Benabbas, C.: Evolution Mio-Plio-Quaternaire Des Bassins Continentaux De L'Algerie Nord Orientale: Apport De La Photogeologie Et Analyse Morphostructurale, University Mentouri, Constantine, 242 pp., https://doi.org/10.13140/RG.2.2.11674.25280, 2006.
Benaissa, A. and Bellouche, M. A.: Geotechnical properties of some landslide-prone geological formations in the urban area of Constantine (Alqeria) [Propriétés géotechniques de quelques formations géologiques propices aux glissements de terrains dans l'agglomération de Constantine (Algérie)], Bulletin of Engineering Geology and the Environment, 57, 301–310, 1999.
Bornaetxea, T., Rossi, M., Marchesini, I., and Alvioli, M.: Effective surveyed area and its role in statistical landslide susceptibility assessments, Nat. Hazards Earth Syst. Sci., 18, 2455–2469, https://doi.org/10.5194/nhess-18-2455-2018, 2018.
Bougdal, R., Belhai, D., and Antoine, P.: Géologie détaillée de la ville de Constantine et ses alentours: une donnée de base pour l'étude des glissements de terrain, Bull. Serv. Géol. Nat., 18, 161–187, 2007.
Bourenane, H. and Bouhadad, Y.: Impact of Land use Changes on Landslides Occurrence in Urban Area: The Case of the Constantine City (NE Algeria), Geotechnical and Geological Engineering, 39, https://doi.org/10.1007/s10706-021-01768-1, 2021.
Bourenane, H., Bouhadad, Y., Guettouche, M. S., and Braham, M.: GIS-based landslide susceptibility zonation using bivariate statistical and expert approaches in the city of Constantine (Northeast Algeria), Bulletin of Engineering Geology and the Environment, 74, 337–355, https://doi.org/10.1007/s10064-014-0616-6, 2015.
Breiman, L.: Random Forests, Machine Learning, 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Caniani, D., Pascale, S., Sdao, F., and Sole, A.: Neural networks and landslide susceptibility: A case study of the urban area of Potenza, Nat. Hazards, 45, 55–72, https://doi.org/10.1007/s11069-007-9169-3, 2008.
Carrión-Mero, P., Briones-Bitar, J., Morante-Carballo, F., Stay-Coello, D., Blanco-Torrens, R., and Berrezueta, E.: Evaluation of slope stability in an urban area as a basis for territorial planning: A case study, Applied Sciences (Switzerland), 11, https://doi.org/10.3390/app11115013, 2021.
Chen, T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, 13–17 August, https://doi.org/10.1145/2939672.2939785, 2016.
Chen, Y.-C.: A tutorial on kernel density estimation and recent advances, Biostatistics and Epidemiology, 1, 161–187, https://doi.org/10.1080/24709360.2017.1396742, 2017.
Chen, Z. and Wang, G.: Comparison of empirically-based and physically-based analyses of coseismic landslides: A case study of the 2016 Kumamoto earthquake, Soil Dyn. Earthq. Eng., 172, 108009, https://doi.org/10.1016/j.soildyn.2023.108009, 2023.
Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger, J. C., Skakun, S. V., and Justice, C.: The Harmonized Landsat and Sentinel-2 surface reflectance data set, Remote Sens. Environ., 219, 145–161, https://doi.org/10.1016/j.rse.2018.09.002, 2018.
Felicísimo, Á. M., Cuartero, A., Remondo, J., and Quirós, E.: Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: A comparative study, Landslides, 10, 175–189, https://doi.org/10.1007/s10346-012-0320-1, 2013.
Frazier, P. I.: A Tutorial on Bayesian Optimization, arXiv:1807.02811,
https://arxiv.org/abs/1807.02811 (last access: 1 February 2025), 2018.
Gardner, M. W. and Dorling, S. R.: Artificial neural networks (the multilayer perceptron) – a review of applications in the atmospheric sciences, Atmos. Environ., 32, 2627–2636, https://doi.org/10.1016/S1352-2310(97)00447-0, 1998.
Gerds, T. A., Andersen, P. K., and Kattan, M. W.: Calibration plots for risk prediction models in the presence of competing risks, Statistics in Medicine, 33, 3191–3203, https://doi.org/10.1002/sim.6152, 2014.
Guemache, M. A., Chatelain, J. L., Machane, D., Benahmed, S., and Djadia, L.: Failure of landslide stabilization measures: The Sidi Rached viaduct case (Constantine, Algeria), Journal of African Earth Sciences, 59, 349–358, https://doi.org/10.1016/j.jafrearsci.2011.01.005, 2011.
Hadji, R., Boumazbeur, A. errahmane, Limani, Y., Baghem, M., Chouabi, A. el M., and Demdoum, A.: Geologic, topographic and climatic controls in landslide hazard assessment using GIS modeling: A case study of Souk Ahras region, NE Algeria, Quatern. Int., 302, 224–237, https://doi.org/10.1016/j.quaint.2012.11.027, 2013.
