Abstract
Landslides are natural destructive phenomena that can cause great damage to property and life loss. One of the fundamental proceedings to reduce the possible damage is identifying landslide-prone areas through different knowledge and data driven methods. Since Gilan province has a high potential for landslides occurrence, thus, the present study goes through to map landslide susceptibility. To accomplish this, two methods of AHP and fuzzy were used and then ROC/AUC curves have been preferred to evaluate the susceptibility map’s performance. In this study, seven input layers of landslide causative factors including slope, lithology, land-use, rainfall, distance to fault, distance to road and distance to river were considered. The landslide susceptibility map derived from AHP method was obtained after assigning the weights to different layers, which were all based on the expert’s judgments. According to this map, 29.53% of total-area, were considered as high and very high-risk areas. In the fuzzy method, three scenarios were adopted to combine the layers. Among them, the third scenario showed the best result. The output map of this scenario has devoted 36.53% of total area to high and very high-risk areas. Prediction accuracy of these maps showed the values of AUC equal to 92.4 and 91.9 for AHP and fuzzy maps, respectively. Both of these maps, mainly, introduced some parts of central, south and southeast areas of Gilan as landslide-prone areas.
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References
Abdullah, L. (2013). Fuzzy multi criteria decision making and its applications: A brief review of category. Procedia-Social and Behavioral Sciences, 97, 131–136.
Ahmed, B. (2015). Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh. Landslides, 12(6), 1077–1095.
Akgun, A., Sezer, E. A., Nefeslioglu, H. A., Gokceoglu, C., & Pradhan, B. (2012). An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Computers & Geosciences, 38(1), 23–34.
Aleotti, P., & Chowdhury, R. (1999). Landslide hazard assessment: Summary review and new perspectives. Bulletin of Engineering Geology and the Environment, 58(1), 21–44.
Althouse, A. D. (2016). Statistical graphics in action: Making better sense of the ROC curve. International Journal of Cardiology, 215, 9–10.
Althuwaynee, O. F., Pradhan, B., & Lee, S. (2016). A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pairwise comparison. International Journal of Remote Sensing, 37(5), 1190–1209.
An, P., Moon, W. M., & Rencz, A. (1991). Application of fuzzy set theory for integration of geological, geophysical, and remote sensing data. Canadian Journal of Exploration Geophysics, 27(1), 1–11.
Arab Amiri, M., Amerian, Y., & Mesgari, M. S. (2016). Spatial and temporal monthly precipitation forecasting using wavelet transform and neural networks, Qara-Qum catchment, Iran. Arabian Journal of Geosciences, 9(5), 1–18.
Arab Amiri, M., & Conoscenti, C. (2017). Landslide susceptibility mapping using precipitation data, Mazandaran Province, north of Iran. Natural Hazards, 89(1), 255–273. https://doi.org/10.1007/s11069-017-2962-8.
Arab Amiri, M., Conoscenti, C., & Mesgari, M. S. (2018). Improving the accuracy of rainfall prediction using a regionalization approach and neural networks. Kuwait Journal of Science, 45(4), 66–75.
Arab Amiri, M., & Mesgari, M. S. (2016). Spatial variability analysis of precipitation in northwest Iran. Arabian Journal of Geosciences, 9(11), 1–10.
Arab Amiri, M., & Mesgari, M. S. (2017). Modeling the spatial and temporal variability of precipitation in northwest Iran. Atmosphere, 8(12), 1–14.
Arab Amiri, M., & Mesgari, M. S. (2018). Analyzing the spatial variability of precipitation extremes along longitude and latitude, northwest Iran. Kuwait Journal of Science, 45(1), 121–127.
Arab Amiri, M., & Mesgari, M. S. (2019). Spatial variability analysis of precipitation and its concentration in Chaharmahal and Bakhtiari province, Iran. Theoretical and Applied Climatology, 137(3–4), 2905–2914.
Arab Amiri, M., Mesgari, M. S., & Conoscenti, C. (2017). Detection of homogeneous precipitation regions at seasonal and annual time scales, northwest Iran. Journal of Water and Climate Change, 8(4), 701–714.
Arora, M. K., Das Gupta, A. S., & Gupta, R. P. (2004). An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas. International Journal of Remote Sensing, 25(3), 559–572.
