Identifying Landslides Prone-Areas Using GIS-based Fuzzy Analytical Hierarchy Process Model in Ziz Upper Watershed (Morocco)

Sadiki, Mohamed; Manaouch, Mohamed; Aghad, Mohamed; Batchi, Mouhcine; Al Karkouri, Jamal

doi:10.12912/27197050/154916

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Tytuł artykułu

Identifying Landslides Prone-Areas Using GIS-based Fuzzy Analytical Hierarchy Process Model in Ziz Upper Watershed (Morocco)

Autorzy

Sadiki Mohamed , Manaouch Mohamed , Aghad Mohamed , Batchi Mouhcine , Al Karkouri Jamal

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DOI

10.12912/27197050/154916

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Abstrakty

Landslides cause massive damage to human lives, infrastructure, and property in many regions of the world. These disasters arise on unstable slopes in mountainous regions. The recent increase in these incidents in many regions, including Morocco, has attracted more attention to their study. In this paper, a combination of GIS techniques, fuzzy analytical hierarchy process (FAHP) were integrated to model landslide susceptibility in the upper Ziz catchment, south-eastern Morocco. The data used for this purpose included several geo-environmental and climatic factors affecting susceptibility to landslides. The results of this modeling showed that 16.7% of the studied area has a high susceptibility to landslides, and that the upstream western part is considered the most susceptible. Evaluation of the resulting map’s accuracy using the inventory of 148 landslide events showed that the FAHP model has an important performance, as the value of the area under the curve was about 0.885.

Słowa kluczowe

landslide susceptibility GIS fuzzy analytical hierarchy process Ziz Morocco

Wydawca

Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej
Lublin University of Technology
WNGB Wydawnictwo Naukowe

Czasopismo

Ecological Engineering & Environmental Technology

Rocznik

2023

Tom

Vol. 24, iss. 1

Strony

67--83

Opis fizyczny

Bibliogr. 67 poz., rys., tab.

Twórcy

autor

Sadiki Mohamed

mohamed.sadiki@uit.ac.ma

Geosciences Laboratory, Department of Geology, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco

https://orcid.org/0000-0003-3970-1683

autor

Manaouch Mohamed

Laboratory of Environment, Societies and Territories, Department of Geography, Faculty of Humanities and Social Sciences, Ibn Tofail University, BP 242, Kenitra, Morocco

https://orcid.org/0000-0002-2362-7098

autor

Aghad Mohamed

Laboratory of Environment, Societies and Territories, Department of Geography, Faculty of Humanities and Social Sciences, Ibn Tofail University, BP 242, Kenitra, Morocco

https://orcid.org/0000-0002-2889-5542

autor

Batchi Mouhcine

Laboratory of Environment, Societies and Territories, Department of Geography, Faculty of Humanities and Social Sciences, Ibn Tofail University, BP 242, Kenitra, Morocco

https://orcid.org/0000-0002-0915-6592

autor

Al Karkouri Jamal

Laboratory of Environment, Societies and Territories, Department of Geography, Faculty of Humanities and Social Sciences, Ibn Tofail University, BP 242, Kenitra, Morocco

