chua-applicationofensemble-2019.pdf (1.91 MB)
Application of ensemble empirical mode decomposition based on machine learning methodologies in forecasting monthly pan evaporation
journal contribution
posted on 2019-01-01, 00:00 authored by M Rezaie-Balf, O Kisi, Lloyd ChuaLloyd Chua© IWA Publishing 2019 Accurate prediction of pan evaporation (P E ) is one of the crucial factors in water resources management and planning in agriculture. In this research, two hybrid models, self-adaptive time-frequency methodology, ensemble empirical mode decomposition (EEMD) coupled with support vector machine (EEMD-SVM) and EEMD model tree (EEMD-MT), were employed to forecast monthly P E . The EEMD-SVM and EEMD-MT were compared with single SVM and MT models in forecasting monthly P E , measured between 1975 and 2008, at Siirt and Diyarbakir stations in Turkey. The results were evaluated using four assessment criteria, Nash–Sutcliffe Efficiency (NSE), root mean square error (RMSE), performance index (PI), Willmott’s index (WI), and Legates–McCabe’s index (LMI). The EEMD-MT model respectively improved the accuracy of MT by 36 and 44.7% with respect to NSE and WI in the testing stage for the Siirt station. For the Diyarbakir station, the improvements in results were less spectacular, with improvements in NSE (1.7%) and WI (2.2%), respectively, in the testing stage. The overall results indicate that the proposed pre-processing technique is very promising for complex time series forecasting and further studies incorporating this technique are recommended.
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Journal
Hydrology ResearchVolume
50Issue
2Pagination
498 - 516Publisher
IWA PublishingLocation
London, Eng.Publisher DOI
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ISSN
1998-9563eISSN
2224-7955Language
engPublication classification
C1 Refereed article in a scholarly journalUsage metrics
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