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简单的RNN和GRU深度学习模型的解释性用于绘制土地易受沟侵蚀的地图

Hamid Gholami¹, Aliakbar Mohammadifar¹, Shahram Golzari²

¹Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran.

The Science of the total environment

|September 11, 2023

概括

深度学习模型准确地绘制了伊朗峡谷侵蚀易感性的地图. 简单的循环神经网络 (RNN) 模型表现优于封闭的循环单元 (GRU),识别了诸如土壤泥含量和植被覆盖等关键因素,用于流域管理.

科学领域:

环境科学环境科学
地质科学地质科学
计算机科学计算机科学

背景情况:

峡谷侵蚀对流域内的土壤,水和植被资源构成重大威胁.
对沟侵蚀危险的空间绘制对于有效的缓解策略至关重要.
先进的学习技术,特别是深度学习 (DL),在空间绘图和预测侵蚀危险方面表现出很高的能力.

研究的目的:

应用和比较两个深度学习模型,简单的循环神经网络 (RNN) 和封闭的循环单元 (GRU),用于绘制土地易受峡谷侵蚀的地图.
利用可解释性方法 (SHAP,CP-PD,PFI) 来了解影响沟侵蚀预测的因素.
为伊朗南部沙米尔-米纳布平原的峡谷侵蚀危险生成准确的空间地图.

主要方法:

应用简单的RNN和GRU深度学习模型来绘制峡谷侵蚀易感性的地图.
使用Boruta算法识别了七个关键控制特征:土壤质量密度,粘土含量,海拔,土地用途类型,植被覆盖,沙子含量和泥含量.
采用SHaply添加式解释 (SHAP),相对等和部分依赖 (CP-PD) 配置文件,以及模型可解释性的变特征重要性 (PFI).

主要成果:

与GRU模型 (KS统计 = 66.6) 相比,简单的RNN模型显示出更高的性能 (KS统计 = 91.6).
RNN模型将平原分为危险级别:7.4%非常低,14.5%低,18.9%中度,31.2%高,28%非常高.
可解释性方法强调了土壤泥含量,植被覆盖 (NDVI) 和土地使用类型是影响模型预测最有影响力的因素.

结论:

深度学习模型,特别是简单的RNN,是生成峡谷侵蚀危险的准确空间地图的有效工具.
可解释性技术提高了DL模型输出的理解,有助于识别关键的侵蚀驱动因素.
这些发现支持可持续的流域管理,通过提供详细的危险地图和对侵蚀过程的洞察力.

关键词:

博鲁塔特征选择算法 Boruta特征选择算法峡谷侵蚀的危险峡谷侵蚀的危险解释方法解释方法伊朗伊朗伊朗伊朗简单的RNN深度学习模型

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