Atkinson, P. M., & Massari, R. (1998). Generalized linear modeling of landslide susceptibility in the Central Apennines, Italy. Computers & Geosciences, 24(4), 373–385.
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.
Bahrami, Y., Hassani, H., & Maghsoudi, A. (2018). Investigating the capabilities of multispectral remote sensors data to map alteration zones in the Abhar area. Geosystem Engineering. https://doi.org/10.1080/12269328.2018.1557083.
Bahrami, Y., Hassani, H., & Maghsoudi, A. (2019). BWM-ARAS: A new hybrid MCDM method for Cu prospectivity mapping in the Abhar area, NW Iran. Spatial Statistics, 33, 100382. https://doi.org/10.1016/j.spasta.2019.100382.
Balezentiene, L., Streimikiene, D., & Balezentis, T. (2013). Fuzzy decision support methodology for sustainable energy crop selection. Renewable Sustainable Energy Reviews, 17, 83–93.
Bianchini, S., Raspini, F., Ciampalini, A., Lagomarsino, D., Bianchi, M., Bellotti, F., et al. (2016). Mapping landslide phenomena in landlocked developing countries by means of satellite remote sensing data: The case of Dilijan (Armenia) area. Geomatics, Natural Hazards and Risk, 8(2), 225–241.
Bishop, C. M. (2006). Pattern recognition and machine learning. NewYork, NY: Springer
Bourenane, H., Bouhadad, Y., Guettouche, M. S., & Braham, M. (2015). 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(2), 337–355.
Bowen, W. M. (1990). Subjective judgments and data environment analysis in site selection. Computer, Environment and Urban Systems, 14(2), 133–144.
Brenning, A. (2005). Spatial prediction models for landslide hazards: Review, comparison and evalution. Natural Hazards and Earth Systems Sciences, 5, 853–862.
Bui, D. T., Pradhan, B., Lofman, O., Revhaug, I., & Dick, O. B. (2012). Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): A comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. CATENA, 96, 28–40.
Cevik, E., & Topal, T. (2003). GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environmental Geology, 44(8), 949–962.
Chen, W., Hong, H., Panahi, M., Shahabi, H., Wang, Y., Shirzadi, A., et al. (2019). Spatial prediction of landslide susceptibility using GIS-based data mining techniques of ANFIS with whale optimization algorithm (WOA) and grey wolf optimizer (GWO). Applied Sciences, 9(18), 3755.
Chen, Y., Yu, J., & Khan, S. (2010). Spatial sensitivity analysis of multi-criteria weights in GIS-based land suitability evaluation. Environmental Modelling & Software, 25(12), 1582–1591.
Cheong, C. W., Jie, L. H., Meng, M. C., & Lan, A. L. H. (2008). Design and development of decision making system using fuzzy analytic hierarchy process. American Journal of Applied Sciences, 5(7), 783–787.
Chi, K. H., Park, N. W., & Lee, K. (2002a). Identification of landslide area using remote sensing data and quantitative assessment of landslide hazard. In Proceedings of IEEE international geoscience and remote sensing Symposium, Toronto, Canada.
Chung, C. F., & Fabbri, A. G. (2001). Prediction models for landslide hazard zonation using a fuzzy set approach. In Geomorphology and environmental impact assessment Balkema, Lisse, The Netherlands (pp. 31–47).
Clerici, A., Perego, S., Tellini, C., & Vescovi, P. (2002). A procedure for landslide susceptibility zonation by the conditional analysis method. Geomorphology, 48(4), 349–364.
Cruden, D. M. (1991). A simple definition of a landslide. Bulletin International Association for Engineering Geology, 43(1), 27–29.
Dai, F. C., & Lee, C. F. (2002). Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology, 42(3–4), 213–228.
Darbra, R. M., & Casal, J. (2009). Environmental risk assessment of accidental releases in chemical plants through fuzzy logic. Chemical Engineering Transactions, 17, 287–292.
Demir, G., Aytekin, M., Akgun, A., Ikizler, S. B., & Tatar, O. (2013). A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by Likelihood frequency ratio and analytic hierarchy process methods. Natural Hazards, 65(3), 1481–1506.