He, H., Dong, X., Du, S., Guo, H., Yan, Y., and Chen, G.: Study on the Stability of Cut Slopes Caused by Rural Housing Construction in Red Bed Areas: A Case Study of Wanyuan City, China, Sustainability (Switzerland), 16, https://doi.org/10.3390/su16031344, 2024.
Huang, W., Ding, M., Li, Z., Yu, J., Ge, D., Liu, Q., and Yang, J.: Landslide susceptibility mapping and dynamic response along the Sichuan-Tibet transportation corridor using deep learning algorithms, Catena, 222, 106866, https://doi.org/10.1016/j.catena.2022.106866, 2023.
Hungr, O., Leroueil, S., and Picarelli, L.: The Varnes classification of landslide types, an update, Landslides, 11, 167–194, https://doi.org/10.1007/s10346-013-0436-y, 2014.
Islam, M. A., Arrafi, M. A., Peas, M. H., Hossain, T., Hasan, M. M., Murshed, S., and Tania, M. J.: Predicting urban landslides in the hilly regions of Bangladesh leveraging a hybrid machine learning model and CMIP6 climate projections, Geosystems and Geoenvironment, 4, 100354, https://doi.org/10.1016/j.geogeo.2025.100354, 2025.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Lui, T.-Y.: LightGBM: A Highly Efficient Gradient Boosting Decision Tree, in: Advances in Neural Information Processing Systems, Curran Associates, Inc., 30,
https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf (last access: 1 February 2025), 2017.
El Kechebour, B.: Relation between Stability of Slope and the Urban Density: Case Study, Procedia Engineering, 114, 824–831, https://doi.org/10.1016/j.proeng.2015.08.034, 2015.
Kyriazos, T. and Poga, M.: Dealing with Multicollinearity in Factor Analysis: The Problem, Detections, and Solutions, Open Journal of Statistics, 13, 404–424, https://doi.org/10.4236/ojs.2023.133020, 2023.
Liu, B., Guo, H., Li, J., Ke, X., and He, X.: Application and interpretability of ensemble learning for landslide susceptibili
ty mapping along the Three Gorges Reservoir area, China, Nat. Hazards, 120, 4601–4632, https://doi.org/10.1007/s11069-023-06374-3, 2024.
Luo, J., Zhao, Z., Li, W., Huang, L., and Zhao, W.: Landslide hazard assessment of an urban agglomeration in central Guizhou Province based on an information value method and SVM, bagging, DNN algorithm, Scientific Reports, 15, 1–15, https://doi.org/10.1038/s41598-025-86258-7, 2025.
Lv, J., Zhang, R., Shama, A., Hong, R., He, X., Wu, R., Bao, X., and Liu, G.: Exploring the spatial patterns of landslide susceptibility assessment using interpretable Shapley method: Mechanisms of landslide formation in the Sichuan-Tibet region, J. Environ. Manage., 366, https://doi.org/10.1016/j.jenvman.2024.121921, 2024.
Ma, H. and Wang, F.: Inventory of shallow landslides triggered by extreme precipitation in July 2023 in Beijing, China, Scientific Data, 11, 1083, https://doi.org/10.1038/s41597-024-03901-0, 2024.
Manchar, N., Benabbas, C., Hadji, R., Bouaicha, F., and Grecu, F.: Landslide susceptibility assessment in constantine region (NE Algeria) by means of statistical models, Studia Geotechnica et Mechanica, 40, 208–219, https://doi.org/10.2478/sgem-2018-0024, 2018.
Mas, J. F., Filho, B. S., Pontius, R. G., Gutiérrez, M. F., and Rodrigues, H.: A suite of tools for ROC analysis of spatial models, ISPRS International Journal of Geo-Information, 2, 869–887, https://doi.org/10.3390/ijgi2030869, 2013.
Matougui, Z. and Zouidi, M.: A temporal perspective on the reliability of wildfire hazard assessment based on machine learning and remote sensing data, Earth Science Informatics, 18, https://doi.org/10.1007/s12145-024-01501-5, 2025.
Matougui, Z., Djerbal, L., and Bahar, R.: A comparative study of heterogeneous and homogeneous ensemble approaches for landslide susceptibility assessment in the Djebahia region, Algeria, Environ. Sci. Pollut. R, https://doi.org/10.1007/s11356-023-26247-3, 2023.
Meena, S. R., Puliero, S., Bhuyan, K., Floris, M., and Catani, F.: Assessing the importance of conditioning factor selection in landslide susceptibility for the province of Belluno (region of Veneto, northeastern Italy), Nat. Hazards Earth Syst. Sci., 22, 1395–1417, https://doi.org/10.5194/nhess-22-1395-2022, 2022.
Mezerreg, N. E. H., Kessasra, F., Bouftouha, Y., Bouabdallah, H., Bollot, N., Baghdad, A., and Bougdal, R.: Integrated geotechnical and geophysical investigations in a landslide site at Jijel, Algeria, Journal of African Earth Sciences, 160, 103633, https://doi.org/10.1016/j.jafrearsci.2019.103633, 2019.
