Subset Modeling Basis ANFIS for Prediction of the Reference Evapotranspiration

Behrooz Keshtegar (), Ozgur Kisi, Hamed Ghohani Arab and Mohammad Zounemat-Kermani
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Behrooz Keshtegar: University of Zabol
Ozgur Kisi: Ilia State University
Hamed Ghohani Arab: University of Sistan and Baluchestan
Mohammad Zounemat-Kermani: Shahid Bahonar University of Kerman

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2018, vol. 32, issue 3, No 17, 1116 pages

Abstract: 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 ET0.

Keywords: Reference evapotranspiration; ANFIS; M5 model tree; ANN; Subset ANFIS (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11269-017-1857-5

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