Dhakal, A. S., Amada, T., & Aniya, M. (2000). Landslide hazard mapping and its evaluation using GIS: An investigation of sampling schemes for a grid-cell based quantitative method. Photogrammetric Engineering and Remote Sensing, 66(8), 981–989.
Dia, F. C., Lee, C. F., Li, J., & Xu, Z. W. (2001). Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environmental Geology, 40(3), 381–391.
Ebrahimi, M., Nematollahi, M. J., Moradian, A., Adineh, S., & Esmaeili, R. (2015). Surface water quality assessment in Gilan province, Iran. Journal of Biodiversity and Environmental Sciences (JBES), 6(5), 269–280.
Einstein, H. H. (1988). Special lecture: Landslide risk assessment procedure. In Proceedings of 5th symposium on landslides, Lausanne (Vol. 2, pp. 1075–1090).
Feizizadeh, B., Roodposhti, M. R., Jankowski, P., & Blaschke, T. (2014). A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping. Computers & Geosciences, 73, 208–222.
Fell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E., & Savage, W. Z. (2008). Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Engineering Geology, 102(3–4), 99–111.
Flentje, P., & Chowdhury, R. (2016). Resilience and sustainability in the management of landslides. In Proceedings of the institution of civil engineers-engineering sustainability (pp. 1–12). Thomas Telford Ltd.
Frattini, P., Crosta, G., Carrara, A., & Agliardi, F. (2008). Assessment of rockfall susceptibility by integrating statistical and physically-based approaches. Geomorphology, 94(3–4), 419–437.
Gokceoglu, C., & Aksoy, H. (1996). Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques. Engineering Geology, 44(1–4), 147–161.
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(1–4), 181–216.
Guzzetti, F., Galli, M., Reichenbach, P., Ardizzone, F., & Cardinali, M. (2006). Landslide hazard assessment in the Collazzone area, Umbria, Central Italy. Natural Hazards and Earth Systems Sciences, 6, 115–131.
He, S., Pan, P., Dai, L., Wang, H., & Liu, J. (2012). Applicat ion of kernel-based Fisher discriminant analysis to map landslide susceptibility in the Qinggan River delta, Three Gorges, China. Geomorphology, 171, 30–41.
Ho, W. (2008). Integrated analytic hierarchy process and its applications. A literature review. European Journal of Operational Research, 186(1), 211–228.
Kanungo, D. P., Arora, M. K., Gupta, R. P., & Sarkar, S. (2005). GISbased landslide hazard zonation using neuro-fuzzy weighting. In Proc 2nd Ind Int Conf on Artificial Intelligence (IICAI-05), Pune (pp. 1222–1237).
Kanungo, D. P., Arora, M. K., Sarkar, S., & Gupta, R. (2006). A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Engineering Geology, 85(3–4), 347–366.
Karimpour, K., Zarghami, R., Moosavian, M. A., & Bahmanyar, H. (2016). New fuzzy model for risk assessment based on different types of consequences. Oil & Gas Science and Technology-Revue d’IFP Energies Nouvelles, 71(1), 17.
Kayastha, P., Dhital, M. R., & De Smedt, F. (2013). Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: A case study from the Tinau watershed, west Nepal. Computers & Geosciences, 52, 398–408.
Lai, S. (1995). Apreference-based interpretation of AHP. Omega, 23(4), 453–462.
Lai, S., & Tsai, F. (2019). Improving GIS-based landslide susceptibility assessments with multitemporal remote sensing and machine learning. Sensors, 19(17), 3717.
Lan, H. X., Zhou, C. H., Wang, L. J., Zhang, H. Y., & Li, R. H. (2004). Landslide hazard spatial analysis and prediction using GIS in the Xiaojiang Watershed, Yunnan, China. Engineering Geology, 76(1–2), 109–128.
Lazzari, M., & Danese, M. (2012). A multitemporal kernel density estimation approach for new triggered landslides forecasting and susceptibility assessment. Disaster Advances, 5(3), 100–108.
Lee, S., & Choi, J. (2004). Landslide susceptibility mapping using GIS and the weight-of evidence model. International Journal of Geographical Information Science, 18(8), 789–814.
Lee, S., Choi, J., Chwae, U., & Chang, B. (2002a). Landslide susceptibility analysis using weight of evidence. In Proc of IEEE Int Geosciences and Remote Sensing Symposium, Toronto, Canada.
