Water demand forecasting using extreme learning machines

Journal of Water and Land Development

Mukesh Tiwari Anand Agricultural University, Department of Soil and Water Engineering, College of Agricultural and Technology, Gujarat, IndiaEmailOther articles by this author:De Gruyter OnlineGoogle Scholar¹  Jan Adamowski McGill University, Faculty of Agricultural and Environmental Sciences, Department of Bioresource Engineering, Quebec, Canada, H9X 3V9EmailOther articles by this author:De Gruyter OnlineGoogle Scholar²  Kazimierz Adamowski University of Ottawa, Department of Civil Engineering, CanadaEmailOther articles by this author:De Gruyter OnlineGoogle Scholar³ 

[1] Anand Agricultural University, Department of Soil and Water Engineering, College of Agricultural and Technology, Gujarat, India;McGill University, Faculty of Agricultural and Environmental Sciences, Department of Bioresource Engineering, Quebec, Canada, H9X 3V9;University of Ottawa, Department of Civil Engineering, Canada

关键词: Keywords: artificial neural networks;    bootstrap;    Canada;    extreme learning machines;    uncertainty;    water demand forecasting;    wavelets;   

DOI  :  10.1515/jwld-2016-0004

学科分类：农业科学（综合）

来源: Instytut Technologiczno-Przyrodniczego / Institute of Technology and Life Sciences

【 摘 要 】

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.

【 授权许可】

Unknown