Modelling reference evapotranspiration by combining neuro-fuzzy and evolutionary strategies

Acta Geophysica

article

Alizamir, Meysam¹  Kisi, Ozgur²  Muhammad Adnan, Rana³  Kuriqi, Alban⁴ 

[1] Department of Civil Engineering, Hamedan Branch, Islamic Azad University;Faculty of Natural Sciences and Engineering, Ilia State University;State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University;Instituto Superior Técnico, Universidade de Lisboa

关键词: Reference evapotranspiration modelling;    Evolutionary neuro-fuzzy inference systems;    Particle swarm optimization;    Genetic algorithm;   

DOI  :  10.1007/s11600-020-00446-9

学科分类：地球科学(综合)

来源: Polska Akademia Nauk * Instytut Geofizyki

【 摘 要 】

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–Sutcliffe efficiency, root mean square errors and determination coefficient (R2) statistics and compared with classical ANFIS, artificial neural networks (ANNs) and classification 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.

【 授权许可】

Unknown