Lee, S., Choi, J., & Min, K. (2002b). Landslide susceptibility analysis and verification using the bayesian probability model. Environmental Geology, 43(1–2), 120–131.
Lee, C. T., Huang, C. C., Lee, J. F., Pan, K. L., Lin, M. L., & Dong, J. J. (2008). Statistical approach to earthquake-induced landslide susceptibility. Engineering Geology, 100(1–2), 43–58.
Lee, S., & Min, K. (2001). Statistical analysis of landslide susceptibility at Yongin, Korea. Environmental Geology, 40(9), 1095–1113.
Lee, S., Ryu, J., Won, J., & Park, H. (2004). Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Engineering Geology, 71(3–4), 289–302.
Li, L., Lan, H., Guo, C., Zhang, Y., Li, Q., & Wu, Y. (2017). A modified frequency ratio method for landslide susceptibility assessment. Landslides, 14(2), 727–741.
Lin, M. L., & Tung, C. C. (2003). A GIS-based potential analysis of the landslides induced by the Chi–Chi earthquake. Engineering Geology, 71(1–2), 63–77.
Luger, G. F. (2005). Artificial intelligence: Structures and strategies for complex problem solving (p. 903). New York: Addison-Wesley.
Marjanović, M., Kovačević, M., Bajat, B., & Voženílek, V. (2011). Landslide susceptibility assessment using SVM machine learning algorithm. Engineering Geology, 123(3), 225–234.
McKean, J., & Roering, J. (2004). Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry. Geomorphology, 57(3–4), 331–351.
Merghadi, A., Abderrahmane, B., & Bui, D. T. (2018). Landslide susceptibility assessment at Mila Basin (Algeria): A comparative assessment of prediction capability of advanced machine learning methods. ISPRS International Journal of Geo-Information, 7(7), 268.
Myronidis, D., Papageorgiou, C., & Theophanous, S. (2016). Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP). Natural Hazards, 81(1), 245–263.
Nefeslioglu, H. A., Gokceoglu, C., & Sonmez, H. (2008). An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Engineering Geology, 97(3–4), 171–191.
Noorollahi, Y., Sadeghi, S., Yousefi, H., & Nohegar, A. (2018). Landslide modelling and susceptibility mapping using AHP and fuzzy approaches. International Journal of Hydrology, 2(2), 137–148.
Osna, T., Sezer, E. A., & Akgun, A. (2014). GeoFIS: An integrated tool for the assessment of landslide susceptibility. Computers & Geosciences, 66, 20–30.
Pachari, A. K., Gupta, P. V., & Chander, R. (1998). Landslide zoning in a part of the Garhwal Himalayas. Environmental Geology, 36(3–4), 325–334.
Park, S., Choi, C., Kim, B., & Kim, J. (2013). Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environmental Earth Sciences, 68(5), 1443–1464.
Piegari, E., Cataudella, V., Di Maio, R., Milano, L., Nicodemi, M., & Soldovieri, M. G. (2009). Electrical resistivity tomography and statistical analysis in landslide modelling: A conceptual approach. Journal of Applied Geophysics, 68(2), 151–158.
Pourghasemi, H. R., Pradhan, B., Gokceoglu, C., & Moezzi, K. D. (2012). Landslide susceptibility mapping using a spatial multi criteria evaluation model at Haraz Watershed, Iran. Terrigenous Mass Movements (pp. 23–49). Heidelberg: Springer.
Pradhan, B. (2010). Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. Journal of the Indian Society of Remote Sensing, 38(2), 301–320.
Pradhan, A. M. S., & Kim, Y. T. (2016). Evaluation of a combined spatial multi-criteria evaluation model and deterministic model for landslide susceptibility mapping. CATENA, 140, 125–139.
Pradhan, B., & Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis: Backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling and Software, 25(6), 747–759.
Pradhan, B., Lee, S., Mansor, S., Buchroithner, M., Jamaluddin, N., & Khujaimah, Z. (2008). Utilization of optical remote sensing data and geographic information system tools for regional landslide hazard analysis by using binomial logistic regression model. Journal of Applied Remote Sensing, 2(1), 023542.
Roodposhti, M. S., Rahimi, S., & Beglou, M. J. (2014). PROMETHEE II and fuzzy AHP: An enhanced GIS-based landslide susceptibility mapping. Natural Hazards, 73(1), 77–95.
