scholarly journals Support Vector Machines in Urban Water Demand Forecasting Using Phase Space Reconstruction

2017 ◽  
Vol 186 ◽  
pp. 537-543 ◽  
Author(s):  
Sina Shabani ◽  
Peyman Yousefi ◽  
Gholamreza Naser
Keyword(s):  
Support Vector Machines ◽  
Phase Space ◽  
Water Demand ◽  
Demand Forecasting ◽  
Phase Space Reconstruction ◽  
Support Vector ◽  
Urban Water ◽  
Water Demand Forecasting ◽  
Urban Water Demand ◽  
Vector Machines

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

System dynamics approach to urban water demand forecasting

2009 ◽  
Vol 15 (1) ◽  
pp. 70-74 ◽  
Author(s):  
Hongwei Zhang ◽  
Xuehua Zhang ◽  
Bao’an Zhang
Keyword(s):  
System Dynamics ◽  
Water Demand ◽  
Demand Forecasting ◽  
Urban Water ◽  
Water Demand Forecasting ◽  
Urban Water Demand
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