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Article

Landslide Susceptibility Prediction Using GIS, Analytical Hierarchy Process, and Artificial Neural Network in North-Western Tunisia

by
Manel Mersni
1,*,
Dhekra Souissi
2,
Adnen Amiri
1,
Abdelaziz Sebei
3,
Mohamed Hédi Inoubli
1 and
Hans-Balder Havenith
4
1
Unité de Recherche de la Géophysique Appliquée aux Minerais et Matériaux (URGAMM), Faculté des Sciences de Tunis, Université Tunis El Manar, Tunis 2092, Tunisia
2
Laboratoire des Interactions Plantes, Sols et Environnements LR21ES01, Faculté des Sciences de Tunis, Université Tunis El Manar, Tunis 2092, Tunisia
3
Laboratoire des Ressources Minérales et Environnement LR01ES06, Faculté des Sciences de Tunis, Université de Tunis El Manar, Tunis 2092, Tunisia
4
Laboratoire de Géorisques et Environnement, Département de Géologie, Université de Liège, 4000 Liège, Belgium
*
Author to whom correspondence should be addressed.
Geosciences 2025, 15(8), 297; https://doi.org/10.3390/geosciences15080297
Submission received: 6 May 2025 / Revised: 13 July 2025 / Accepted: 22 July 2025 / Published: 3 August 2025
(This article belongs to the Section Natural Hazards)
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Abstract

Landslide susceptibility modelling represents an efficient approach to enhance disaster management and mitigation strategies. The focus of this paper lies in the development of a landslide susceptibility evaluation in northwestern Tunisia using the Analytical Hierarchy Process (AHP) and Artificial Neural Network (ANN) approaches. The used database covers 286 landslides, including ten landslide factor maps: rainfall, slope, aspect, topographic roughness index, lithology, land use and land cover, distance from streams, drainage density, lineament density, and distance from roads. The AHP and ANN approaches were applied to classify the factors by analyzing the correlation relationship between landslide distribution and the significance of associated factors. The Landslide Susceptibility Index result reveals five susceptible zones organized from very low to very high risk, where the zones with the highest risks are associated with the combination of extreme amounts of rainfall and steep slope. The performance of the models was confirmed utilizing the area under the Relative Operating Characteristic (ROC) curves. The computed ROC curve (AUC) values (0.720 for ANN and 0.651 for AHP) convey the advantage of the ANN method compared to the AHP method. The overlay of the landslide inventory data locations of historical landslides and susceptibility maps shows the concordance of the results, which is in favor of the established model reliability.
Keywords: landslide susceptibility mapping; GIS; AHP; ANN; histogram method; ROC curve landslide susceptibility mapping; GIS; AHP; ANN; histogram method; ROC curve

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MDPI and ACS Style

Mersni, M.; Souissi, D.; Amiri, A.; Sebei, A.; Inoubli, M.H.; Havenith, H.-B. Landslide Susceptibility Prediction Using GIS, Analytical Hierarchy Process, and Artificial Neural Network in North-Western Tunisia. Geosciences 2025, 15, 297. https://doi.org/10.3390/geosciences15080297

AMA Style

Mersni M, Souissi D, Amiri A, Sebei A, Inoubli MH, Havenith H-B. Landslide Susceptibility Prediction Using GIS, Analytical Hierarchy Process, and Artificial Neural Network in North-Western Tunisia. Geosciences. 2025; 15(8):297. https://doi.org/10.3390/geosciences15080297

Chicago/Turabian Style

Mersni, Manel, Dhekra Souissi, Adnen Amiri, Abdelaziz Sebei, Mohamed Hédi Inoubli, and Hans-Balder Havenith. 2025. "Landslide Susceptibility Prediction Using GIS, Analytical Hierarchy Process, and Artificial Neural Network in North-Western Tunisia" Geosciences 15, no. 8: 297. https://doi.org/10.3390/geosciences15080297

APA Style

Mersni, M., Souissi, D., Amiri, A., Sebei, A., Inoubli, M. H., & Havenith, H.-B. (2025). Landslide Susceptibility Prediction Using GIS, Analytical Hierarchy Process, and Artificial Neural Network in North-Western Tunisia. Geosciences, 15(8), 297. https://doi.org/10.3390/geosciences15080297

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