首页|期刊导航|地球与行星物理（英文）|Deep learning-based subseasonal to seasonal precipitation prediction in southwest China:Algorithm comparison and sensitivity to input features
地球与行星物理（英文）2023，Vol.7Issue(4)：471-486,16.DOI:10.26464/epp2023049
Deep learning-based subseasonal to seasonal precipitation prediction in southwest China:Algorithm comparison and sensitivity to input features
Deep learning-based subseasonal to seasonal precipitation prediction in southwest China:Algorithm comparison and sensitivity to input features
摘要
关键词
recurrent neural network/long short-term memory recurrent/sensitivity analysis/artificial intelligence explainability/complex terrain/southwest ChinaKey words
recurrent neural network/long short-term memory recurrent/sensitivity analysis/artificial intelligence explainability/complex terrain/southwest China引用本文复制引用
GuoLu Gao,Yang Li,XueYun Zhou,XiaoMing Xiang,JiaQi Li,ShuCheng Yin..Deep learning-based subseasonal to seasonal precipitation prediction in southwest China:Algorithm comparison and sensitivity to input features[J].地球与行星物理（英文）,2023,7(4):471-486,16.基金项目
We acknowledge the language support from Yulong Tan,and the helpful suggestions from anonymous reviewers and editors.This work was supported by the National Natural Science Foundation of China(Nos.U20A2097,42175042),the Natural Science Founda-tion of Sichuan(Nos.2022NSFSC1056,2023NSFSC0246),the China Scholarship Council(No.201908510031),the Plateau and Basin Rainstorm,Drought and Flood Key Laboratory of Sichuan Province(Nos.SCQXKJZD202102-6,SCQXKJYJXMS202102),the Innovation Team Fund of Southwest Regional Meteorological Center,China Meteorological Administration(No.XNQYCXTD202201),and the Sichuan Science and Technology Program(No.2022YFS0544). （Nos.U20A2097,42175042）
