Volume 14 Issue 2
Apr.  2023
Turn off MathJax
Article Contents
Gizem Karakas, Sultan Kocaman, Candan Gokceoglu. A Hybrid Multi-Hazard Susceptibility Assessment Model for a Basin in Elazig Province, Türkiye[J]. International Journal of Disaster Risk Science, 2023, 14(2): 326-341. doi: 10.1007/s13753-023-00477-y
Citation: Gizem Karakas, Sultan Kocaman, Candan Gokceoglu. A Hybrid Multi-Hazard Susceptibility Assessment Model for a Basin in Elazig Province, Türkiye[J]. International Journal of Disaster Risk Science, 2023, 14(2): 326-341. doi: 10.1007/s13753-023-00477-y

A Hybrid Multi-Hazard Susceptibility Assessment Model for a Basin in Elazig Province, Türkiye

doi: 10.1007/s13753-023-00477-y

This article is part of the Ph.D. thesis research of Gizem Karakas. The authors thank Recep Can for his continuous support.

  • Accepted Date: 2023-02-03
  • Available Online: 2023-04-28
  • Publish Date: 2023-03-29
  • Preparation of accurate and up-to-date susceptibility maps at the regional scale is mandatory for disaster mitigation, site selection, and planning in areas prone to multiple natural hazards. In this study, we proposed a novel multi-hazard susceptibility assessment approach that combines expert-based and supervised machine learning methods for landslide, flood, and earthquake hazard assessments for a basin in Elazig Province, Türkiye. To produce the landslide susceptibility map, an ensemble machine learning algorithm, random forest, was chosen because of its known performance in similar studies. The modified analytical hierarchical process method was used to produce the flood susceptibility map by using factor scores that were defined specifically for the area in the study. The seismic hazard was assessed using ground motion parameters based on Arias intensity values. The univariate maps were synthesized with a Mamdani fuzzy inference system using membership functions designated by expert. The results show that the random forest provided an overall accuracy of 92.3% for landslide susceptibility mapping. Of the study area, 41.24% were found prone to multi-hazards (probability value > 50%), but the southern parts of the study area are more susceptible. The proposed model is suitable for multi-hazard susceptibility assessment at a regional scale although expert intervention may be required for optimizing the algorithms.
  • loading
  • [1]
    AFAD (Disaster and Emergency Management Presidency of Türkiye). 2020. Report on natural event statistics in Türkiye. Afet Yönetimi Kapsamında 2019 Yılına Bakış ve Doğa Kaynaklı Olay İstatistikleri. https://www.afad.gov.tr/kurumlar/afad.gov.tr/e_Kutuphane/KurumsalRaporlar/Afet_Istatistikleri_2020_web.pdf. Accessed 4 Feb 2023 (in Turkish).
    Akinci, H., and A.Y. Ozalp. 2021. Landslide susceptibility mapping and hazard assessment in Artvin (Turkey) using frequency ratio and modified information value model. Acta Geophysica 69(3):725-745.
    Aksha, S.K., L.M. Resler, L. Juran, and L.W. Carstensen. 2020. A geospatial analysis of multi-hazard risk in Dharan, Nepal. Geomatics, Natural Hazards and Risk 11(1):88-111.
    Alpyurur, M., and M.A. Lav. 2022. An assessment of probabilistic seismic hazard for the cities in Southwest Turkey using historical and instrumental earthquake catalogs. Natural Hazards 114(1):335-365.
    Arca, D., M. Hacısalihoğlu, and S.H. Kutoğlu. 2020. Producing forest fire susceptibility map via multi-criteria decision analysis and frequency ratio methods. Natural Hazards 104(1):73-89.
    Arias, A. 1970. Measure of earthquake intensity. In Seismic design for nuclear power plants, ed. R.J. Hansen, 438-483. Cambridge:MIT Press.
    Avci, V., and M. Sunkar. 2018. The relationship of landslides with lithological units and fault lines occurring on the East Anatolian Fault Zone, between Palu (Elazığ) and Bingöl, Turkey. Bulletin of the Mineral Research and Exploration 157:23-38.
    Bera, S., A. Das, and T. Mazumder. 2022. Evaluation of machine learning, information theory and multi-criteria decision analysis methods for flood susceptibility mapping under varying spatial scale of analyses. Remote Sensing Applications:Society and Environment 25:Article 100686.
