基于前期海温波动与机器学习的复合干热灾害预报:以珠江流域为例

Forecasting of compound dry-hot events based on preceding sea surface temperature patterns and machine learning: A case study of Pearl River Basin

摘要

【目的】气候变化背景下复合干热灾害呈加剧趋势,对社会与生态系统构成了严重威胁。为提升复合干热灾害的预警与防范能力,亟须建立高精度复合干热事件预报模型。【方法】基于不同时期的海温构建前期海温波动模式(PSFP)作为预报因子,采用Loess季节趋势分解方法进行数据处理,结合支持向量机(SVM)、随机森林(RF)和极限学习机(ELM),建立了具备确定、集合与概率预报的复合干热灾害预报模型(PSFP-SVM、PSFP-RF和PSFP-ELM)。以珠江流域为研究区,选取1989年、1991年、1999年、2011年夏季的典型复合干热事件进行模型检验。【结果】结果表明:1和3个月预见期下模型能有效预报珠江流域夏季复合干热事件时空分布特征,其中PSFP-ELM效果最佳,其R²高于0.5,准确率高于0.8,预报结果与实测值高度一致,且在三个月预见期下表现更优。PSFP模型预报精度显著高于基于环流因子的传统预报模型,效果提升可达十倍以上。【结论】研究成果可为干热灾害的风险预警和防灾减灾决策提供科学支撑。

Abstract

[Objective] In the context of climate change, compound dry-hot events(CDHEs) have shown an increasing trend in both frequency and intensity, posing severe threats to social and ecological systems. To enhance the early warning and prevention capability for CDHEs, it is imperative to establish a high-precision forecasting model. [Methods] Preceding sea surface temperature fluctuation patterns(PSFP) were constructed as predictors based on SST variations during different periods. The data were processed using the Loess seasonal-trend decomposition method. Three machine learning models—support vector machine(SVM), random forest( RF), and extreme learning machine( ELM)—were employed to develop deterministic, ensemble, and probabilistic forecasting models(PSFP-SVM, PSFP-RF, and PSFP-ELM) for CDHEs. Taking the Pearl River Basin as the study area, four representative summer CDHEs in 1989, 1991, 1999, and 2011 were selected for model validation. [Results] The result showed that the models could effectively predict the spatiotemporal distribution characteristics of summer CDHEs in the Pearl River Basin with 1-and 3-month lead time. Among them, the PSFP-ELM model performed best, achieving an R² higher than 0. 5 and an accuracy exceeding 0. 8. Its forecast result showed strong agreement with observations and superior performance at the 3-month lead time. The forecasting accuracy of the PSFP-based models was significantly higher than that of traditional circulation factors-based models, with performance improvements exceeding tenfold. [Conclusion] The findings provide a scientific basis for risk early warning and decision-making of disaster prevention and mitigation regarding CDHEs.

关键词

复合干热灾害 / 预报 / 机器学习 / 前期海温波动 / STL分解 / 珠江流域 / 概率预报 / 影响因素

Key words

compound dry-hot events / forecasting / machine learning / preceding sea surface temperature fluctuation / STL decomposition / Pearl River Basin / probabilistic forecasting / influencing factors

基金资助

国家自然科学基金项目(52479015)

广东省自然科学基金项目(2023A1515030191)

华南理工大学中央高校基本科研业务费杰出青年项目(2024ZYGXZR084)