Ross, T. J. (1995). Fuzzy logic with engineering applications. New York: McGraw-Hill.
Saaty, T. L. (1977). A scaling method for priorities in hierarchical structure. Journal of Mathematical Psychology, 15(3), 34–39.
Saaty, T. L. (1980). The analytical hierarchy process: Planning, priority setting, resource allocation (1st ed.). New York: McGraw-Hill.
Sadr, M. P., Maghsoudi, A., & Saljoughi, B. S. (2014). Landslide susceptibility mapping of Komroud sub-basin using fuzzy logic approach. Geodynamics Research International Bulletin, 2(2), XVI–XXVIII.
Saha, A. K., Gupta, R. P., Sarkar, I., Arora, M. K., & Csaplovics, E. (2005). An approach for GIS-based statistical landslide susceptibility zonation—With a case study in the Himalayas. Landslides, 2(1), 61–69.
Sezer, E. A., Pradhan, B., & Gokceoglu, C. (2011). Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Systems with Applications, 38(7), 8208–8219.
Srivastava, V., Srivastava, H. B., & Lakhera, R. C. (2010). Fuzzy gamma based geomatic modelling for landslide hazard susceptibility in a part of Tons river valley, northwest Himalaya, India. Geomatics, Natural Hazards and Risk, 1(3), 225–242.
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.
Süzen, M. L., & Doyuran, V. (2004). A comparison of the GIS based landslide susceptibility assessment methods: Multivariate versus bivariate. Environmental Geology, 45(5), 665–679.
Swets, J. A. (1988). Measuring the accuracy of diagnostic systems. Science, 240(4857), 1285–1293.
Wang, Q., Li, W., Chen, W., & Bai, H. (2015). GIS-based assessment of landslide susceptibility using certainty factor and index of entropy models for the Qianyang County of Baoji city, China. Journal of Earth System Science, 124(7), 1399–1415.
Wieczorek, G. F. (1984). Preparing a detailed landslide-inventory map for hazard evaluation and reduction. Bulletin Association of Engineering Geologists, 21(3), 337–342.
Wu, X., Ren, F., & Niu, R. (2013). Landslide susceptibility assessment using object mapping units, decision tree, and support vector machine models in the Three Gorges of China. Environmental Earth Sciences, 71(11), 4725–4738.
Xu, C., Dai, F., Xu, X., & Lee, Y. H. (2012a). GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology, 145, 70–80.
Xu, C., Xu, X. W., Dai, F. C., & Arun, K. S. (2012b). Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China. Computers & Geosciences, 46, 317–329.
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.
Yang, Z., Lan, H., Gao, X., Li, L., Meng, Y., & Wu, Y. (2015). Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China. Natural Hazards, 75(3), 2467–2487.
Yazdani, E. A., & Ghanavati, E. (2016). Landslide Hazard Zonation by using AHP (Analytical Hierarchy Process) model in GIS (Geographic Information System) Environment (Case study: Kordan Watershed). International Journal of Progressive Sciences and Technologies (IJPSAT), 2(1), 24–39.
Yousefi, M., & Carranza, E. J. M. (2015). Prediction-area (P-A) plot and C-A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling. Computers & Geosciences, 79, 69–81.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.
Zêzere, J. L., Pereira, S., Melo, R., Oliveira, S. C., & Garcia, R. A. C. (2017). Mapping landslide susceptibility using data-driven methods. Science of the Total Environment, 589, 250–267.
Zhu, A. X., Miao, Y., Yang, L., Bai, S., Liu, J., & Hong, H. (2018). Comparison of the presence-only method and presence-absence method in landslide susceptibility mapping. CATENA, 171, 222–233.
Zhu, A. X., Wang, R., Liu, J., Du, F., Qin, C. Z., Lin, Y., et al. (2014). An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic. Geomorphology, 214, 128–138.
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Bahrami, Y., Hassani, H. & Maghsoudi, A. Landslide susceptibility mapping using AHP and fuzzy methods in the Gilan province, Iran. GeoJournal 86, 1797–1816 (2021). https://doi.org/10.1007/s10708-020-10162-y
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DOI: https://doi.org/10.1007/s10708-020-10162-y