基于树的算法用于在干旱和半干旱地区空间建模土壤颗粒分布
Osman Abakay1, Miraç Kılıç2, Hikmet Günal1
1Faculty of Agriculture, Department of Soil Science and Plant Nutrition, Harran University, Sanliurfa, Turkey.
Environmental monitoring and assessment
|February 14, 2024
概括
使用基于树的模型 (如XGBoost) 准确地预测土壤质地,并结合遥感数据,有助于精准农业. XGBoost模型在估计粘土,泥和沙子含量方面表现出卓越的性能.
科学领域:
- 数字地土测绘 - - 数字地土测绘
- 精准农业 精准农业 精准农业
- 遥感 遥感 遥感 遥感
背景情况:
- 准确的土壤颗粒大小分布对于有效的土壤管理和保护至关重要.
- 精准农业依赖于详细的土壤数据来优化作物生产.
- 目前用于大面积土壤质地测绘的现有方法存在局限性.
研究的目的:
- 评估各种基于树的机器学习模型的性能,以预测土壤质地.
- 评估遥感数据作为土壤纹理预测中的共变量的实用性.
- 为了确定对土壤质地绘制最有影响力的遥感衍生变量.
主要方法:
- 采用了基于树的模型,包括CART,随机森林 (RF) 和XGBoost.
- 利用遥感植物和土壤指数作为预测变量.
- 在特征选择中应用了Boruta方法.
- 使用来自土耳其东南部622个土壤样本进行训练和测试的模型.
主要成果:
- XGBoost模型在预测粘土 (R2=0.74),泥 (R2=0.71) 和沙子 (R2=0.75) 含量方面取得了最高的准确性.
- 与RF和CART模型相比,XGBoost在RMSE中显示出显著的改善.
- 规范差异植被指数 (NDVI) 和斜率角度是粘土含量的关键预测指标.
结论:
- 基于树的模型,特别是XGBoost,对于粒子大小分布的数字土壤映射是有效的.
- 遥感数据可以显著提高土壤质地预测的准确性.
- 博鲁塔变量选择方法适用于在数字土壤绘图中识别相关的共变量.
关键词:
克莱·克莱·克莱 (Clay Clay) 是一个数据挖掘是一种数据挖掘.数字土壤绘制地图模型模型模型模型模型颗粒大小分布 颗粒大小分布遥感是一种远程传感.沙子 沙子 沙子 沙子这里是Silt的地板.更多相关视频
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