Robustness of Extreme Learning Machine in the prediction of hydrological flow series
- Publication Type:
- Journal Article
- Citation:
- Computers and Geosciences, 2018, 120 pp. 105 - 114
- Issue Date:
- 2018-11-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1-s2.0-S0098300417304867-main.pdf | Published Version | 1.32 MB |
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© 2018 Elsevier Ltd Prediction of hydrological flow series generated from a catchment is an important aspect of water resources management and decision making. The underlying process underpinning catchment flow generation is complex and depends on many parameters. Determination of these parameters using a trial and error method or optimization algorithm is time consuming. Application of Artificial Intelligence (AI) based machine learning techniques including Artificial Neural Network, Genetic Programming (GP) and Support Vector Machine (SVM) replaced the complex modeling process and at the same time improved the prediction accuracy of hydrological time-series. However, they still require numerous iterations and computational time to generate optimum solutions. This study applies the Extreme Learning Machine (ELM) to hydrological flow series modeling and compares its performance with GP and Evolutionary Computation based SVM (EC-SVM). The robustness and performance of ELM were studied using the data from two different catchments located in two different climatic conditions. The robustness of ELM was evaluated by varying number of lagged input variables the number of hidden nodes and input parameter (regularization coefficient). Higher lead days prediction and extrapolation capability were also investigated. The results show that (1) ELM yields reasonable results with two or higher lagged input variables (flows) for 1-day lead prediction; (2) ELM produced satisfactory results very rapidly when the number of hidden nodes was greater than or equal to 1000; (3) ELM showed improved results when regularization coefficient was fine-tuned; (4) ELM was able to extrapolate extreme values well; (5) ELM generated reasonable results for higher number of lead days (second and third) predictions; (6) ELM was computationally much faster and capable of producing better results compared to other leading AI methods for prediction of flow series from the same catchment. ELM has the potential for forecasting real-time hydrological flow series.
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