Selected model fusion: an approach for improving the accuracy of monthly streamflow forecasting
| Autor: | Kumars Ebrahimi, Shahab Araghinejad, Fereshteh Modaresi |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Atmospheric Science
Fusion 010504 meteorology & atmospheric sciences Artificial neural network Computer science Model selection 0208 environmental biotechnology 02 engineering and technology Geotechnical Engineering and Engineering Geology 01 natural sciences Regression 020801 environmental engineering Streamflow Statistics Linear regression Benchmark (computing) Selection algorithm 0105 earth and related environmental sciences Civil and Structural Engineering Water Science and Technology |
| Zdroj: | Journal of Hydroinformatics. 20:917-933 |
| ISSN: | 1465-1734 1464-7141 |
| DOI: | 10.2166/hydro.2018.098 |
| Popis: | Monthly streamflow forecasting plays an important role in water resources management, especially for dam operation. In this paper, an approach of model fusion technique named selected model fusion (SMF) is applied and assessed under two strategies of model selection in order to improve the accuracy of streamflow forecasting. The two strategies of SMF are: fusion of the outputs of best individual forecasting models (IFMs) selected by dendrogram analysis (S1), and fusion of the best outputs of all IFMs resulting from an ordered selection algorithm (S2). In both strategies, five data-driven models including: artificial neural network, generalized regression neural network, least square-support vector regression, K-nearest neighbor regression, and multiple linear regression with optimized structure are performed as IFMs. The SMF strategies are applied for forecasting the monthly inflow to Karkheh reservoir, Iran, owning various patterns between predictor and predicted variables in different months. Results show that applying SMF approach based on both strategies results in more accurate forecasts in comparison with fusion of all IFMs outputs (S3), as the benchmark. However, comparison of the two SMF strategies reveals that the implementation of strategy (S2) considerably improves the accuracy of forecasts than strategy (S1) as well as the best IFM results (S4) in all months. |
| Databáze: | OpenAIRE |
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