prikaz prve stranice dokumenta Integration of High-Accuracy Geospatial Data and Machine Learning Approaches for Soil Erosion Susceptibility Mapping in the Mediterranean Region: A Case Study of the Macta Basin, Algeria
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Integration of High-Accuracy Geospatial Data and Machine Learning Approaches for Soil Erosion Susceptibility Mapping in the Mediterranean Region: A Case Study of the Macta Basin, Algeria
Sustainability, 15 (2023), 13; 1-23. https://doi.org/10.3390/su151310388
Bouguerra, Hamza; Tachi, Salah Eddine; Bouchehed, Hamza; Gilja, Gordon; Aloui, Nadir; Hasnaoui, Yacine; Aliche, Abdelmalek; Benmamar, Saâdia; Navarro-Pedreño, Jose

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Bouguerra, H., Tachi, S. E., Bouchehed, H., Gilja, G., Aloui, N., Hasnaoui, Y. ... Navarro Pedreño, J. (2023). Integration of High-Accuracy Geospatial Data and Machine Learning Approaches for Soil Erosion Susceptibility Mapping in the Mediterranean Region: A Case Study of the Macta Basin, Algeria. Sustainability, 15. (13), 1-23. doi: 10.3390/su151310388

Bouguerra, Hamza, et al. "Integration of High-Accuracy Geospatial Data and Machine Learning Approaches for Soil Erosion Susceptibility Mapping in the Mediterranean Region: A Case Study of the Macta Basin, Algeria." Sustainability, vol. 15, no. 13, 2023, pp. 1-23. https://doi.org/10.3390/su151310388

Bouguerra, Hamza, Salah Eddine Tachi, Hamza Bouchehed, Gordon Gilja, Nadir Aloui, Yacine Hasnaoui, Abdelmalek Aliche, Saâdia Benmamar and Jose Navarro Pedreño. "Integration of High-Accuracy Geospatial Data and Machine Learning Approaches for Soil Erosion Susceptibility Mapping in the Mediterranean Region: A Case Study of the Macta Basin, Algeria." Sustainability 15, no. 13 (2023): 1-23. https://doi.org/10.3390/su151310388

Bouguerra, H., et al. (2023) 'Integration of High-Accuracy Geospatial Data and Machine Learning Approaches for Soil Erosion Susceptibility Mapping in the Mediterranean Region: A Case Study of the Macta Basin, Algeria', Sustainability, 15(13), pp. 1-23. doi: 10.3390/su151310388

Bouguerra H, Tachi SE, Bouchehed H, Gilja G, Aloui N, Hasnaoui Y, and sur.. Integration of High-Accuracy Geospatial Data and Machine Learning Approaches for Soil Erosion Susceptibility Mapping in the Mediterranean Region: A Case Study of the Macta Basin, Algeria. Sustainability [Internet]. 2023 June 30 [cited 2024 October 09];15(13):1-23. doi: 10.3390/su151310388

H. Bouguerra, et al., "Integration of High-Accuracy Geospatial Data and Machine Learning Approaches for Soil Erosion Susceptibility Mapping in the Mediterranean Region: A Case Study of the Macta Basin, Algeria", Sustainability, vol. 15, no. 13, pp. 1-23, June 2023. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:237:933450. [Accessed: 09 October 2024]

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Title (english)Integration of High-Accuracy Geospatial Data and Machine Learning Approaches for Soil Erosion Susceptibility Mapping in the Mediterranean Region: A Case Study of the Macta Basin, Algeria
AuthorHamza Bouguerra
AuthorSalah Eddine Tachi
AuthorHamza Bouchehed
AuthorGordon Gilja
AuthorNadir Aloui
AuthorYacine Hasnaoui
AuthorAbdelmalek Aliche
AuthorSaâdia Benmamar
AuthorJose Navarro-Pedreño
Author's institutionUniversity of Zagreb
Faculty of Civil Engineering
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Civil Engineering
Abstract (english)
Erosion can have a negative impact on the agricultural sustainability and grazing lands in the Mediterranean area, especially in northern Algeria. It is useful to map the spatial occurrence of erosion and identify susceptible erodible areas on large scale. The main objective of this research was to compare the performance of four machine learning techniques: Categorical boosting, Adaptive boosting, Convolutional Neural Network, and stacking ensemble models to predict the occurrence of erosion in the Macta basin, northwestern Algeria. Several climatologic, morphologic, hydrological, and geological factors based on multi-sources data were elaborated in GIS environment to determine the erosion factors in the studied area. The conditioning factors encompassing rainfall erosivity, slope, aspect, elevation, LULC, topographic wetness index, distance from river, distance from roads, clay mineral ratio, lithology, and geology were derived via the integration of topographic attributes and remote sensing data including Landsat 8 and Sentinel 2 within a GIS framework. The inventory map of soil erosion was created by integrating data from the global positioning system to locate erosion sites, conducting extensive field surveys, and analyzing satellite images obtained from Google Earth through visual interpretation. The dataset was divided randomly into two sets with 60% for training and calibrating and 40% for testing the models. Statistical metrics including sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (ROC) were used to assess the validity of the proposed models. The results revealed that machine learning and deep learning, as well stacking ensemble techniques, showed outstanding performance with accuracy over 98% with sensitivity 0.98 and specificity 0.98. Policy makers and local authorities can utilize the predicted erosion susceptibility maps to promote sustainable use of water and soil conservation and safeguard agricultural activities against potential damage.
Keywords (english)
Languageenglish
Publication typeScientific paper - Other
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleSustainability
Numberingvol. 15, no. 13, pp. 1-23, 10388
e-ISSN2071-1050
DOIhttps://doi.org/10.3390/su151310388
URN:NBNurn:nbn:hr:237:933450
Publication2023-06-30
Document URLhttps://www.mdpi.com/2071-1050/15/13/10388
Type of resourceText
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-09-02 12:53:34