Serendipity
07468266b4
- LSTM-Attention模型(983K参数) + XGBoost基线 - Flask API后端(4端点) + ECharts可视化大屏(6面板) - LaTeX学位论文完整框架(7章+参考文献) - ERA5下载脚本(CDS逐月并行下载) - README项目文档 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
25 lines
2.6 KiB
TeX
25 lines
2.6 KiB
TeX
\chapter*{摘要}
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\addcontentsline{toc}{chapter}{摘要}
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随着全球气候变暖，高温热浪事件频发，对老年群体的健康构成严重威胁。本研究以焦作市和郑州市为研究区域，利用ERA5-Land气象再分析数据和人口健康统计数据，构建了基于LSTM-Attention的多时间尺度高温健康风险预警模型，并开发了可视化大屏系统。
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本研究主要工作包括：（1）获取并预处理2010-2024年焦作、郑州两市的ERA5-Land气象数据，结合人口普查和卫生统计年鉴数据，构建了温度-健康风险关联数据集；（2）设计了LSTM结合多头自注意力机制的深度学习模型，实现了短期（1-3天）、中期（7天）和长期（30天）三个时间尺度的风险等级预测；（3）以XGBoost作为基线模型进行对比实验，验证了深度学习方法的有效性；（4）基于Flask和ECharts开发了深色科技蓝风格的Web可视化大屏，实现了温度趋势、风险预警、人口概况等信息的多维度展示。
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实验结果表明，LSTM-Attention模型在短期和中期预警任务上优于传统机器学习方法，能够为高温热浪健康风险管理提供有效的决策支持。
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\textbf{关键词：}高温热浪；银发群体；多时间尺度预警；LSTM-Attention；可视化
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\newpage
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\chapter*{Abstract}
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\addcontentsline{toc}{chapter}{Abstract}
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With global warming, frequent heatwave events pose serious threats to the health of the elderly population. This study takes Jiaozuo and Zhengzhou as research areas, utilizes ERA5-Land meteorological reanalysis data and population health statistics to construct an LSTM-Attention based multi-time-scale heat health risk early warning model, and develops a visualization dashboard system.
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The main contributions include: (1) acquisition and preprocessing of ERA5-Land meteorological data (2010-2024) for both cities, combined with census and health statistics data; (2) design of a deep learning model combining LSTM with multi-head self-attention for risk prediction at three time scales (short/medium/long term); (3) comparative experiments with XGBoost baseline to validate the deep learning approach; (4) development of a Flask+ECharts web dashboard with dark tech-blue theme for multi-dimensional visualization.
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Experimental results show that the LSTM-Attention model outperforms traditional methods in short and medium-term early warning tasks, providing effective decision support for heatwave health risk management.
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\textbf{Keywords:} Heatwave; Elderly Population; Multi-time-scale Early Warning; LSTM-Attention; Visualization
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