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Water Resources Management

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11-11-2017

Subset Modeling Basis ANFIS for Prediction of the Reference Evapotranspiration

Authors: Behrooz Keshtegar, Ozgur Kisi, Hamed Ghohani Arab, Mohammad Zounemat-Kermani

Published in: Water Resources Management | Issue 3/2018

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Abstract

The study investigates accuracy of a new modeling scheme, subset adaptive neuro fuzzy inference system (subset ANFIS), in estimating the daily reference evapotranspiration (ET₀). Daily weather data of relative humidity, solar radiation, air temperature, and wind speed from three stations in Central Anatolian Region of Turkey were utilized as input to the applied models. The input data set for modeling the ET₀ was divided to several subsets to calibrate the local data using a local modeling-based ANFIS. The estimates obtained from subset ANFIS models were compared with those of the M5 model tree (M5Tree), ANFIS models and ANN. Mean absolute error (MAE), root mean square error (RMSE), and model efficiency factor criteria were applied for analysis of models. The accuracy of M5Tree (from 15.3% to 32.5% in RMSE, from 14.4% to 24.2% in MAE), ANN (from 24.3% to 65.3% in RMSE, from 34.1% to 47% in MAE) and ANFIS (from 17.4% to 35.4% in RMSE, from 10.8% to 28.3% in MAE) models was significantly increased using subset ANFIS for estimating da ily ET_0.

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Title: Subset Modeling Basis ANFIS for Prediction of the Reference Evapotranspiration
Authors: Behrooz Keshtegar
Ozgur Kisi
Hamed Ghohani Arab
Mohammad Zounemat-Kermani
Publication date: 11-11-2017
Publisher: Springer Netherlands
Published in: Water Resources Management / Issue 3/2018
Print ISSN: 0920-4741
Electronic ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-017-1857-5

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