽  
Artificial Neural

scholarly journals Water demand forecasting using extreme learning machines

2016 ◽  
Vol 28 (1) ◽  
pp. 37-52 ◽  
Author(s):  
Mukesh Tiwari ◽  
Jan Adamowski ◽  
Kazimierz Adamowski
Keyword(s):  
Water Demand ◽  
Demand Forecasting ◽  
Urban Water ◽  
Demand Model ◽  
Demand Prediction ◽  
Learning Machines ◽  
Water Demand Forecasting ◽  
Urban Water Demand ◽  
Learning Machine ◽  
Similar Accuracy

AbstractThe capacity of recently-developed extreme learning machine (ELM) modelling approaches in forecasting daily urban water demand from limited data, alone or in concert with wavelet analysis (W) or bootstrap (B) methods (i.e., ELM, ELMW, ELMB), was assessed, and compared to that of equivalent traditional artificial neural network-based models (i.e., ANN, ANNW, ANNB). The urban water demand forecasting models were developed using 3-year water demand and climate datasets for the city of Calgary, Alberta, Canada. While the hybrid ELMBand ANNBmodels provided satisfactory 1-day lead-time forecasts of similar accuracy, the ANNWand ELMWmodels provided greater accuracy, with the ELMWmodel outperforming the ANNWmodel. Significant improvement in peak urban water demand prediction was only achieved with the ELMWmodel. The superiority of the ELMWmodel over both the ANNWor ANNBmodels demonstrated the significant role of wavelet transformation in improving the overall performance of the urban water demand model.

scholarly journals Hybrid regression model for near real-time urban water demand forecasting

2017 ◽  
Vol 309 ◽  
pp. 532-541 ◽  
Author(s):  
Bruno M. Brentan ◽  
Edevar Luvizotto Jr. ◽  
Manuel Herrera ◽  
Joaquín Izquierdo ◽  
Rafael Pérez-García
Keyword(s):  
Regression Model ◽  
Real Time ◽  
Water Demand ◽  
Demand Forecasting ◽  
Urban Water ◽  
Water Demand Forecasting ◽  
Urban Water Demand
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