提高数字土壤绘图的准确性,使用表面和地下土壤特征作为连续诊断层
Maryam Osat1, Ahmad Heidari2, Shahrokh Fatehi3
1Horticulture Crop Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran. maryam.ousat@gmail.com.
Environmental monitoring and assessment
|December 19, 2023
概括
这项研究表明,包括地下土壤特征可以提高数字土壤绘图的准确性. 将这些土壤共变量与传统的土壤共变量相结合,可以提高对土壤家族类的预测.
科学领域:
- 数字土壤科学 数字土壤科学
- 地质科学 地质科学
- 机器学习应用 机器学习应用
背景情况:
- 数字土壤绘图通常使用地形,遥感和地质数据.
- 在传统的土壤绘图方法中,地下土壤的特征往往被忽视.
- 了解土壤的空间变化对于准确的土壤分类至关重要.
研究的目的:
- 调查土壤诊断特征作为数字土壤绘图中的共变量的实用性.
- 为了比较不同机器学习算法的性能,使用各种共变量数据集.
- 为了提高在山地景观中的土壤家族类映射的准确性.
主要方法:
- 采用常规共变量 (CC) 和土壤地下和常规共变量 (SCC) 的组合.
- 使用的机器学习算法:随机森林 (RF),多项逻辑回归 (LR) 和C5.0决策树 (C5).
- 使用卡帕系数 (KC) 和整体精度 (OA) 等指标评估模型性能.
主要成果:
- 随机森林模型与SCC数据集实现了最高的性能 (KC = 0.85,OA = 90%).
- 结合土壤共变量,在所有测试的算法中始终提高了模型性能.
- 土壤共变量,斜率和面积被确定为土壤家族类分布的关键预测因素.
结论:
- 土壤诊断特征是改善数字土壤绘图的有价值的共变量.
- 机器学习模型,特别是随机森林,有效地利用这些增强的共变量集.
- 这些发现强调了地下数据对于在复杂地形中准确地分类土壤的重要性.
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