Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap-neural network models

2013 ◽  
Vol 49 (10) ◽  
pp. 6486-6507 ◽  
Author(s):  
Mukesh K. Tiwari ◽  
Jan Adamowski
Keyword(s):  
Neural Network ◽  
Water Demand ◽  
Demand Forecasting ◽  
Network Models ◽  
Uncertainty Assessment ◽  
Urban Water ◽  
Neural Network Models ◽  
Water Demand Forecasting ◽  
Urban Water Demand

scholarly journals Weekly urban water demand forecasting using a hybrid wavelet–bootstrap–artificial neural network approach

2014 ◽  
Vol 46 (3) ◽  
pp. 197-204
Author(s):  
Kaz Adamowski ◽  
Jan F. Adamowski ◽  
Ousmane Seidou ◽  
Bogdan Ozga-Zieliński
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Demand ◽  
Demand Forecasting ◽  
Urban Water ◽  
Neural Network Approach ◽  
Water Demand Forecasting ◽  
Urban Water Demand ◽  
Artificial Neural ◽  
Artificial Neural Network Approach

Abstract Weekly urban water demand forecasting using a hybrid wavelet-bootstrap-artificial neural network approach. This study developed a hybrid wavelet-bootstrap-artificial neural network (WBANN) model for weekly (one week) urban water demand forecasting in situations with limited data availability. The proposed WBANN method is aimed at improving the accuracy and reliability of water demand forecasting. Daily maximum temperature, total precipitation and water demand data for almost three years were used in this study. It was concluded that the hybrid WBANN model was more accurate compared to the ANN, BANN and WANN methods, and can be applied successfully for operational water demand forecasting. The WBANN model simulated peak water demand very effectively. The better performance of the WBANN model indicated that wavelet analysis significantly improved the model’s performance, whereas the bootstrap technique improved the reliability of forecasts by producing ensemble forecasts. The WBANN model was also found to be effective in assessing the uncertainty associated with water demand forecasts in terms of confidence bands; this can be helpful in operational water demand forecasting.

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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