Soil water erosion susceptibility assessment using deep learning algorithms

Khosravi, Khabat, Rezaie, Fatemeh, Cooper, James R ORCID: 0000-0003-4957-2774, Kalantari, Zahra, Abolfathi, Soroush and Hatamiafkoueieh, Javad
(2023) Soil water erosion susceptibility assessment using deep learning algorithms. Journal of Hydrology, 618. p. 129229.

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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.

Item Type: Article
Uncontrolled Keywords: Soil erosion, Deep learning, Land degradation, CNN, RNN, LSTM
Divisions: Faculty of Science and Engineering > School of Environmental Sciences
Depositing User: Symplectic Admin
Date Deposited: 02 Mar 2023 10:43
Last Modified: 05 Apr 2023 13:19
DOI: 10.1016/j.jhydrol.2023.129229
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