Soil water erosion susceptibility assessment using deep learning algorithms
Soil water erosion susceptibility assessment using deep learning algorithms
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基于深度学习算法的土壤水蚀敏感性评价
DOI：
10.1016/j.jhydrol.2023.129229
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发表时间：
2023-02-10
影响因子：
6.4
通讯作者：
Hatamiafkoueieh, Javad
中科院分区：
文献类型：
--
作者：
Khosravi, Khabat;Rezaie, Fatemeh;Hatamiafkoueieh, Javad
准确评估土壤水侵蚀（SWE）敏感性对于减少土地退化和土壤流失以及减轻侵蚀对生态系统服务、水质、洪水和基础设施的负面影响至关重要。深度学习算法因其高性能和灵活性而在地球科学领域受到关注。然而，人们对这些算法提供快速、廉价和准确的土壤侵蚀敏感性预测的潜力缺乏了解。这项研究首次量化了这种潜力。使用三种深度学习算法（卷积神经网络 (CNN)、循环神经网络 (RNN) 和长短期记忆 (LSTM)）对历史上经历过严重水蚀的伊朗流域进行磁化率空间预测。通过比较它们的预测性能和对驱动地质环境因素的分析，结果表明：（1）海拔是对SWE敏感性最有效的变量； (2) 三个开发的模型都具有良好的预测性能，其中 RNN 稍占优势； (3) SWE敏感性图显示，近40%的流域对SWE高度或非常高度敏感，20%中度敏感，表明该流域迫切需要土壤侵蚀控制。通过这些算法，可以使用现成的数据轻松准确地预测流域的土壤侵蚀敏感性。因此，结果表明，这些模型在数据匮乏的流域（例如本文研究的流域）中具有巨大的应用潜力，特别是在发展中国家，因为这些国家可能缺乏技术建模技能和对流域中发生的侵蚀过程的了解。
Accurate assessment of soil water erosion (SWE) susceptibility is critical for reducing land degradation and soil loss, and for mitigating the negative impacts of erosion on ecosystem services, water quality, flooding and infrastructure. Deep learning algorithms have been gaining attention in geoscience due to their high performance and flexibility. However, an understanding of the potential for these algorithms to provide fast, cheap, and accurate predictions of soil erosion susceptibility is lacking. This study provides the first quantification of this potential. Spatial predictions of susceptibility are made using three deep learning algorithms - Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM) - for an Iranian catchment that has historically experienced severe water erosion. Through a comparison of their predictive performance and an analysis of the driving geo-environmental factors, the results reveal: (1) elevation was the most effective variable on SWE susceptibility; (2) all three developed models had good prediction performance, with RNN being marginally the most superior; (3) maps of SWE susceptibility revealed that almost 40 % of the catchment was highly or very highly susceptible to SWE and 20 % moderately susceptible, indicating the critical need for soil erosion control in this catchment. Through these algorithms, the soil erosion susceptibility of catchments can potentially be predicted accurately and with ease using readily available data. Thus, the results reveal that these models have great potential for use in data poor catchments, such as the one studied here, especially in developing nations where technical modeling skills and understanding of the erosion processes occurring in the catchment may be lacking.