Modelling reference evapotranspiration by combining neuro fuzzy and evolutionary strategies

• Autorzy: Alizamir, Meysam , Kisi, Ozgur , Muhammad Adnan, Rana , Kuriqi, Alban
• Języki publikacji: EN

Abstrakty

EN

This study investigates the potential of two evolutionary neuro-fuzzy inference systems, adaptive neuro-fuzzy inference system (ANFIS) with particle swarm optimization (ANFIS–PSO) and genetic algorithm (ANFIS–GA), in modelling reference evapotranspiration (ET0). The hybrid models were tested using Nash–Sutclife efciency, root mean square errors and determination coefcient (R2 ) statistics and compared with classical ANFIS, artifcial neural networks (ANNs) and classifcation and regression tree (CART). Various combinations of monthly weather data of solar radiation, relative humidity, average air temperature and wind speed gotten from two stations, Antalya and Isparta, Turkey, were used as input parameters to the developed models to estimate ET0. The recommended evolutionary neuro-fuzzy models produced better estimates compared to ANFIS, ANN and CART in modelling monthly ET0. The ANFIS–PSO and/or ANFIS–GA improved the accuracy of ANFIS, ANN and CART by 40%, 32% and 66% for the Antalya and by 14%, 44% and 67% for the Isparta, respectively.

Słowa kluczowe

PL

optymalizacja roju cząstek; algorytm genetyczny

EN

particle swarm optimization; genetic algorithm; reference evapotranspiration modelling; evolutionary neuro-fuzzy inference systems

• Czasopismo: Acta Geophysica
• Rocznik: 2020
• Tom: Vol. 68, no. 4
• Strony: 1113--1126
• Opis fizyczny: Bibliogr. 55 poz.

Twórcy

autor

Alizamir, Meysam

• Department of Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran,

autor

Kisi, Ozgur

• Faculty of Natural Sciences and Engineering, Ilia State University, Tbilisi, Georgia

autor

Muhammad Adnan, Rana

• State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China

autor

Kuriqi, Alban

• CERIS, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)

Identyfikatory

DOI

10.1007/s11600-020-00446-9