Water demand forecasting using extreme learning machines

• Main Authors: Tiwari Mukesh, Adamowski Jan, Adamowski Kazimierz
• Format: Article
• Language: English
• Published: Sciendo 2016-03-01
• Series: Journal of Water and Land Development
• Subjects: artificial neural networks; bootstrap; Canada; extreme learning machines; uncertainty; water demand forecasting; wavelets
• Online Access: http://www.degruyter.com/view/j/jwld.2016.28.issue-1/jwld-2016-0004/jwld-2016-0004.xml?format=INT

The 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 ELMB and ANNB models provided satisfactory 1-day lead-time forecasts of similar accuracy, the ANNW and ELMW models provided greater accuracy, with the ELMW model outperforming the ANNW model. Significant improvement in peak urban water demand prediction was only achieved with the ELMW model. The superiority of the ELMW model over both the ANNW or ANNB models demonstrated the significant role of wavelet transformation in improving the overall performance of the urban water demand model.