Prediction model for daily reference crop evapotranspiration based on hybrid algorithm and principal components analysis in Southwest China

Abstract

Accurately estimating reference crop evapotranspiration (ETo) is crucial for water resource management and precise irrigation. The FAO-56 Penman–Monteith model has the highest accuracy in terms of ETo prediction, but its application is restricted by a lack of complete meteorological data. To develop a high accuracy prediction model for ETo with incomplete meteorological data, this study used four principal component analysis methods (i.e. path analysis, stepwise linear regression analysis, logistic regression analysis and factor analysis) to determine few factors that strongly affect ETo as the input to established the ETo model with machine learning (ML). The prediction model for ETo was established using support vector machine (SVM), gradient boosting decision tree (GBDT), particle swarm optimisation (PSO)- SVM, and PSO-GBDT algorithms. This study demonstrated that SVM, GBDT, PSO-SVM and PSO-GBDT models can maintain the highest accuracy by using the five most important affect factors extracted on the basis of path analysis (PA) as input, for various models, the precision ranges of NSE, R2, RMSE, MAE, and GPI were approximately 0.53–0.70, 0.63–0.84, 0.27–0.47, 0.27–0.69, and 0.72–1.01 respectively. The accuracy of SVM and GBDT significantly improved through optimisation using PSO. When inputting the top five factors of importance extracted by PA algorithm in each model, the SVM and GBDT model optimized by PSO algorithm increased the average value of GPI by 17.74% and 46.71% respectively. When inputting the top five factors extracted by the PA algorithm in PSO-GBDT, the average value of NSE, R2, RMSE and MAE is 22.37%, 2.83%, 9.05% and 6.84% higher than that of PSO-SVM, and the GPI is 8.30% higher than that of PSO-SVM. This study shows that the ETo prediction model constructed by the PSO-GBDT algorithm can obtain the highest accuracy when the top five factors extracted by the PA algorithm are used as input. This provides a reference for ETo estimation in Southwest China.