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  4. Application of Deep Learning and Geospatial Analysis in Soil Loss Risk in the Moulouya Watershed, Morocco
 
research article

Application of Deep Learning and Geospatial Analysis in Soil Loss Risk in the Moulouya Watershed, Morocco

Hlal, Mohammed
•
El Monhim, Bilal
•
Chenal, Jérôme  
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April 30, 2025
Water

This study integrates deep learning and geospatial analysis to enhance soil loss estimation in the Moulouya Watershed, a region prone to erosion due to diverse topography and climatic conditions. Traditional models like the Universal Soil Loss Equation (USLE) and its revised version (RUSLE) often fall short in capturing complex environmental interactions, leading to inaccurate soil loss predictions. This research introduces a novel approach using Convolutional Neural Networks (CNNs) combined with Geographic Information Systems (GISs) to improve the precision and spatial resolution of soil loss risk assessments. High-resolution satellite imagery, soil maps, and climatic data were processed to extract critical factors, such as slope, land cover, and rainfall erosivity, which were then fed into the CNN model. The findings revealed that the CNN model outperformed traditional methods, achieving a low Root Mean Square Error (RMSE) of 2.3 and an R-squared value of 0.92, significantly surpassing the USLE and RUSLE models. The resulting high-resolution soil loss maps identified high-risk erosion areas, particularly in the central and eastern regions of the watershed, with soil loss rates exceeding 40 tons/ha/year. These findings demonstrate the superior predictive capabilities of deep learning, offering valuable insights for targeted soil conservation strategies and highlighting the potential of advanced computational techniques to revolutionize environmental modeling.

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Type
research article
DOI
10.3390/w17091351
Author(s)
Hlal, Mohammed

Université Mohammed VI Polytechnique

El Monhim, Bilal

Mohamed I University

Chenal, Jérôme  

EPFL

Munyaka, Baraka Jean-Claude  

EPFL

Azmi, Rida

Université Mohammed VI Polytechnique

Sbai, Abdelkader

Mohamed I University

Cwick, Gary

Southeast Missouri State University

Hichou, Badr Ben

Proactive Smart Cities

Date Issued

2025-04-30

Publisher

AIP Publishing

Published in
Water
Special issue title

Bridging the Gaps: Hydrological Research for Sustainable River Management

Volume

17

Issue

9

Article Number

1351

Start page

1

End page

23

Subjects

deep learning

•

geospatial analysis

•

soil loss estimation

•

convolutional neural networks

•

watershed management

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
CEAT  
Available on Infoscience
May 2, 2025
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/249661
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