Hierarchical Fuzzy Systems Integrated with Particle Swarm Optimization for Daily Reference Evapotranspiration Prediction: A Novel Approach

Abstract

Reference evapotranspiration (ET0) is a crucial element for deriving a meaningful scheduling of irrigation for major crops. Thus, precise projection of future ET0 is essential for better management of scarce water resources in many parts of the globe. This study evaluates the potential of a Hierarchical Fuzzy System (HFS) optimized by Particle Swarm Optimization (PSO) algorithm (PSO-HFS) to predict daily ET0. The meteorological variables and estimated ET0 were employed as inputs and outputs, respectively, for the PSO-HFS model. The FAO 56 PM method to ET0 computation was implemented to obtain ET0 values using the climatic variables obtained from two weather stations located in Gazipur Sadar and Ishurdi, Bangladesh. Prediction accuracy of PSO-HFS was compared with that of a FIS, M5 Model Tree, and a Regression Tree (RT) model. Several statistical performance evaluation indices were used to evaluate the performances of the PSO-HFS, FIS, M5 Model Tree, and RT in estimating daily ET0. Ranking of the models was performed using the concept of Shannon’s Entropy that accounts for a set of performance evaluation indices. Results revealed that the PSO-HFS model performed better than the tree-based models. Generalization capabilities of the preposed models were evaluated using the dataset from a test station (Ishurdi station). Results revealed that the models performed equally well with the unseen test dataset, and that the PSO-HFS model provided superior performance over other tree based models. The overall results imply that PSO-HFS model could effectively be utilized to model ET0 values quite efficiently and accurately.