Analysis of Landslide Vulnerability in Chefchaouen Using a Statistical Approach SIG
DOI:
https://doi.org/10.48346/IMIST.PRSM/ajlp-gs.v7i6.53081Keywords:
Landslides, Approche statique, SIG, Vulnerability, Chefchaouen, MarocAbstract
Context and background
In the Rif region of northern Morocco, terrain instabilities pose significant social, economic, and environmental challenges. The city of Chefchaouen, located in the heart of the Rif mountain range, is particularly prone to landslides and slope instability due to its complex geology, geographic location, and periodic intense rainfall. These factors frequently trigger landslides, causing damage and disruption.
Goal and Objectives
The primary goal is to create a landslide susceptibility map to support effective land-use management and risk assessment. The objectives include:
Identifying and analyzing key factors contributing to landslides. Utilizing landslide susceptibility mapping to guide risk management strategies.Minimizing the socio-economic impacts of terrain instabilities in Chefchaouen.
Methodology
A static method was used to assess nine conditioning factors for landslides: geology, slope, land use, altitude, aspect, proximity to roads and rivers, soil type, and precipitation. Data collection included a landslide inventory compiled by the urban municipality of Chefchaouen, high-resolution satellite imagery, and field verifications, identifying 400 instances of terrain instability. The dataset was split into two parts: 70% for training a predictive model and 30% for testing the model's performance using the AUC (Area Under Curve) metric. Slope exposure (orientation of a slope relative to geographic north) emerged as the factor with the highest prediction rate, at 8.711.
Results
The study produced a landslide susceptibility map that serves as a vital decision-making tool. This map highlights areas at risk and supports the development of risk management and land-use planning strategies, helping to mitigate the socio-economic consequences of terrain instability in Chefchaouen.
Context and background
In the Rif region of northern Morocco, terrain instabilities pose significant social, economic, and environmental challenges. The city of Chefchaouen, located in the heart of the Rif mountain range, is particularly prone to landslides and slope instability due to its complex geology, geographic location, and periodic intense rainfall. These factors frequently trigger landslides, causing damage and disruption.
Goal and Objectives
The primary goal is to create a landslide susceptibility map to support effective land-use management and risk assessment. The objectives include:
Identifying and analyzing key factors contributing to landslides. Utilizing landslide susceptibility mapping to guide risk management strategies.Minimizing the socio-economic impacts of terrain instabilities in Chefchaouen.
Methodology
A static method was used to assess nine conditioning factors for landslides: geology, slope, land use, altitude, aspect, proximity to roads and rivers, soil type, and precipitation. Data collection included a landslide inventory compiled by the urban municipality of Chefchaouen, high-resolution satellite imagery, and field verifications, identifying 400 instances of terrain instability. The dataset was split into two parts: 70% for training a predictive model and 30% for testing the model's performance using the AUC (Area Under Curve) metric. Slope exposure (orientation of a slope relative to geographic north) emerged as the factor with the highest prediction rate, at 8.711.
Results
The study produced a landslide susceptibility map that serves as a vital decision-making tool. This map highlights areas at risk and supports the development of risk management and land-use planning strategies, helping to mitigate the socio-economic consequences of terrain instability in Chefchaouen.
Context and background
In the Rif region of northern Morocco, terrain instabilities pose significant social, economic, and environmental challenges. The city of Chefchaouen, located in the heart of the Rif mountain range, is particularly prone to landslides and slope instability due to its complex geology, geographic location, and periodic intense rainfall. These factors frequently trigger landslides, causing damage and disruption.
Goal and Objectives
The primary goal is to create a landslide susceptibility map to support effective land-use management and risk assessment. The objectives include:
Identifying and analyzing key factors contributing to landslides. Utilizing landslide susceptibility mapping to guide risk management strategies.Minimizing the socio-economic impacts of terrain instabilities in Chefchaouen.
Methodology
A static method was used to assess nine conditioning factors for landslides: geology, slope, land use, altitude, aspect, proximity to roads and rivers, soil type, and precipitation. Data collection included a landslide inventory compiled by the urban municipality of Chefchaouen, high-resolution satellite imagery, and field verifications, identifying 400 instances of terrain instability. The dataset was split into two parts: 70% for training a predictive model and 30% for testing the model's performance using the AUC (Area Under Curve) metric. Slope exposure (orientation of a slope relative to geographic north) emerged as the factor with the highest prediction rate, at 8.711.
Results
The study produced a landslide susceptibility map that serves as a vital decision-making tool. This map highlights areas at risk and supports the development of risk management and land-use planning strategies, helping to mitigate the socio-economic consequences of terrain instability in Chefchaouen.
