Soil water erosion susceptibility assessment using deep learning algorithms

•Soil water erosion (SWE) is predicted though three kinds of deep learning algorithms.•Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM) are investigated.•All three models had good prediction performance, with RNN being marginally the most superior....

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Veröffentlicht in:Journal of hydrology (Amsterdam) Jg. 618; S. 129229
Hauptverfasser: Khosravi, Khabat, Rezaie, Fatemeh, Cooper, James R., Kalantari, Zahra, Abolfathi, Soroush, Hatamiafkoueieh, Javad
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.03.2023
Schlagworte:
CNN
Deep learning
ecosystems
erosion control
infrastructure
Land degradation
LSTM
neural networks
prediction
RNN
Soil erosion
soil water
water erosion
water quality
watersheds
Deep learning
CNN
Soil erosion
RNN
LSTM
Land degradation
ISSN:0022-1694, 1879-2707, 1879-2707
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Zusammenfassung:•Soil water erosion (SWE) is predicted though three kinds of deep learning algorithms.•Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM) are investigated.•All three models had good prediction performance, with RNN being marginally the most superior.•Elevation was the most effective variable on soil water erosion susceptibility.•Maps of SWE susceptibility revealed that almost 40% of the catchment was highly or very highly susceptible to SWE. 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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0022-1694
1879-2707
1879-2707
DOI:10.1016/j.jhydrol.2023.129229