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基于可解释的随机森林的区域农业干旱脆弱性预测.
1School Statistics and Mathematics, North China University of Water Resources and Electric Power, Zhengzhou, 450046, P.R. China.
本研究使用随机森林模型预测河南省的干旱脆弱性,发现趋势越来越大,并建议加强预警系统. 准确的预测有助于缓解干旱.
科学领域:
- 环境科学 环境科学
- 气候科学 气候科学
- 灾害管理 灾害管理
背景情况:
- 干旱是影响社会和经济的一大全球性自然灾害.
- 河南省因干旱而面临重大社会经济风险.
- 预测干旱脆弱性对于缓解和稳定至关重要.
研究的目的:
- 预测河南省的干旱脆弱性指数 (DVI).
- 确定影响干旱脆弱性的关键因素.
- 为缓解干旱影响及时采取措施提供信息.
主要方法:
- 为河南省计算的历史DVI (2010-2022年).
- 使用灰色相关性分析建立了一个预测指标系统.
- 应用随机森林和夏普利添加式拓展 (SHAP) 模型用于预测和解释.
主要成果:
- 随机森林模型实现了96.96%的准确性,平均百分比误差为3.05%.
- 预测河南省 (2023-2025) 干旱脆弱性的趋势将会增加.
- SHAP模型为随机森林模型的预测提供了可解释的见解.
结论:
- 可解释的随机森林模型准确地预测了干旱脆弱性.
- 建议采取主动措施,包括加强早期预警和提高弹性.
- 减少干旱的社会经济影响对于可持续发展至关重要.
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