根据正统的插值和机器学习技术对土壤质地分类的评估
Lei Feng1, Umer Khalil2, Bilal Aslam3
1Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China; College of Environment and Ecology, Chongqing University, Chongqing, China.
Environmental research
|December 30, 2023
概括
人工神经网络 (ANN) 在预测巴基斯坦土壤结构方面表现优于其他方法. 虽然ANNs显示出更高的精度,但与土壤质感的相关性仍然低于50%.
科学领域:
- 土壤科学 土壤科学
- 地质统计学 在地质统计学
- 机器学习 机器学习
背景情况:
- 土壤质地对于农业规划和作物产量至关重要.
- 准确地绘制土壤质感地图对于有效的土地管理至关重要.
研究的目的:
- 评估人工神经网络 (ANN),战争,联合战争和反向距离权重 (IDW) 的有效性,以预测土壤质地.
- 为了比较这些插值技术在巴基斯坦拉瓦尔宾迪地区的精度.
主要方法:
- 采集了44个土壤样本 (深度10-15厘米),并使用水表法分析了质地.
- 使用ArcGIS进行空间绘图和使用MATLAB进行土壤纹理评估.
- 使用相关系数 (R),GMER和RMSE等统计指标来评估预测的准确性.
主要成果:
- 人工神经网络 (ANN) 在评估粘土,泥土和沙子的颗粒大小和空间分布方面表现出卓越的有效性和精度,与战争,联合战争和IDW相比.
- 反向距离权重 (IDW) 在测试方法中显示出较低的精度.
- 年度数字实现了不到50%的相关性,这表明有改进的余地.
结论:
- ANNs为土壤质地预测提供了一种有前途的方法,其性能优于传统的地理统计方法.
- 尽管存在相关性的局限性,但测试方法为未来的农业研究和规划提供了可接受的结果.
- 准确的土壤质地图对于优化作物产量和牧场安排至关重要.
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