Khosravi, Khabat, Rezaie, Fatemeh, Cooper, James R., Kalantari, Zahra, Abolfathi, Soroush and Hatamiafkoueieh, Javad (2023) Soil water erosion susceptibility assessment using deep learning algorithms. Journal of Hydrology . 129229. doi:10.1016/j.jhydrol.2023.129229 ISSN 0022-1694.

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Abstract

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:	Journal Article
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > Q Science (General) Q Science > QE Geology T Technology > TC Hydraulic engineering. Ocean engineering
Divisions:	Faculty of Science, Engineering and Medicine > Engineering > Engineering
Library of Congress Subject Headings (LCSH):	Soil erosion , Soil erosion -- Simulation methods, Soil erosion prediction, Sedimentation and deposition , Deep learning (Machine learning) , Land degradation
Journal or Publication Title:	Journal of Hydrology
Publisher:	Elsevier BV
ISSN:	0022-1694
Official Date:	2023
Dates:	Date Event 2023 Published 6 February 2023 Available 2 February 2023 Accepted
Article Number:	129229
DOI:	10.1016/j.jhydrol.2023.129229
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Date of first compliant deposit:	9 February 2023
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID NE/S01697X/1 [NERC] Natural Environment Research Council http://dx.doi.org/10.13039/501100000270 NE/V008404/1 [NERC] Natural Environment Research Council http://dx.doi.org/10.13039/501100000270 UNSPECIFIED RUDN University UNSPECIFIED
Open Access Version:	https://www.sciencedirect.com/science/ar...

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