Soil Erosion Quantification using Machine Learning in Sub-Watersheds of Northern Portugal
Protected areas (PA) play an important role in minimizing the effects of soil erosion in watersheds. This study evaluated the performance of machine learning models, specifically support vector machine with linear kernel (SVMLinear), support vector machine with polynomial kernel (SVMPoly), and rando...
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| Format: | Article |
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MDPI AG
2022-12-01
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| Series: | Hydrology |
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| Online Access: | https://www.mdpi.com/2306-5338/10/1/7 |
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| author | Saulo Folharini António Vieira António Bento-Gonçalves Sara Silva Tiago Marques Jorge Novais |
| author_facet | Saulo Folharini António Vieira António Bento-Gonçalves Sara Silva Tiago Marques Jorge Novais |
| author_sort | Saulo Folharini |
| collection | DOAJ |
| description | Protected areas (PA) play an important role in minimizing the effects of soil erosion in watersheds. This study evaluated the performance of machine learning models, specifically support vector machine with linear kernel (SVMLinear), support vector machine with polynomial kernel (SVMPoly), and random forest (RF), on identifying indicators of soil erosion in 761 sub-watersheds and PA in northern Portugal, by using soil erosion by water in Europe, according to the revised universal soil loss equation (RUSLE2015), as target variable. The parameters analyzed were: soil erosion by water in Europe according to the revised universal soil loss equation (RUSLE2015), total burned area of the sub-watershed in the period of 1975-2020, fire recurrence, topographic wetness index (TWI), and the morphometric factors, namely area (A), perimeter (P), length (L), width (W), orientation (O), elongation ratio (Re), circularity ratio (Rc), compactness coefficient (Cc), form factor (Ff), shape factor (Sf), DEM, slope, and curvature. The median coefficient of determination (R<sup>2</sup>) for each model was RF (0.61), SVMpoly (0.68), and SVMLinear (0.54). Regarding the analyzed parameters, those that registered the greatest importance were A, P, L, W, curvature, and burned area, indicating that an analysis which considers morphometric factors, together with soil erosion data affected by water and soil moisture, is an important indicator in the analysis of soil erosion in watersheds. |
| first_indexed | 2024-03-09T12:29:09Z |
| format | Article |
| id | doaj.art-dfd09254e1414ceab0602ad518e35c47 |
| institution | Directory Open Access Journal |
| issn | 2306-5338 |
| language | English |
| last_indexed | 2024-03-09T12:29:09Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Hydrology |
| spelling | doaj.art-dfd09254e1414ceab0602ad518e35c472023-11-30T22:31:23ZengMDPI AGHydrology2306-53382022-12-01101710.3390/hydrology10010007Soil Erosion Quantification using Machine Learning in Sub-Watersheds of Northern PortugalSaulo Folharini0António Vieira1António Bento-Gonçalves2Sara Silva3Tiago Marques4Jorge Novais5Communication and Society Research Centre, University of Minho, 4704-553 Guimarães, PortugalCommunication and Society Research Centre, University of Minho, 4704-553 Guimarães, PortugalCommunication and Society Research Centre, University of Minho, 4704-553 Guimarães, PortugalCommunication and Society Research Centre, University of Minho, 4704-553 Guimarães, PortugalCommunication and Society Research Centre, University of Minho, 4704-553 Guimarães, PortugalCommunication and Society Research Centre, University of Minho, 4704-553 Guimarães, PortugalProtected areas (PA) play an important role in minimizing the effects of soil erosion in watersheds. This study evaluated the performance of machine learning models, specifically support vector machine with linear kernel (SVMLinear), support vector machine with polynomial kernel (SVMPoly), and random forest (RF), on identifying indicators of soil erosion in 761 sub-watersheds and PA in northern Portugal, by using soil erosion by water in Europe, according to the revised universal soil loss equation (RUSLE2015), as target variable. The parameters analyzed were: soil erosion by water in Europe according to the revised universal soil loss equation (RUSLE2015), total burned area of the sub-watershed in the period of 1975-2020, fire recurrence, topographic wetness index (TWI), and the morphometric factors, namely area (A), perimeter (P), length (L), width (W), orientation (O), elongation ratio (Re), circularity ratio (Rc), compactness coefficient (Cc), form factor (Ff), shape factor (Sf), DEM, slope, and curvature. The median coefficient of determination (R<sup>2</sup>) for each model was RF (0.61), SVMpoly (0.68), and SVMLinear (0.54). Regarding the analyzed parameters, those that registered the greatest importance were A, P, L, W, curvature, and burned area, indicating that an analysis which considers morphometric factors, together with soil erosion data affected by water and soil moisture, is an important indicator in the analysis of soil erosion in watersheds.https://www.mdpi.com/2306-5338/10/1/7soil erosionsub-watershedsmachine learningburned areasprotected areas |
| spellingShingle | Saulo Folharini António Vieira António Bento-Gonçalves Sara Silva Tiago Marques Jorge Novais Soil Erosion Quantification using Machine Learning in Sub-Watersheds of Northern Portugal Hydrology soil erosion sub-watersheds machine learning burned areas protected areas |
| title | Soil Erosion Quantification using Machine Learning in Sub-Watersheds of Northern Portugal |
| title_full | Soil Erosion Quantification using Machine Learning in Sub-Watersheds of Northern Portugal |
| title_fullStr | Soil Erosion Quantification using Machine Learning in Sub-Watersheds of Northern Portugal |
| title_full_unstemmed | Soil Erosion Quantification using Machine Learning in Sub-Watersheds of Northern Portugal |
| title_short | Soil Erosion Quantification using Machine Learning in Sub-Watersheds of Northern Portugal |
| title_sort | soil erosion quantification using machine learning in sub watersheds of northern portugal |
| topic | soil erosion sub-watersheds machine learning burned areas protected areas |
| url | https://www.mdpi.com/2306-5338/10/1/7 |
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