Water demand forecasting using extreme learning machines
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 tradition...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2016-03-01
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| Series: | Journal of Water and Land Development |
| Subjects: | |
| Online Access: | http://www.degruyter.com/view/j/jwld.2016.28.issue-1/jwld-2016-0004/jwld-2016-0004.xml?format=INT |
| _version_ | 1797759550897520640 |
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| author | Tiwari Mukesh Adamowski Jan Adamowski Kazimierz |
| author_facet | Tiwari Mukesh Adamowski Jan Adamowski Kazimierz |
| author_sort | Tiwari Mukesh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T18:46:02Z |
| format | Article |
| id | doaj.art-c35b02884a8b454ba5cc80de3d6da946 |
| institution | Directory Open Access Journal |
| issn | 2083-4535 |
| language | English |
| last_indexed | 2024-03-12T18:46:02Z |
| publishDate | 2016-03-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | Journal of Water and Land Development |
| spelling | doaj.art-c35b02884a8b454ba5cc80de3d6da9462023-08-02T07:40:33ZengPolish Academy of SciencesJournal of Water and Land Development2083-45352016-03-01281375210.1515/jwld-2016-0004jwld-2016-0004Water demand forecasting using extreme learning machinesTiwari MukeshAdamowski JanAdamowski KazimierzThe 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.http://www.degruyter.com/view/j/jwld.2016.28.issue-1/jwld-2016-0004/jwld-2016-0004.xml?format=INTartificial neural networksbootstrapCanadaextreme learning machinesuncertaintywater demand forecastingwavelets |
| spellingShingle | Tiwari Mukesh Adamowski Jan Adamowski Kazimierz Water demand forecasting using extreme learning machines Journal of Water and Land Development artificial neural networks bootstrap Canada extreme learning machines uncertainty water demand forecasting wavelets |
| title | Water demand forecasting using extreme learning machines |
| title_full | Water demand forecasting using extreme learning machines |
| title_fullStr | Water demand forecasting using extreme learning machines |
| title_full_unstemmed | Water demand forecasting using extreme learning machines |
| title_short | Water demand forecasting using extreme learning machines |
| title_sort | water demand forecasting using extreme learning machines |
| topic | artificial neural networks bootstrap Canada extreme learning machines uncertainty water demand forecasting wavelets |
| url | http://www.degruyter.com/view/j/jwld.2016.28.issue-1/jwld-2016-0004/jwld-2016-0004.xml?format=INT |
| work_keys_str_mv | AT tiwarimukesh waterdemandforecastingusingextremelearningmachines AT adamowskijan waterdemandforecastingusingextremelearningmachines AT adamowskikazimierz waterdemandforecastingusingextremelearningmachines |