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|Title:||Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates||Authors:||Qasem, SN
|Issue Date:||2019||Publisher:||Hong Kong Polytechnic University, Department of Civil and Structural Engineering||Source:||Engineering applications of computational fluid mechanics, 1 Jan. 2019, v. 13, no. 1, p. 177-187 How to cite?||Journal:||Engineering applications of computational fluid mechanics||Abstract:||Evaporation rate is one of the key parameters in determining the ecological conditions and it has an irrefutable role in the proper management of water resources. In this paper, the efficiency of some data-driven techniques including support vector regression (SVR) and artificial neural networks (ANN) and combination of them with wavelet transforms (WSVR and WANN) were investigated for predicting evaporation rates at Tabriz (Iran) and Antalya (Turkey) stations. For evaluating the performances of studied techniques, four different statistical indicators were utilized namely the root mean square error (RMSE), the mean absolute error (MAE), the correlation coefficient (R), and Nash-Sutcliffe ef?ciency (NSE). Additionally, Taylor diagrams were implemented to test the similarity among the observed and predicted data. Outcomes showed that at Tabriz station, the ANN3 (third input combination that are air temperatures and solar radiation used by ANN) with RMSE of 0.701, MAE of 0.525, R of 0.990 and NSE of 0.977 had better performances in comparison with WANN, SVR and WSVR. So, the wavelet transforms did not have positive effects in increasing the precision of ANN and SVR predictions at Tabriz station. Also, approximately the same trend was seen at Antalya station. In other words, ANN5 (fifth input combination that are air temperatures, relative humidity and solar radiation used by ANN) with RMSE of 0.923, MAE of 0.697, R of 0.962 and NSE of 0.898 had a more accurate predictions among others. Conversely, wavelet transform reduced the prediction errors of SVR at Antalya station. So, the WSVR5 with RMSE of 1.027, MAE of 0.728, R of 0.950 and NSE of 0.870 predicted evaporation rates of Antalya station more precisely than other SVR models. As a conclusion, results from the current study proved that ANN provided reasonable trends for evaporation modeling at both Tabriz and Antalya stations.||URI:||http://hdl.handle.net/10397/80495||ISSN:||1994-2060||EISSN:||1997-003X||DOI:||10.1080/19942060.2018.1564702||Rights:||© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,distribution, and reproduction in any medium, provided the original work is properly cited.
The following publication Qasem, S. N., Samadianfard, S., Kheshtgar, S., Jarhan, S., Kisi, O., Shamshirband, S., & Chau, K. W. (2019). Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates. Engineering Applications of Computational Fluid Mechanics, 13(1), 177-187 is available at https://dx.doi.org/10.1080/19942060.2018.1564702
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