    Bergstra, J., and Y. Bengio. 2012. Random search for hyper-parameter optimization. The Journal of Machine Learning Research 13:281-305.
    Borcherdt, R.D., and G. Glassmoyer. 1992. On the characteristics of local geology and their influence on ground motions generated by the Loma Prieta earthquake in the San Francisco Bay region, California. Bulletin of the Seismological Society of America 82(2):603-641.
    Bordbar, M., H. Aghamohammadi, H.R. Pourghasemi, and Z. Azizi. 2022. Multi-hazard spatial modeling via ensembles of machine learning and meta-heuristic techniques. Scientific Report 12:Article 145.
    Breiman, L. 2001. Random forests. Machine Learning 45:5-32.
    Bui, D.T., K. Khosravi, S. Li, H. Shahabi, M. Panahi, V.P. Singh, K. Chapi, A. Shirzadi, et al. 2018. New hybrids of ANFIS with several optimization algorithms for flood susceptibility modeling. Water 10(9):Article 1210.
    Campbell, K.W., and Y. Bozorgnia. 2010. A ground motion prediction equation for the horizontal component of Cumulative Absolute Velocity (CAV) based on the PEER-NGA strong motion database. Earthquake Spectra 26(3):635-650.
    Can, R., S. Kocaman, and C. Gokceoglu. 2021. A comprehensive assessment of XGBoost algorithm for landslide susceptibility mapping in the upper basin of Ataturk Dam, Turkey. Applied Sciences 11:Article 4993.
    Castellaro, S., F. Mulargia, and P.L. Rossi. 2008. Vs30:Proxy for seismic amplification. Seismological Research Letters 79(4):540-543.
    Cetinkaya, S., and S. Kocaman. 2022. Snow avalanche susceptibility mapping for Davos, Switzerland. The International Archive of Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-2022:1083-1090.
    Chapi, K., V.P. Singh, A. Shirzadi, H. Shahabi, D.T. Bui, B.T. Pham, and K. Khosravi. 2017. A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environmental Modelling & Software 95:229-245.
    Chen, W., Y. Li, W. Xue, H. Shahabi, S. Li, H. Hong, X. Wang, H. Bian, et al. 2020. Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods. Science of the Total Environment 701:Article 134979.
    Chousianitis, K., V. Del Gaudio, P. Pierri, and G.A. Tselentis. 2018. Regional ground-motion prediction equations for amplitude-, frequency response-, and duration-based parameters for Greece. Earthquake Engineering Structural Dynamics 47(11):2252-2274.
    CLMS (Copernicus Land Monitoring Service). 2021.Website of CLMS. https://land.copernicus.eu. Accessed 3 Jan 2022.
    Colesanti, C., and J. Wasowski. 2006. Investigating landslides with space-borne Synthetic Aperture Radar (SAR) interferometry. Engineering Geology 88(3-4):173-199.
    Conrad, O., B. Bechtel, M. Bock, H. Dietrich, E. Fischer, L. Gerlitz, J. Wehberg, V. Wichmann, and J. Böhner. 2015. System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geoscientific Model Development 8(2):1991-2007.
    Costanzo, A. 2018. Shaking maps based on cumulative absolute velocity and Arias Intensity:The cases of the two strongest earthquakes of the 2016-2017 Central Italy seismic sequence. ISPRS International Journal of Geo-Information 7(7):Article 244.
    Dahri, N., and H. Abida. 2017. Monte Carlo simulation-aided analytical hierarchy process (AHP) for flood susceptibility mapping in Gabes Basin (southeastern Tunisia). Environmental Earth Sciences 76(7):Article 302.
    Dai, F.C., and C.F. Lee. 2002. Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42(3-4):213-228.
    Das, S. 2018. Geographic information system and AHP-based flood hazard zonation of Vaitarna basin, Maharashtra, India. Arabian Journal of Geosciences 11(19):Article 576.
    Del Gaudio, V., P. Pierri, and K. Chousianitis. 2019. Influence of site response and focal mechanism on the performance of peak ground motion prediction equations for the Greek region. Soil Dynamics and Earthquake Engineering 125:Article 105745.
