TY - JOUR A2 - Coscarelli，罗伯托AU - 徐，任非盟 - 陈，能撑AU - 陈，裕民AU - 陈，Zeqiang PY - 2020 DA - 2020年3月9日TI - 降尺度和使用机多CMIP5降水的投影学习方法在上汉水流域SP - 8680436 VL - 2020 AB - 降尺度相当的大气环流模式（GCM）缓解区域气候模拟的缺点。然而，信息很少使用机器学习方法关于缩小，特别是在水文流域尺度。本研究开发的多个机器学习（ML）按比例缩小的模型，基于贝叶斯模型平均（BMA），为下限值的8耦合模式比较计划第五阶段（CMIP5）使用模式输出统计（MOS）模型降水模拟的年1961年- 2005年上汉江流域。一系列统计指标，包括Pearson相关系数（PCC），均方根误差（RMSE），和相对偏压（Rbias两端）的，被用于评价和比较分析。此外，BMA和最佳ML缩小模型被用来名下的代表性浓度路径4.5（RCP4.5）和RCP8.5情景在21世纪低档次的沉淀。结果显示如下：（1）BMA合奏模拟的表现显然好于单独的模型和简单平均模式集合（MME）的。所述PCC达到0.74，并且RMSE被减少了28％-60％，对于所有的GCMS和33％到MME。（2）缩小的模型大大改善站仿真性能。对于回归（SVR）支持向量机优于多层感知器（MLP）和随机森林（RF）。 The downscaling results based on the BMA ensemble simulation and SVR models were regarded as the best performing overall (PCC, RMSE, and Rbias were 0.82, 35.07, mm and −5.45%, respectively). (3) Based on BMA and SVR models, the projected precipitations show a weak increasing trend on the whole under RCP4.5 and RCP8.5. Specifically, the average rainfall during the mid- (2040–2069) and late (2070–2099) 21st century increased by 3.23% and 1.02%, respectively, compared to the base year (1971–2000) under RCP4.5, while they increased by 4.25% and 8.30% under RCP8.5. Additionally, the magnitude of changes during winter and spring was higher than that during summer and autumn. Furthermore, future work is recommended to study the improvement of downscaling models and the effect of local climate. SN - 1687-9309 UR - https://doi.org/10.1155/2020/8680436 DO - 10.1155/2020/8680436 JF - Advances in Meteorology PB - Hindawi KW - ER -