https://orcid.org/0000-0002-0049-9847

Bibliografia

1. Agence du bassin hydraulique de Guir-Ziz-Ghris (ABH-GZR). 2019. Errachidia, Morocco. http://www.abhgzr.ma, Accessed 2 April 2021.
2. Abraham, M.T., Satyam, N., Shreyas, N., Pradhan, B., Segoni, S., Abdul Maulud, K.N., Alamri, A.M. 2021. Forecasting landslides using SIGMA model: a case study from Idukki, India. Geomatics Natural Hazards & Risk, 12(1), 540–559.
3. Akgun, A., Bulut, F. 2007. GIS based landslide hazard for Arsin-Yomra (Trabzon, north Turkey) region. Environmental Geology, 51, 1377–1387.
4. Arabameri, A., Pradhan, B., Rezaei, K., Lee, C.W. 2019. Assessment of landslide susceptibility using statistical-and artificial intelligence-based FR–RF integrated model and multiresolution DEMs. Remote Sensing, 11(9), 999. https://doi.org/10.3390/rs11090999
5. Arabameri, A., Pradhan, B., Rezaei, K., Lee, S., Sohrabi, M. 2020. An ensemble model for landslide susceptibility mapping in a forested area. Geocarto International, 35(15), 1680–1705.
6. Arabameri, A., Saha, S., Roy, J., Tiefenbacher, J.P., Cerda, A., Biggs, T., Collins, A.L. 2020. A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility. Science of the Total Environment, 726, 138595.
7. Bahrami, Y., Hassani, H., Maghsoudi, A. 2020. Landslide susceptibility mapping using AHP and fuzzy methods in the Gilan province, Iran. GeoJournal. https://doi.org/10.1007/s10708-020-10162-y
8. Balogun, A.L., Rezaie, F., Pham, Q.B., Gigović, L., Drobnjak, S., Aina, Y.A., Lee, S. 2021. Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms. Geoscience Frontiers, 12(3), 101104.
9. Barella, C.F., Sobreira, F.G., Zêzere, J.L. 2018. A comparative analysis of statistical landslide susceptibility mapping in the southeast region of Minas Gerais state. Bulletin International Association English Geologues, 78, 3205–3221.
10. Benzougagh, B., Meshram, S.G., Baamar, B., Dridri, A., Boudad, L., Sadkaoui, D., Mimich, K. 2020. Relationship between landslide and morphostructural analysis: a case study in Northeast of Morocco. Applied Water Sciences, 10(7), 1–10.
11. Charrire, A. 1990. Héritage hercynien et évolution géodynamique alpine d’une chaine intracontinentale: le Moyen Atlas au S de Fès (Maroc). Thèse Doct. Etat, Toulouse. C.
12. hen, W., Chai, H., Zhao, Z., Wang, Q., Hong, H. 2016. Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China. Environmental Earth Sciences, 75(6), 1–13.
13. Chen, Y., Yu, J., Khan, S. 2010. Spatial sensitivity analysis of multi-criteria weights in GIS-based land suitability evaluation. Environmental Modelling & Softwares, 25(12), 1582–1591.
14. Chen, W., Chen, X., Peng, J., Panahi, M., Lee, S. 2021. Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer. Geosciences Frontiers, 12(1), 93–107.
15. Dikshit, A., Pradhan, B., Alamri, A.M. 2020. Pathways and challenges of the application of artificial intelligence to geohazards modelling. Gondwana Research. https://doi.org/10.1016/j.gr.2020.08.007
16. Dikshit, A., Sarkar, R., Pradhan, B., Acharya, S., Alamri, A.M. 2020. Spatial landslide risk assessment at Phuentsholing, Bhutan. Geosciences, 10(4). 131.https://doi.org/10.3390/geosciences10040131
17. Dikshit, A., Sarkar, R., Pradhan, B., Jena, R., Drukpa, D., Alamri, A.M. 2020. Temporal probability assessment and its use in landslide susceptibility mapping for eastern Bhutan. Water, 12(1), 267. https://doi.org/10.3390/w12010267
18. Dokanovic, S. 2019. Landslide susceptibility mapping of SE Serbia using GIS. Geološki Analibalkanskoga Poluostrva, 80(2), 105–116.https://doi.org/10.2298/GABP1902105D
19. 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.
20. Es-smairi, A., El Moutchou, B., Touhami, A.E.O. 2021. Landslide susceptibility assessment using analytic hierarchy process and weight of evidence methods in parts of the Rif chain (northernmost Morocco). Arabian Journal of Geosciences, 14(14), 1–18.
21. Feizizadeh, B., Blaschke, T. 2013. GIS-multicriteria decision analysis for landslide susceptibility mapping: comparing three methods for the Urmia lake basin, Iran. Natural Hazards, 65(3), 2105–2128. https://doi.org/10.1007/s11069-012-0463-3
22. Fenjiro, I., Zouagui, A., Manaouch, M. 2020. Assessment of Soil Erosion by RUSLE Model using Remote Sensing and GIS - A case study of Ziz Upper Basin Southeast Morocco. Forum Geografic, 19(2), 131–142.
