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Advances in Meteorology
Volume 2017, Article ID 5356324, 9 pages
https://doi.org/10.1155/2017/5356324
Research Article

A New Method for Evaporation Modeling: Dynamic Evolving Neural-Fuzzy Inference System

1School of Natural Sciences and Engineering, Ilia State University, Tbilisi, Georgia
2Department of Civil Engineering, Birjand University of Technology, Birjand, Iran
3Department of Civil and Environmental Engineering, Incheon National University, Incheon 22012, Republic of Korea
4Incheon Disaster Prevention Research Center, Incheon National University, Incheon 22012, Republic of Korea

Correspondence should be addressed to Jong Wan Hu; rk.ca.noehcni@42pgnoj

Received 8 May 2017; Revised 24 July 2017; Accepted 20 August 2017; Published 27 September 2017

Academic Editor: Francesco Viola

Copyright © 2017 Ozgur Kisi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Evaporation estimation is very essential for planning and development of water resources. The study investigates the ability of new method, dynamic evolving neural-fuzzy inference system (DENFIS), in modeling monthly pan evaporation. Monthly maximum and minimum temperatures, solar radiation, wind speed, and relative humidity data obtained from two stations located in Turkey are used as inputs to the models. The results of DENFIS method were compared with the classical adaptive neural-fuzzy inference system (ANFIS) by using root mean square error (RMSE), mean absolute relative error (MARE), and Nash-Sutcliffe Coefficient (NS) statistics. Cross validation was applied for better comparison of the models. The results indicated that DENFIS models increased the accuracy of ANFIS models to some extent. RMSE, MARE, and NS of the ANFIS model were increased by 11.13, 11.45, and 6.83% for the Antalya station and 20.11, 12.94%, and 8.29% for the Antakya station using DENFIS.