ANFIS Modeling with ICA, BBO, TLBO, and IWO Optimization Algorithms and Sensitivity Analysis for Predicting Daily Reference Evapotranspiration: Fid-Bau Portal

ANFIS Modeling with ICA, BBO, TLBO, and IWO Optimization Algorithms and Sensitivity Analysis for Predicting Daily Reference Evapotranspiration

Zeinolabedini Rezaabad, Maryam / Ghazanfari, Sadegh / Salajegheh, Maryam

Abstract

Evapotranspiration (ET) is an important factor in water resource management. This research investigated the performance of four optimization algorithms to hybridize adaptive network-based fuzzy inference systems (ANFIS) models as follow: ANFIS with imperialist competitive algorithm (ANFIS-ICA), ANFIS with biogeography-based optimization (ANFIS-BBO), ANFIS with teaching-learning–based optimization (ANFIS-TLBO), and ANFIS with invasive weed optimization algorithm (ANFIS-IWO). The hybridized algorithms were used to predict reference evapotranspiration ( ${ET}_{o}$ ) values in Kerman synoptic station. Six observed variables, including mean air temperature ( $T_{m e a n}$ ), bright sunshine hours ( $S S H$ ), solar radiation ( $R_{s}$ ), mean speed of the wind at 2-m height ( $U_{2}$ ), pan evaporation ( $E_{p a n}$ ), and three estimated variables, including extraterrestrial radiation ( $R_{a}$ ), saturation vapor pressure ( $e_{s}$ ), and actual vapor pressure ( $e_{a}$ ) were utilized to develop hybrid models. The results showed that the accuracy of hybrid models by using $T_{m e a n}$ , $U_{2}$ , $e_{s}$ , and $e_{a}$ was better than those using all required variables for developing the FAO-Penman-Monteith (FAO-PM) equation. Among the hybrid models, the ANFIS-ICA with respect to $R = 0.99$ , $RMSE = 0.5$ , and $NSE = 0.98$ was considered the superior model. A sensitivity analysis has been done to assess the impact of inputs on the output of the superior model. $E_{a}$ and $T_{m e a n}$ had the highest and lowest effect on ${ET}_{o}$ prediction, respectively. Finally, ${ET}_{o}$ values were estimated by relatively new empirical equations and compared with FAO-PM equation. It was observed that the capability of hybrid models was more than the empirical equations in estimation of the ${ET}_{o}$ values.