Abstract
Soil erosion is an obstacle in the process of maintaining and restoring the ecological environment in desert steppe landforms. We studied soil erosion in typical desert steppe landform areas in the northern foothills of the Yinshan Mountains. We used differences in altitude maps from different years to construct a dataset for soil erosion research, combined with data on precipitation, human activities, land cover, topography, and soil properties. We employed a convolutional neural network to learn and predict ASED and SEDD in different regions of the Yinshan Mountains. The trained model provided satisfactory prediction performance. We also determined the contributions of different factors and years to soil erosion prediction. Human activities had a greater impact on ASED prediction, while the impact of various factors on SEDD prediction was more balanced. Topography and precipitation factors had a relatively small effect on ASED prediction but played a prominent role in SEDD prediction. This study provides a new approach to exploring the mechanism of different factors on soil erosion in the Yinshan Mountains’ northern foothills.
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Data Availability Statement
The data presented in this study are openly available in Science Data Bank (https://cstr.cn/31253.11.sciencedb.07135) at DOI: 10.57760/sciencedb.07135, (Nguyen et al. 2021), USGS EROS Archive - Digital Elevation - Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global at DOI: 10.5066/F7PR7TFT and ‘AW3D30’ ALOS GLOBAL DIGITAL SURFACE MODEL IN ANT-ARCTICA WITH OTHER OPEN ACCESS DATASETS at DOI: 10.5194/isprs-archives-XLIII-B4-2021-401-2021, (Nguyen and Chen 2021).
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Acknowledgements
This research was supported by Yinshanbeilu Grassland Ecohydrology National Observation and Research Station,China Institute of Water Resources and Hydropower Research, Beijing 100038, China, Grant NO. YSS202111.
Funding
This research was funded by Yinshanbeilu Grassland Eco-hydrology National Observation and Research Station,China Institute of Water Resources and Hydropower Research, Beijing 100038, China, Grant NO. YSS202111.
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Conceptualization, Songan Hou; methodology, Songan Hou and Ying Yu; software, Songan Hou; validation, Ying Yu and Qingyun Wang; investigation, Songan Hou, Ying Yu and Qingyun Wang; data curation, Songan Hou, Ying Yu and Qingyun Wang; writing—original draft preparation, Songan Hou, Ying Yu and Qingyun Wang; writing—review & editing, Songan Hou, Ying Yu and Qingyun Wang; funding acquisition, Ying Yu. All authors have read and agreed to the published version of the manuscript.
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Communicated by: Hassan Babaie.
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Hou, S., Yu, Y. & Wang, Q. Predictive modeling of diverse factors impacting regional soil erosion degree with machine learning. Earth Sci Inform (2024). https://doi.org/10.1007/s12145-024-01329-z
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DOI: https://doi.org/10.1007/s12145-024-01329-z