References
Ait Brahim, L., Bousta, M., Jemmah, I. A., El Hamdouni, I., ElMahsani, A., Abdelouafi, A., Sossey Alaoui, F., & Lallout, I. (2018). Landslide susceptibility mapping using AHP method and GIS in the peninsula of Tangier (Rif-northern Morocco). MATEC Web of Conferences, 149,02084. https://doi.org/10.1051/matecconf/201814902084
Anbalagan, R. (1992). Landslide hazard evaluation and zonation mapping in mountainous terrain. Engineering geology, 32(4), 269-277.
Ayalew, L., & Yamagishi, H. (2005). The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology, 65(1-2), 15-31.
Ayalew, L., Yamagishi, H., & Ugawa, N. (2004). Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides, 1, 73-81.
Beguería, S. (2006). Validation and evaluation of predictive models in hazard assessment and risk management. Natural Hazards, 37, 315-329. https://doi.org/10.1007/s11069-005-5182-6
Bijukchhen, S. M., Kayastha, P., & Dhital, M. R. (2013). A comparative evaluation of heuristic and bivariate statistical modelling for landslide susceptibility mappings in Ghurmi–Dhad Khola, east Nepal. Arabian Journal of Geosciences, 6, 2727-2743. https://doi.org/10.1007/s12517-012-0569-7
Brabb, E. E. (1984). Innovative approaches to landslide hazard and risk mapping.
https://api.semanticscholar.org/CorpusID:134088701
Chung, C.-J., & Fabbri, A. (1999). Probabilistic prediction models for landslide hazard mapping. Photogrammetric Engineering and Remote Sensing, 65, 1389 1399.
Chung, C.-J. F., & Fabbri, A. G. (2003). Validation of Spatial Prediction Models for Landslide Hazard Mapping. Natural Hazards, 30(3), 451 472. https://doi.org/10.1023/B:NHAZ.0000007172.62651.2b
Chung, C.-J., & Fabbri, A. G. (2008). Predicting landslides for risk analysis—Spatial models tested by a cross-validation technique. GIS Technology and Models for Assessing Landslide Hazard and Risk, 94(3),438 452. https://doi.org/10.1016/j.geomorph.2006.12.036
Dai, F. C., & Lee, C. F. (2002). Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology, 42(3), 213 228.
Dai, F. C., & Lee, C. F. (2002). Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology, 42(3), 213 228. https://doi.org/10.1016/S0169-555X(01)00087-3
El Kharim, Y. (2012). Rasgos geológicos de la inestabilidad de laderas en la región de Tetuán (Rif septentrional, Marruecos). Geological Features of the Slope Instability in Tetouan Region (the Northern Rif, Morocco).
El Kharim, Y., Bounab, A., Ilias, O., Hilali, F., & Ahniche, M. (2021). Landslides in the urban and suburban perimeter of Chefchaouen (Rif, Northern Morocco): Inventory and case study. Natural Hazards, 107(1), 355 373. https://doi.org/10.1007/s11069-021-04586-z
Elmoulat, M., Brahim, L. A., Elmahsani, A., Abdelouafi, A., & Mastere, M. (2021). Mass movements susceptibility mapping by using heuristic approach. Case study: province of Tétouan (North of Morocco). Geoenvironmental Disasters, 8, 1-19. https://doi.org/10.1186/s40677-021-00192-0
Ghosh, S., Carranza, E. J. M., van Westen, C. J., Jetten, V. G., & Bhattacharya, D. N. (2011). Selecting and weighting spatial predictors for empirical modeling of landslide susceptibility in the Darjeeling Himalayas (India). Geomorphology, 131(1), 35 56. https://doi.org/10.1016/j.geomorph.2011.04.019
Gulácsi, A., & Kovács, F. (2015). Drought Monitoring With Spectral Indices Calculated From Modis Satellite Images In Hungary. Journal of Environmental Geography, 8(3 4), Article 3 4. https://doi.org/10.1515/jengeo-2015-0008
Guzzetti, F., Carrara, A., Cardinali, M., & Reichenbach, P. (1999). Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology, 31(1), 181 216. https://doi.org/10.1016/S0169-555X(99)00078-1
Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., & Ardizzone, F. (2005). Probabilistic landslide hazard assessment at the basin scale. Geomorphology, 72(1), 272 299.
https://doi.org/10.1016/j.geomorph.2005.06.002
Han, S., Liu, B., Fan, X., Feng, T., Yang, J., Zhou, Z., Gong, H., & Luo, J. (2023). A new approach for landslide susceptibility assessments based on KDE-MDBN: A case study from mountainous regions impacted by the Wenchuan earthquake, China. Environmental Modelling & Software, 167, 105759. https://doi.org/10.1016/j.envsoft.2023.105759
Haque, U., Da Silva, P. F., Devoli, G., Pilz, J., Zhao, B., Khaloua, A., ... & Glass, G. E. (2019). The human cost of global warming: Deadly landslides and their triggers (1995–2014). Science of the Total Environment, 682, 673684. https://doi.org/10.1016/j.scitotenv.2019.03.415
Lee, S., & Min, K. (2001). Statistical analysis of landslide susceptibility at Yongin, Korea. Environmental Geology, 40(9), 1095 1113. https://doi.org/10.1007/s002540100310
Lepore, C., Kamal, S. A., Shanahan, P., & Bras, R. L. (2012). Rainfall-induced landslide susceptibility zonation of Puerto Rico. Environmental Earth Sciences, 66, 1667-1681. https://doi.org/10.1007/s12665-011-0976-1
Li, Y., Yang, X., Hu, X., Wan, L., & Ma, E. (2024). Mechanisms of rainfall-induced landslides and interception dynamic response: A case study of the Ni changgou landslide in Shimian, China. Scientific Reports.
