Spatial variability of soil water erosion: Comparing empirical and intelligent techniques

IF 8.5 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Ali Golkarian , Khabat Khosravi , Mahdi Panahi , John J. Clague
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引用次数: 8

Abstract

Soil water erosion (SWE) is an important global hazard that affects food availability through soil degradation, a reduction in crop yield, and agricultural land abandonment. A map of soil erosion susceptibility is a first and vital step in land management and soil conservation. Several machine learning (ML) algorithms optimized using the Grey Wolf Optimizer (GWO) metaheuristic algorithm can be used to accurately map SWE susceptibility. These optimized algorithms include Convolutional Neural Networks (CNN and CNN-GWO), Support Vector Machine (SVM and SVM-GWO), and Group Method of Data Handling (GMDH and GMDH-GWO). Results obtained using these algorithms can be compared with the well-known Revised Universal Soil Loss Equation (RUSLE) empirical model and Extreme Gradient Boosting (XGBoost) ML tree-based models. We apply these methods together with the frequency ratio (FR) model and the Information Gain Ratio (IGR) to determine the relationship between historical SWE data and controlling geo-environmental factors at 116 sites in the Noor-Rood watershed in northern Iran. Fourteen SWE geo-environmental factors are classified in topographical, hydro-climatic, land cover, and geological groups. We next divided the SWE sites into two datasets, one for model training (70% of the samples = 81 locations) and the other for model validation (30% of the samples = 35 locations). Finally the model-generated maps were evaluated using the Area under the Receiver Operating Characteristic (AU-ROC) curve. Our results show that elevation and rainfall erosivity have the greatest influence on SWE, while soil texture and hydrology are less important. The CNN-GWO model (AU-ROC = 0.85) outperformed other models, specifically, and in order, SVR-GWO = GMDH-GWO (AUC = 0.82), CNN = GMDH (AUC = 0.81), SVR = XGBoost (AUC = 0.80), and RULSE. Based on the RUSLE model, soil loss in the Noor-Rood watershed ranges from 0 to 2644 t ha–1yr−1.

Abstract Image

土壤水分侵蚀的空间变异性:经验技术与智能技术的比较
水土流失是一种重要的全球性灾害,它通过土壤退化、作物减产和农业用地撂荒影响粮食供应。绘制土壤侵蚀易感性地图是土地管理和土壤保持的第一步,也是至关重要的一步。使用灰狼优化器(GWO)元启发式算法优化的几种机器学习(ML)算法可用于准确映射SWE敏感性。这些优化算法包括卷积神经网络(CNN和CNN- gwo)、支持向量机(SVM和SVM- gwo)和数据处理分组方法(GMDH和GMDH- gwo)。利用这些算法得到的结果可以与著名的修正通用土壤流失方程(RUSLE)经验模型和基于极端梯度增强(XGBoost) ML树的模型进行比较。我们将这些方法与频率比(FR)模型和信息增益比(IGR)一起应用于伊朗北部Noor-Rood流域116个站点的历史SWE数据与控制地质环境因子之间的关系。14个SWE地质环境因子按地形、水文气候、土地覆盖和地质类群进行分类。接下来,我们将SWE站点分为两个数据集,一个用于模型训练(70%的样本= 81个位置),另一个用于模型验证(30%的样本= 35个位置)。最后,使用受试者工作特征曲线下面积(AU-ROC)对模型生成的地图进行评估。结果表明,高程和降雨侵蚀力对SWE的影响最大,土壤质地和水文影响较小。CNN- gwo模型(AU-ROC = 0.85)优于其他模型,具体来说,依次为SVR- gwo = GMDH- gwo (AUC = 0.82)、CNN = GMDH (AUC = 0.81)、SVR = XGBoost (AUC = 0.80)和RULSE。基于RUSLE模型,Noor-Rood流域的土壤流失量在0 ~ 2644t ha-1yr - 1之间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Geoscience frontiers
Geoscience frontiers Earth and Planetary Sciences-General Earth and Planetary Sciences
CiteScore
17.80
自引率
3.40%
发文量
147
审稿时长
35 days
期刊介绍: Geoscience Frontiers (GSF) is the Journal of China University of Geosciences (Beijing) and Peking University. It publishes peer-reviewed research articles and reviews in interdisciplinary fields of Earth and Planetary Sciences. GSF covers various research areas including petrology and geochemistry, lithospheric architecture and mantle dynamics, global tectonics, economic geology and fuel exploration, geophysics, stratigraphy and paleontology, environmental and engineering geology, astrogeology, and the nexus of resources-energy-emissions-climate under Sustainable Development Goals. The journal aims to bridge innovative, provocative, and challenging concepts and models in these fields, providing insights on correlations and evolution.
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