基于特征优化和权重空间聚类的玉米风险评估:中国山东省的一个案例研究
Yanan Zuo1, Min Ji2, Jiutao Yang3
1College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, 266590, China.
Scientific reports
|July 31, 2025
概括
气候变暖增加了玉米虫的爆发. 这项研究开发了一种新的机器学习模型,用于精确的风险评估和山东省的空间划分,以帮助控制害虫的努力.
科学领域:
- 农业科学 农业科学
- 数据科学数据科学数据科学
- 环境科学 环境科学
背景情况:
- 玉米挖掘机在农业中造成了重大的全球经济损失.
- 气候变暖加剧了害虫疫情,需要先进的风险评估.
- 现有的模型在空间时间风险评估中往往缺乏准确性.
研究的目的:
- 开发和验证一种新的机器学习方法来评估玉米发生风险.
- 量化山东省玉米风险的时间和空间分布.
- 为害虫管理策略提供决策支持.
主要方法:
- 为不平衡的害虫数据集构建了一个特征优化模型 (边界-SMOTE与遗传算法-随机森林).
- 为空间风险评估开发了一个加权集群算法,克服了传统方法的局限性.
- 综合自然灾害风险理论 (危险性,敏感性,能力) 用于全面的风险区分.
主要成果:
- 与原来的随机森林模型相比,改进的模型显著提高了性能指标 (OOB_score,精度,F1_score).
- 权重的K-means集群表现出优于权重的聚合层次集群和权重的DBSCAN的性能.
- 空间风险区划确定了山东西南部和北部的高风险地区,与实际情况保持一致.
结论:
- 拟议的机器学习框架为农业害虫风险评估提供了一种新有效的方法.
- 这些发现为山东省针对性地预防和控制玉米虫提供了关键数据.
- 这项研究有助于了解气候变化对害虫动态的影响,并制定适应性管理策略.
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