Advanced Hybrid Metaheuristic Machine Learning Models Application for Reference Crop Evapotranspiration Prediction

Abstract

Hybrid metaheuristic algorithm (MA), an advanced tool in the artificial intelligence field, provides precise reference evapotranspiration (ETo) prediction that is highly important for water resource availability and hydrological studies. However, hybrid MAs are quite scarcely used to predict ETo in the existing literature. To this end, the prediction abilities of two support vector regression (SVR) models coupled with three types of MAs including particle swarm optimization (PSO), grey wolf optimization (GWO), and gravitational search algorithm (GSA) were studied and compared with single SVR and SVR-PSO in predicting monthly ETo using meteorological variables as inputs. Data obtained from Rajshahi, Bogra, and Rangpur stations in the humid region, northwestern Bangladesh, was used for this purpose as a case study. The prediction precision of the proposed models was trained and tested using nine input combinations and assessed using root mean square error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE). The tested results revealed that the SVR-PSOGWO model outperformed the other applied soft computing models in predicting ETo in all input combinations, followed by the SVR-PSOGSA, SVR-PSO, and SVR. It was found that SVR-PSOGWO decreases the RMSE of SVR, SVR-PSO, and SVR-PSOGSA by 23%, 27%, 14%, 21%, 19%, and 5% in Rangpur and Bogra stations during the testing stage. The RMSE of the SVR, SVR-PSO, and SVR-PSOGSA reduced by 32%, 20%, and 3%, respectively, employing the SVR-PSOGWO for the Rajshahi Station. The proposed hybrid machine learning model has been recommended as a potential tool for monthly ETo prediction in a humid region and similar climatic regions worldwide.

Reference Evapotranspiration Estimation Using Genetic Algorithm-Optimized Machine Learning Models and Standardized Penman–Monteith Equation in a Highly Advective Environment

“…The GA is a widely used optimization technique that has shown promise in agricultural studies for fine-tuning the parameters of ML models [50]. It is an evolutionary algorithm used to search for optimized solutions to the natural evolutionary process through simulation [1].…”

Research on cutting tool edge geometry design based on SVR-PSO

“…SHANGGUAN et al [9] employed PSO-SVR to predict the icing time of road surfaces, thereby addressing e ciency challenges in road transportation systems in cold regions and improving prediction accuracy. IKRAM et al [10] investigated the predictive capabilities of a coupled SVR model with PSO, validating their method using data from three meteorological stations in wet areas of northwestern Bangladesh. Their proposed hybrid machine learning model was recommended as a predictive tool for monthly reference evapotranspiration in similar wet areas globally.…”

Modeling Potential Evapotranspiration by Improved Machine Learning Methods Using Limited Climatic Data

“…In contrast, among soft computing models, those of hybrid structure have better performance in computation time and reduced errors [25][26][27][28]. Therefore, in this study, we tested random vector functional link (RVFL) and relevance vector machine (RVM) with a quantum-based avian navigation optimizer algorithm (QANA) and artificial hummingbird algorithm (AHA) to estimate ET in a semi-arid region in Pakistan.…”