Martha, T. R., van Westen, C. J., Kerle, N., Jetten, V., & Vinod Kumar, K. (2013). Landslide hazard and risk assessment using semi-automatically created landslide inventories. Geomorphology, 184, 139 150. https://doi.org/10.1016/j.geomorph.2012.12.001
Mastere, M. (2011). la susceptibilite aux mouvements de terrain dans la province de chefchaouen (Rif Central, Maroc).
Mastere, M., Lanoë, B., van-Vliet, Brahim, L. A., & Moulat, M. E. (2015). A linear indexing approach to mass movements susceptibility mapping. The case of the Chefchaouen province (Morocco). Rev. Int. Géomatique, 25, 245 265.
Mezughi, T. H., Akhir, J. B. M., Rafek, A. G. M., & Abdullah, I. (2011). Landslide Susceptibility Assessment using Frequency Ratio Model Applied to an Area along the E-W Highway (Gerik-Jeli). American Journal of Environmental Sciences, 7, 43 50.
Nold, M., Uttinger, J., & Wildi, W. (1981). Géologie de la Dorsale calcaire entre Tétouan et Assifane (Rif interne, Maroc) (Vol. 300). Editions du Service géologique du Maroc.
Pateau, M. (2014). De l’aléa au risque naturel: Cas de la région Tanger-Tétouan (Rif, Maroc). Geo-Eco-Trop, 38, 23 32.
Pokharel, B., & Thapa, P. B. (2019). Landslide susceptibility in Rasuwa District of central Nepal after the 2015 Gorkha Earthquake. Journal of Nepal Geological Society, 59, 79-88. https://doi.org/10.3126/jngs.v59i0.24992
Pradhan, P., & Dahal, B. K. (2023). Landslide Susceptibility Analysis of Jugal Rural Municipality, Sindhupalchok. Journal of Science and Engineering, 10, 49 57.
https://doi.org/10.3126/jsce.v10i1.61019
Raghuvanshi, T. K., Ibrahim, J., & Ayalew, D. (2014). Slope stability susceptibility evaluation parameter (SSEP) rating scheme–an approach for landslide hazard zonation. Journal of African Earth Sciences, 99, 595 612.
Roccati, A., Paliaga, G., Luino, F., Faccini, F., & Turconi, L. (2021). GIS-Based Landslide Susceptibility Mapping for Land Use Planning and Risk Assessment. Land, 10(2), 162.
https://doi.org/10.3390/land10020162
Rossi, M., Luciani, S., Valigi, D., Kirschbaum, D., Brunetti, M. T., Peruccacci, S., & Guzzetti, F. (2017). Statistical approaches for the definition of landslide rainfall thresholds and their uncertainty using rain gauge and satellite data. Geomorphology, 285, 16 27.
https://doi.org/10.1016/j.geomorph.2017.02.001
Rossi, M., & Reichenbach, P. (2016). LAND-SE: A software for statistically based landslide susceptibility zonation, version 1.0. Geoscientific Model Development,9(10), 3533 3543. https://doi.org/10.5194/gmd-9-3533-2016
Roy, S. (2023). Transportation Infrastructure, Slope Instability, and Soil Erosion. In Disturbing Geomorphology by Transportation Infrastructure: Problem, Prospect, and Solution (pp. 109-133). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-37897-3_4
Santangelo, M., Gioia, D., Cardinali, M., Guzzetti, F., & Schiattarella, M. (2013). Interplay between mass movement and fluvial network organization: An example from southern Apennines, Italy. Sediment Sources, Source-to-Sink Fluxes and Sedimentary Budgets,188,54 67.
https://doi.org/10.1016/j.geomorph.2012.12.008
Tsangaratos, P., Loupasakis, C., Nikolakopoulos, K., Angelitsa, V., & Ilia, I. (2018). Developing a landslide susceptibility map based on remote sensing, fuzzy logic and expert knowledge of the Island of Lefkada, Greece. Environmental Earth Sciences, 77(10), 363. https://doi.org/10.1007/s12665-018-7548-6
Varnes, D. J. (1984). Landslide hazard zonation: A review of principles and practice. Retrieved from https://api.semanticscholar.org/CorpusID:128645072
Yilmaz, I. (2009). Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey). Computers & Geosciences,35(6),1125 1138. https://doi.org/10.1016/j.cageo.2008.08.007
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