基于随机森林与支持向量机的水库长期径流预报

水库长期径流预报对于研判水文情势变化和指导水库调度管理具有重要意义。针对云南龙江水库年、汛期和枯水期平均入库径流，利用随机森林从环流指数、海温、气压和前期月径流中选取关键预报因子，基于粒子群与交叉验证相结合的算法优选参数，建立随机森林与支持向量机模型，开展龙江水库入库径流预报研究。结果表明:太平洋中北部与西部气候因子对径流预报的影响较大，前期月径流对年、汛期径流的重要性偏低，但对枯水期的影响程度与部分气候因子相当。随机森林与支持向量机模型总体精度较高，模拟与预报的合格率均达到85%以上，平均绝对百分比误差均低于15%，支持向量机的泛化能力强于随机森林，但二者在局部极值流量处的预报精度尚有待提升。

Long-term inflow forecast of reservoir based on Random Forest and support vector machine

Long-term runoff forecasting for the reservoir is of great significance for studying the hydrological regime and guiding the regulations. In this paper, the mean inflow of annual, flood and dry seasons of the Longjiang Reservoir are selected as forecast elements. Random Forest (RF) is utilized to filter key predictors from circulation indices, sea temperature, air pressure and previous monthly runoff. Afterwards, models based on RF and support vector machine (SVM), which are calibrated using particle swarm optimization algorithm combined with cross-validation, are established to predict the inflow of the Longjiang Reservoir. Results show that climate factors in the north-central and western Pacific have generally implemented a greater influence on prediction, while the effect of the pre-monthly runoff is relatively low, however it can be comparable to some climate factors when used to predict runoff in the dry season. The average accuracy of RF and SVM is generally satisfactory, with the qualification rate of simulation and forecast exceeding 85% and the average absolute percentage error less than 15%. SVM shows stronger generalization ability compared to RF in this study case, while the ability of both models in predicting partial extreme inflow remains to be improved.