人民珠江2024，Vol.45Issue(5)：96-102,7.DOI:10.3969/j.issn.1001-9235.2024.05.011
基于海表温度和随机森林的珠江流域干旱预报模型研究
Drought Prediction Model of Pearl River Basin Based on SST and Machine Learning
摘要
Abstract
Sea surface temperature(SST)is one of the main factors for drought forecasting.Conventional forecasting models mainly use SST from fixed sea areas(e.g.,ENSO),without searching for available SST signals from a global large-scale perspective.Combining with the random forest algorithm,this paper constructs a new meteorological drought forecasting model through regression analysis to screen global SST areas of forecasting significance and takes the Pearl River Basin as an example for application tests.The results are as follows.① The model can effectively forecast the temporal and spatial evolution of drought,and as the forecast period becomes longer,the forecast accuracy decreases accordingly.② The accuracy of drought forecast is higher in non-flood season than in flood season,and the coastal area has a better forecast effect than the inland area.③ The occurrence of droughts in the Pearl River Basin may be related to typical climate fluctuations,such as the El Niño Southern Oscillation and the North Atlantic Oscillation.关键词
干旱预报/海表温度/随机森林/珠江流域Key words
drought predication/sea surface temperature/random forest/Pearl River Basin分类
建筑与水利引用本文复制引用
冯鑫,刘艳菊,童宏福,钱姝妮..基于海表温度和随机森林的珠江流域干旱预报模型研究[J].人民珠江,2024,45(5):96-102,7.基金项目
国家自然科学基金(52109019) （52109019）
广东省自然科学基金(2023A1515030191) （2023A1515030191）