Mezhoud, L. and Benazzouz, M.-T.: Évaluation de la susceptibilité à l'aléa «glissement de terrain» par l'utilisation de l'outil SIG: application à la ville de Constantine (Algérie), Sciences & Technologie D, 47, 91–103, 2018.
Mounia, B., Merzoug, B., Chaouki, B., and Djaouza, A. A.: Physico-Chemical Characterization of Limestones and Sandstones in a Complex Geological Context, ExampleNorth-East Constantine: Preliminary Results, International Journal of Engineering and Technology, 114–118, https://doi.org/10.7763/ijet.2013.v5.523, 2013.
O'Brien, R. M.: A caution regarding rules of thumb for variance inflation factors, Quality and Quantity, 41, 673–690, https://doi.org/10.1007/s11135-006-9018-6, 2007.
OpenStreetMap contributors: OpenStreetMap – Road network data (2019),
https://www.openstreetmap.org (last access: 19 November 2025), 2019.
Pascale, S., Sdao, F., and Sole, A.: A model for assessing the systemic vulnerability in landslide prone areas, Nat. Hazards Earth Syst. Sci., 10, 1575–1590, https://doi.org/10.5194/nhess-10-1575-2010, 2010.
Pascale, S., Parisi, S., Mancini, A., Schiattarella, M., Conforti, M., Sole, A., Murgante, B., and Sdao, F.: Landslide susceptibility mapping using artificial neural network in the urban area of Senise and San Costantino Albanese (Basilicata, Southern Italy), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7974 LNCS, 473–488, https://doi.org/10.1007/978-3-642-39649-6_34, 2013.
Pham, B. T., Tien Bui, D., Prakash, I., and Dholakia, M. B.: Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS, Catena, 149, 52–63, https://doi.org/10.1016/j.catena.2016.09.007, 2017.
Pham, B. T., Nguyen, M. D., Bui, K. T. T., Prakash, I., Chapi, K., and Bui, D. T.: A novel artificial intelligence approach based on Multi-layer Perceptron Neural Network and Biogeography-based Optimization for predicting coefficient of consolidation of soil, Catena, 173, 302–311, https://doi.org/10.1016/j.catena.2018.10.004, 2019.
Saha, S., Saha, A., Hembram, T. K., Pradhan, B., and Alamri, A. M.: Evaluating the performance of individual and novel ensemble of machine learning and statistical models for landslide susceptibility assessment at Rudraprayag district of Garhwal Himalaya, Applied Sciences (Switzerland), 10, https://doi.org/10.3390/app10113772, 2020.
Schlögl, M., Richter, G., Avian, M., Thaler, T., Heiss, G., Lenz, G., and Fuchs, S.: On the nexus between landslide susceptibility and transport infrastructure – an agent-based approach, Nat. Hazards Earth Syst. Sci., 19, 201–219, https://doi.org/10.5194/nhess-19-201-2019, 2019.
Sun, D., Wang, J., Wen, H., Ding, Y. K., and Mi, C.: Landslide susceptibility mapping (LSM) based on different boosting and hyperparameter optimization algorithms: A case of Wanzhou District, China, Journal of Rock Mechanics and Geotechnical Engineering, https://doi.org/10.1016/j.jrmge.2023.09.037, 2024.
Tang, R. X., Kulatilake, P. H. S. W., Yan, E. C., and Cai, J. Sen: Evaluating landslide susceptibility based on cluster analysis, probabilistic methods, and artificial neural networks, Bulletin of Engineering Geology and the Environment, 79, 2235–2254, https://doi.org/10.1007/s10064-019-01684-y, 2020.
Tanyu, B. F., Abbaspour, A., Alimohammadlou, Y., and Tecuci, G.: Landslide susceptibility analyses using Random Forest, C4.5, and C5.0 with balanced and unbalanced datasets, Catena, 203, 105355, https://doi.org/10.1016/j.catena.2021.105355, 2021.
Varnes, D. J.: Landslide hazard zonation: a review of principles and practice, Natural Hazards, United Nations Educational, Scientific and Cultural Organization, Paris, 3, 1–63,
https://trid.trb.org/View/281932 (last access: 1 February 2025), 1984.
Yang, C., Liu, L. L., Huang, F., Huang, L., and Wang, X. M.: Machine learning-based landslide susceptibility assessment with optimized ratio of landslide to non-landslide samples, Gondwana Res., 123, 198–216, https://doi.org/10.1016/j.gr.2022.05.012, 2023.
Zehra, K. T., Kursat, O. A., and Candan, G.: Performance Comparison of Landslide Susceptibility Maps Derived from Logistic Regression and Random Forest Models in the Bolaman Basin, Türkiye, Nat. Hazards Rev., 25, 4023054, https://doi.org/10.1061/NHREFO.NHENG-1771, 2024.