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作 者:张轩 张行南 王高旭[1,2] 吴巍[1,2] 许怡 ZHANG Xuan;ZHANG Xingnan;WANG Gaoxu;WU Wei;XU Yi(Hydrology and Water Resources Department,Nanjing Hydraulic Research Institute,Nanjing 210029,China;State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering,Nanjing 210029,China;College of Hydrology and Water Resources,Hohai University,Nanjing 210098,China)
机构地区:[1]南京水利科学研究院水文水资源研究所,江苏南京210029 [2]水文水资源与水利工程科学国家重点实验室,江苏南京210029 [3]河海大学水文水资源学院,江苏南京210098
出 处:《水资源保护》2022年第4期131-136,165,共7页Water Resources Protection
基 金:国家重点研发计划(2016YFA0601703,2016YFC0401005);国家自然科学基金(91847301,92047203,52009080,42075191)。
摘 要:为分析数理统计模型与机器学习模型在中长期径流预报中的特点与适用性,挑选逐步回归与随机森林两种方法构建入库流量中长期预报模型,以气象因子的物理机制为基础,结合单相关系数及随机森林重要性分析识别关键气象因子并输入模型。利用长江上游乌东德、瀑布沟两个水库1959—1998年的入库流量训练了模型,并且预测了两个水库1999—2014年的入库流量。结果表明:两种模型的训练效果良好,稳定性强,随机森林的预测结果比逐步回归的精度高,但精度的差距较小;随机森林能减少预测因子值的异常变化带来的拟合误差,但过拟合问题更为明显。In order to analyze the characteristics and applicability of the mathematical statistical model and the machine learning model in medium and long term runoff forecast,stepwise regression and random forest were selected to build a medium and long term forecast model.Based on the physical mechanism of meteorological factors,combined with single correlation coefficient and random forest importance analysis,key meteorological factors were identified and input into the model.The model was trained based on the inflow runoff of Wudongde and Pubugou reservoirs in the upper reaches of the Yangtze River from 1959 to 1998,and the inflow runoff of the two reservoirs from 1999 to 2014 was predicted.The results show that the two models have good training effect and strong stability.The prediction result of random forest is higher than that of stepwise regression,but the difference of accuracy is small.Random forest can reduce the fitting error caused by the abnormal change of predictor value,but the overfitting problem is more obvious.
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