基于CART重要度排序和混合ELM模型的蒸散预测

准确地预测区域蒸散有助于区域水资源的合理利用，减少水资源浪费。为从多项气象因子中筛选出核心因子，构建少因子蒸散预测模型，高效精确预测蒸散，该研究在九大农业区选取23个典型站点，搜集降水量、日照时数等8个气象因子数据，使用分类回归树（classification and regression tree，CART）对气象因子进行重要度排序。基于排序结果，选取排序前3～5项气象因子，基于极限学习机（extreme learning machine，ELM）模型对蒸散进行预测。同时，使用遗传算法（genetic algorithm，GA），粒子群算法（particle swarm optimization，PSO），麻雀搜索算法（sparrow search algorithm，SSA）对ELM模型进行优化，并使用这3种优化算法（GA-ELM、PSO-ELM、SSA-ELM）构建少因子混合优化蒸散预测模型。结果表明：1）基于CART算法重要度排序结果，蒸散的主要影响因子依次是降水量、日照时数、平均本站气压、日最高气温、平均相对湿度。2）3种优化算法预测模型中，PSO-ELM模型的预测精度最高，23个站点的蒸散预测的均方根误差为6.608～22.077 mm/d，纳什效率系数为0.824～0.998，R²为0.908～0.995，平均绝对误差为5.075～16.677 mm/d。3）ELM模型在云贵高原区和四川盆地及周边地区有较好的适用性，3种优化算法在华南区和云贵高原区有较好的适用性，其中PSO-ELM模型的适用性最高。研究结果为中国九大农业区域的作物需水量计算提供参考。

Evapotranspiration prediction using CART importance ranking and hybrid ELM models

Accurate prediction of regional water evapotranspiration can greatly contribute to the rational utilization of regional water resources for the water resources saving. Crop evapotranspiration can be one of the most important indicators for the water evapotranspiration status of crops, in order to evaluate the soil water balance of farmland and the water management of farmland. However, the calculation of crop evapotranspiration requires a large amount of meteorological factor data. There is often redundant data with the low correlation with the evapotranspiration in meteorological data, which seriously affects the prediction accuracy and efficiency of the model. It is a high demand to extract the core environmental factors for the less-factor prediction model. The classification and regression tree (CART) can be used to make feasible for the large data sources in a short time. Then, Less factors can be extracted for the predictive analysis in a more scientific way. In this study, a less-factor water evapotranspiration model was constructed to efficiently and accurately predict the evapotranspiration data. The core factors were selected from multiple meteorological factors. 23 typical stations were selected in the nine agricultural regions, and then collected data on eight meteorological factors, such as precipitation, and sunshine hours. The meteorological factors were ranked in the order of importance using CART. The top 3-5 meteorological factors were then selected to predict the evapotranspiration using the extreme learning machine (ELM) model. At the same time, the ELM model was optimized using genetic algorithm (GA), particle swarm optimization (PSO), sparrow search algorithm (SSA). Three optimization algorithms (GA-ELM, PSO-ELM, SSA-ELM) were used to construct a less-factor hybrid optimization water evapotranspiration prediction model. Relative root mean square error (RMSE), coefficient of determination (R²), mean absolute error (MAE), and Nash-Sutcliffe coefficient (NSE) were used to evaluate the performance of the ELM and optimization model. The results showed that: 1) The main influencing factors were ranked in the order of the precipitation, sunshine duration, mean station pressure, daily maximum temperature, and average relative humidity using the importance ranking of the CART algorithm. 2) The PSO-ELM model presented the highest prediction accuracy among the three optimization algorithms. Specifically, the RMSE of evapotranspiration prediction for the 23 stations was ranged from 6.608 to 22.077 mm/d, the NSE of 0.824-0.998, R² of 0.908-0.995, and MAE of 5.075-16.677 mm/d. In addition, the RF and SVR model with the strong generalization performance were selected to compare with the PSO-ELM model. The prediction performance of three models was slightly improved with the increase of the input factors, indicating the slight overall improvement. The three models shared the strong generalization and robustness. The meteorological factors were input into the prediction model, according to the order of importance. The meteorological factors ranked the 4th and 5th were relatively less important, where the prediction accuracy was slightly improved with the increase of the input parameters. The PSO-ELM model presented the highest prediction accuracy among the three models. 3) The ELM model performed better applicability in the Yunnan-Guizhou Plateau region, the Sichuan Basin, and the surrounding areas. The three optimization algorithms showed the better applicability in the South China and the Yunnan-Guizhou Plateau region, with the highest applicability of the PSO-ELM model. The findings can provide an important reference for the crop water demand calculation in nine major agricultural regions in China.