23. Flentje, P., Chowdhury, R. 2016. Resilience and sustainability in the management of landslides. In Proceedings of the institution of civil engineers-engineering sustainability. Thomas Telford Ltd, 1–12.
24. Glade, T., Crozier, M.J. 2012. A review of scale dependency in landslide hazard and risk analysis. Landslide Hazard Risk, 75, 75–138.
25. Gonzague, D., Despujols, M. 1939. Carte géologique de Midelt au 1/200 000 publiée par le service géologique du Maroc.
26. Hinaje, S. 1995. Apport de l’analyse de la tectonique cassante tardi et post-panafricane à la modélisation de la mise en place des miniralisations dans la boutoniére de Bou-Azzer (Anti-Atlas, Maroc). Thèse de 3ème cycle, Fac. Sci. Rabat.
27. Hines, J.W. 1997. Fuzzy and neural approaches in engineering. Wiley, New York, NY
28. Lai, S. 1995. A preference-based interpretation of AHP. Omega, 23(4), 453–462.
29. Lee, S., Pradhan, B. 2007. Landslide hazard mapping at Selangor, malaysia using frequency ratio and logistic regression models. Landslides, 4, 33–41.
30. Lee, S., Lee, M.J., Jung, H.S., Lee, S. 2020. Landslide susceptibility mapping using naïve Bayes and bayesian network models in Umyeonsan, Korea. Geocarto International, 35(15), 1665–1679.
31. Lyazidi, M., Eyssautier, L., Marcais, J., Choubert, G., Faillot, P. 1956. Carte géologique de Rich et Boudnib au1/200 000 publiée par le service géologique du Maroc.
32. Manaouch, M., Mohamed, S., Imad, F. 2021. Coupling Fuzzy logic and Analytical Hierarchy Process (FAHP) with GIS for Landslide Susceptibility Mapping (LSM) in Ziz upper watershed, SE Morocco. Accessed online at https://www.researchgate.net/publication/349538914_Coupling_Fuzzy_logic_and_Analytical_Hierarchy_Proce_FAHP_with_GIS_for_Landslide_Susceptibility_Mapping_LSM_in_Ziz_upper_watershed_SE_Morocco
33. Manaouch, M., Mohamed, S., Imad, F. 2021. Integrating WaTEM/SEDEM model and GIS-based FAHP Method for Identifying Ecological Rainwater Harvesting Sites in Ziz upper watershed, SE Morocco. Accessed online at https://www.researchsquare.com/article/rs-529521/v1.pdf
34. Moayedi, H., Mehrabi, M., Mosallanezhad, M., Rashid, A.S.A., Pradhan, B. 2019. Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Engineering Computations, 35(3), 967–984.
35. Mohamed, M., Anis, Z., Imad, F. 2020. Assessment of the Effects of Land Use/Land Cover Changes on Soil Loss and Sediment Yield Using WaTEM/SEDEM Model: Case Study of Ziz Upper Watershed in SE-Morocco. Current Applied Science and Technology, 21(2), 337–350.
36. Mohamed, M., Anis Z., Imed, F. 2020. Regionalscale modeling of water erosion and sediment yield in a semi-arid context: A case study of Ziz upper watershed in south-eastern Morocco. GEOIT4W-2020: Proceedings of the 4th Edition of International Conference on GeoIT and Water Resources 2020, Al-Hoceima, Morocco, March 11–12, 2020. http://doi.org/10.1145/3399205.3399209
37. MycBratney, A.B., Odeh, I.O.A. 1997. Application of fuzzy sets in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions. Geoderma, 77, 85–113.
38. Ngo, P.T.T., Panahi, M., Khosravi, K., Ghorbanzadeh, O., Kariminejad, N., Cerda, A., Lee, S. 2021. Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geosciences Frontiers, 12(2), 505–519.
39. Nhu, V.H., Shirzadi, A., Shahabi, H., Chen, W., Clague, J.J., Geertsema, M., Lee, S. 2020. Shallow landslide susceptibility mapping by random forest base classifier and its ensembles in a semi-arid region of Iran. Forests, 11(4), 421.
40. Panahi, M., Gayen, A., Pourghasemi, H.R., Rezaie, F., Lee, S. 2020. Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic algorithms. Sciences of the Total Environment, 741, 139937.
41. Pellicani, R., Van Westen, S., Spilatro, G. 2014. Assessing landslide exposure in areas with limited landslide information. Landslides, 11, 463–480.
42. Pham, B.T., Nguyen-Thoi, T., Qi, C., Van Phong, T., Dou, J., Ho, L.S., Prakash, I. 2020. Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping. Catena, 195, 104805.
43. Pham, B.T., Phong, T.V., Nguyen-Thoi, T., Parial, K.K., Singh, S., Ly, H.B., Prakash, I. 2020. Ensemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers. Geocarto International 1–23. https://doi.org/10.1080/10106049.2020.1737972
44. Pham, B.T., Vu, V.D., Romulus, C., Phong, T.V., Ngo, T.Q., Prakash, I. 2021. Landslide susceptibility mapping using state-of-the-art machine learning ensembles. Geocarto International, 1–25.
