一种机器学习方法来绘制哥伦比亚土著社区潜在的农业生态复杂性的地图
Cintya Katherine Ojeda Riaños1, Carlos Alberto Torres2, Juan Camilo Zapata Calero3
1Investigadora SENNOVA-SENA Regional Putumayo. Puerto Asís, Colombia.
Journal of environmental management
|September 29, 2024
概括
机器学习绘制了哥伦比亚土著沙格拉的农业生态潜力. 该工具通过确定合适的地区和了解影响生物多样性的环境因素,有助于规划可持续农业.
科学领域:
- 农业生态学和土著知识系统
- 环境科学与环境保护
- 地理空间分析和机器学习
背景情况:
- 密集农业带来了环境挑战,农业生态系统提供了潜在的解决方案.
- 哥伦比亚Sibundoy山谷的土著查格拉面临着农业扩张和人口增长的威胁.
- 有效的农业生态规划需要准确,可访问地图的农业生态潜力,这往往是昂贵和耗时的.
研究的目的:
- 开发一种具有成本效益和高效的方法,用于绘制Sibundoy山谷的农业生态潜力.
- 创建生物多样性管理系数 (BMC) 的数字地图,作为农业生态潜力的代理.
- 通过空间规划,支持建立和增强土著沙格拉.
主要方法:
- 利用机器学习,特别是XGBoost模型,来预测农业生态潜力.
- 训练模型使用15个环境预测因素和800个查格拉的现场BMC数据.
- 部署模型以生成整个研究区域的BMC课程的空间地图.
主要成果:
- 在使用XGBoost模型预测多类BMC结构时获得了70%的准确性.
- 生成了一个详细的地图,显示了农业生态潜力的空间分布 (BMC类).
- 确定了影响更高BMC状态的关键环境预测因素及其最佳值,并捕获了干扰足迹.
结论:
- 机器学习为绘制农业生态潜力的有效工具,克服传统方法的局限性.
- 开发的BMC地图为农业生态规划提供了宝贵的见解,识别了适合和不适合Chagras的地区.
- 了解预测变量有助于优化改善农业生态系统和生物多样性管理的条件.
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