基于中国图形卷积网络的松病风险预测
Xiumei Mo1, Xiaoting Zhao1, Junhao Zhao1
1State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing, China.
Pest management science
|June 28, 2025
概括
一个新的图形卷积网络 (GCN) 模型准确地预测了中国各地松病风险. 这项技术有助于提前预警,并制定有效的预防策略,对这种破坏性的针叶树疾病.
科学领域:
- 森林病理学 森林病理学
- 计算生态学计算生态学
- 疾病流行病学 疾病流行病学
背景情况:
- 松病是对针叶树种类的重大全球威胁,造成了严重的生态和经济损害.
- 它于1982年引入中国,已经影响了超过10亿棵松树,需要先进的监测和控制方法.
研究的目的:
- 开发一个精确的时空风险预测模型,用于中国的松病.
- 为有针对性的预防和控制工作确定高风险地区.
主要方法:
- 空间时间爆发数据和环境因素的探索性视觉分析.
- 使用图形卷积网络 (GCN) 开发和验证风险预测模型.
主要成果:
- 该GCN模型将24个省份的755个县确定为风险地区,其中219个被指定为高风险地区.
- 观察到"向北扩张和向西进展"的趋势,主要风险集中在东南,北京-天津-河北和东北.
- 预测28个目前没有疫情的城市作为潜在的高风险区域.
结论:
- 图形卷积网络 (GCN) 技术对松病的时空风险预测具有很高的准确性.
- 该模型为早期预警系统和制定有效的预防和控制策略提供了关键的技术支持.
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