45. PNABV. 2014. Plan National d’Aménagement des Bassins Versants, Étude d’aménagement du bassin versant d’Assif Melloul, Agence du Bassin Hydraulique de l’Oum Er-Rbia.
46. Pourghasemi, H.R., Pradhan, B., Gokceoglu, C. 2012. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural Hazards, 63(2), 965–996.
47. Pradhan, B., Ohb, H.J., Buchroithner, M. 2010. Use of remote sensing data and GIS to produce a landslide susceptibility map of a landslide prone area using a weight of evidence model. Assessment, 11, 13.
48. Reichenbach, P., Rossi, M., Malamud, B.D., Mihir, M., Guzzetti, F. 2018. A review of statistically-based landslide susceptibility models. Earth Sciences Reviews, 180, 60–91.
49. Saaty, T.L. 1980. The analytical hierarchy process, 350. New York: McGraw Hill.
50. Saboya, F., Alves, M.G., Pinto, W.D. 2006. Assessment of failure susceptibility of soil slopes using fuzzy logic. Engineering Geology, 86, 211–224.
51. Sadki, D., Elmi, S., Amhoud, H. 1999. Les formations jurassiques du Haut Atlas central marocain: corrélation et évolution géodynamique. Le 1er Coll. Nat. Sur le Jur. Marocain, 122–123.
52. Saha, S., Roy, J. 2019. Landslide susceptibility mapping using knowledge driven statistical models in Darjeeling District, West Bengal, India. Geoenvironmental Disasters, 6, 11. https://doi.org/10.1186/s40677-019-0126-8
53. Saha, S., Roy, J., Pradhan, B., Hembram, T.K. 2021. Hybrid ensemble machine learning approaches for landslide susceptibility mapping using different sampling ratios at East Sikkim Himalayan, India. Advances in Space Research https://doi.org/10.1016/j.asr.2021.05.018
54. Saha, S., Saha, A., Hembram, T.K., Pradhan, B., Alamri, A.M. 2020. 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, 10(11), 3772. https://doi.org/10.3390/app10113772
55. Shahri, A.A., Spross, J., Johansson, F., Larsson, S. 2019. Landslide susceptibility hazard map in southwest Sweden using artificial neural network. Catena, 183, 104225.
56. Sumathi, V.R., Natesan, U., Sarkar, C. 2008. GIS-based approach for optimized siting of municipal solid waste landfill. Waste Management, 28(11), 2146–2160.
57. Tan, X., Zhou, S., Zhou, S. 2020. Nationwide Susceptibility Mapping of Landslides in Kenya Using the Fuzzy Analytic Hierarchy Process Model. Land, 9, 535. https://doi:10.3390/land9120535
58. Van Westen, C.J., Rengers, N., Terlien, T.J., Soeters, R. 1997. Prediction of the occurrence of slope instability phenomena through GIS-based hazard zonation. Geologische Rundschau, 86, 404–414.
59. Waithaka, E.H., Agutu, N., Mbaka, J.G., Ngigi, T.G. 2015. Landslide scar/soil erodibility mapping using Landsat TM/ETM+ bands 7 and 3 normalised difference index: A case study of central region of Kenya. Applied Geography, 64, 108–120.
60. Yalcin, A. 2008. GIS based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. Catena, 72, 1–12.
61. Yalcin, A., Reis, S., Aydinoglu, A.C., Yomralioglu, T. 2011. A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena, 85(3), 274–287.
62. Zadeh, L.A. 1965. Fuzzy sets. Inform Control, 8(3), 338–353.
63. Zare, M., Pourghasemi, H.R., Vafakhah, M., Pradhan, B. 2013. Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arabian Journal of Geosciences, 6(8), 2873–2888.
64. Zhang, T., Han, L., Zhang, H., Zhao, Y., Zhao, L. 2019. GIS-based landslide susceptibility mapping using hybrid integration approaches of fractal dimension with index of entropy and support vector machine. Journal of Mountain Science, 16(6), 1275–1288.
65. Zhou, C., Yin, K., Cao, Y., Ahmed, B., Li, Y., Catani, F., Pourghasemi, H.R. 2018. Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China. Computers & Geosciences, 112, 23–37.
66. Zhou, S., Zhou, S., Tan, X. 2020. Nationwide Susceptibility Mapping of Landslides in Kenya Using the Fuzzy Analytic Hierarchy Process Model. Land, 9(12), 535.
67. Zieher, T., Perzl, F., Rossel, M., Rutzinger, M., Meibl, G., Markart, G., Geitner, C. 2016. A multiannual landslideinventory for the assessment of shallow landslide susceptibility-Two test cases in vorarlberg, Austria. Geomorphology, 259, 40–54.

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Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

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