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Torabi Haghighi, A., Darabi, H., Karimidastenaei, Z. et al. Land degradation risk mapping using topographic, human-induced, and geo-environmental variables and machine learning algorithms, for the Pole-Doab watershed, Iran. Environ Earth Sci 80, 1 (2021). https://doi.org/10.1007/s12665-020-09327-2

Land degradation risk mapping using topographic, human-induced, and geo-environmental variables and machine learning algorithms, for the Pole-Doab watershed, Iran

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Author: Torabi Haghighi, Ali1; Darabi, Hamid1; Karimidastenaei, Zahra1;
Organizations: 1Water, Energy and Environmental Engineering Research Unit, University of Oulu, P.O. Box 4300, 90014, Oulu, Finland
2Agricultural Research, Education and Extension Organization (AREEO), Agricultural and Natural Resources Research and Education Center of Markazi Province, P. O. Box: 38188-9-3811, Arak, Iran
3Department of Watershed Management, Sari Agriculture Science and Natural Resources University, P.O. Box 737, Sari, Iran
4Soil Conservation and Watershed Management Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran
5Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 3.6 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe202101111497
Language: English
Published: Springer Nature, 2020
Publish Date: 2021-01-11
Description:

Abstract

Land degradation (LD) is a complex process affected by both anthropogenic and natural driving variables, and its prevention has become an essential task globally. The aim of the present study was to develop a new quantitative LD mapping approach using machine learning techniques, benchmark models, and human-induced and socio-environmental variables. We employed four machine learning algorithms [Support Vector Machine (SVM), Multivariate Adaptive Regression Splines (MARS), Generalized Linear Model (GLM), and Dragonfly Algorithm (DA)] for LD risk mapping, based on topographic (n = 7), human-induced (n = 5), and geo-environmental (n = 6) variables, and field measurements of degradation in the Pole-Doab watershed, Iran. We assessed the performance of different algorithms using receiver operating characteristic, Kappa index, and Taylor diagram. The results revealed that the main topographic, geoenvironmental, and human-induced variable was slope, geology, and land use change, respectively. Assessments of model performance indicated that DA had the highest accuracy and efficiency, with the greatest learning and prediction power in LD risk mapping. In LD risk maps produced using SVM, GLM, MARS, and DA, 19.16%, 19.29%, 21.76%, and 22.40%, respectively, of total area in the Pole-Doab watershed had a very high degradation risk. The results of this study demonstrate that in LD risk mapping for a region, topographic, and geological factors (static conditions) and human activities (dynamic conditions, e.g., residential and industrial area expansion) should be considered together, for best protection at watershed scale. These findings can help policymakers prioritize land and water conservation efforts.

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Series: Environmental earth sciences
ISSN: 1866-6280
ISSN-E: 1866-6299
ISSN-L: 1866-6280
Volume: 80
Article number: 1
DOI: 10.1007/s12665-020-09327-2
OADOI: https://oadoi.org/10.1007/s12665-020-09327-2
Type of Publication: A1 Journal article – refereed
Field of Science: 1171 Geosciences
Subjects:
Funding: Our thanks to the Amol authority for supplying the necessary data (flooded locations and thematic layers) and reports, and to the OLVI Foundation for great financial support for this project. Open access funding provided by University of Oulu including Oulu University Hospital.
Copyright information: © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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