Improving reference evapotranspiration estimation using novel inter-model ensemble approaches

Abstract

Over recent years, many machine learning tools have been developed in the desire to replace conventional empirical models for reference evapotranspiration (ET0) estimation that evidently needed tedious data collection and calibration. The feasibility of novel hybrid inter-model ensembles as a catalyst to further improve the estimation of ET0 in a tropical climate region was investigated. The multilayer perceptron (MLP), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) were used as base model to estimate ET0 at four meteorological stations of Peninsular Malaysia. Bootstrap aggregating, Bayesian model averaging (BMA) and extreme learning machine (ELM) based non-linear neural ensemble (NNE) were the three approaches used to hybridize the base models. The results showed that the bootstrap aggregating could not improve the performance of the base models by changing the data structure. The effectiveness of the BMA approach was constrained by the fact that it would favor the model with better accuracy. The whale optimization algorithm (WOA) optimized ELM operates in a black-box manner in order to compute the collective decisions of the MLP, SVM and ANFIS for estimating ET0. The WOA-ELM, free to combine the favorable traits and features of the base models had resulted in a hybrid model with higher accuracy as well as generalizability. The global performance indicator (GPI) was used to compare and rank the models from all aspects. The GPI ranking system justified that the inter-model ensemble developed using the WOA-ELM-E (ensemble) was the best model scoring the highest value. Decision-makers are urged to take advantage of the hybrid models for more advantageous beneficial water management plans.