    Dhar, S., A.K. Rai, and P. Nayak. 2017. Estimation of seismic hazard in Odisha by remote sensing and GIS techniques. Natural Hazards 86(2):695-709.
    Erdik, M., Y. Biro, T. Onur, K. Sesetyan, and G.B. Tanircan. 1999. Assessment of earthquake hazard in Turkey and neighboring. Annali di Geofisica 42(6):1125. https://doi.org/10.4401/ag-3773.
    ESA-WorldCover. 2020. Worldwide land cover mapping:VITO NV.2021. https://esa-worldcover.org/en. Accessed 4 Feb 2023.
    Feizizadeh, B., D. Omarzadeh, V. Mohammadnejad, H. Khallaghi, A. Sharifi, and B.G. Karkarga. 2022. An integrated approach of artificial intelligence and geoinformation techniques applied to forest fire risk modeling in Gachsaran, Iran. Journal of Environmental Planning and Management. https://doi.org/10.1080/09640568.2022.2027747.
    Foulser-Piggot, R., and K. Goda. 2015. Ground-motion prediction models for Arias Intensity and cumulative absolute velocity for Japanese earthquakes considering single-station sigma and within-event spatial correlation. Bulletin of the Seismological Society of America 105(4):1903-1918.
    Gokceoglu, C., H. Sonmez, H.A. Nefeslioglu, T.Y. Duman, and T. Can. 2005. The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity. Engineering Geology 81(1):65-83.
    Gokceoglu, C., M.T. Yurur, S. Kocaman, et al. 2020. Investigation of Elazig Sivrice Earthquake (24 January 2020, Mw=6.8) employing radar interferometry and stereo airphoto photogrammetry. Geomatics and Geological Engineering Departments, Ankara, Turkey:Hacettepe University. https://www.researchgate.net/publication/339596648_Hacettepe_Universitesi_Muhendislik_Fakultesi_RADAR_INTERFEROMETRISI_VE_STEREO_HAVA_FOTOGRAMETRISI_ILE_ELAZIG_SIVRICE_DEPREMININ_24_OCAK_2020_M_W_68_INCELENMESI_Hazirlayanlar_Hacettepe_Universitesi_Muh. Accessed 4 Feb 2023.
    Gupta, K., and N. Satyam. 2022. Estimation of Arias intensity and peak ground acceleration (PGA) using probabilistic seismic hazard assessment of Uttarakhand state (India). Arabian Journal of Geosciences 15(5):Article 437.
    Hammami, S., L. Zouhri, D. Souissi, A. Souei, A. Zghibi, A. Marzougui, and M. Dlala. 2019. Application of the GIS based multi-criteria decision analysis and analytical hierarchy process (AHP) in the flood susceptibility mapping (Tunisia). Arabian Journal of Geosciences 12(21):Article 653.
    Herece, E. 2016. 1:100.000 scale Turkish geological maps, K42 Quadrangle. Publication of General Directorate of the Mineral Research and Exploration, No. 234. Ankara, Turkey:Department of Geological Research.
    Hong, H., P. Tsangaratos, I. Ilia, J. Liu, A.-X. Zhu, and W. Chen. 2018. Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China. Science of the Total Environment 625:575-588.
    Jaboyedoff, M., T. Oppikofer, A. Abellán, M.-H. Derron, A. Loye, R. Metzger, and A. Pedrazzini. 2010. Use of LIDAR in landslide investigations:A review. Natural Hazards 61(1):5-28.
    Javidan, N., A. Kavian, H.R. Pourghasemi, C. Conoscenti, Z. Jafarian, and J. Rodrigo-Comino. 2021. Evaluation of multi-hazard map produced using MaxEnt machine learning technique. Scientific Reports 11(1):Article 6496.
    Kalantar, B., B. Pradhan, S.A. Naghibi, A. Motevalli, and S. Mansor. 2018. Assessment of the effects of training data selection on the landslide susceptibility mapping:A comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomatics, Natural Hazards and Risk 9(1):49-69.
    Karakas, G., R. Can, S. Kocaman, H.A. Nefeslioglu, and C. Gokceoglu. 2020. Landslide susceptibility mapping with random forest model for Ordu, Turkey. ISPRS-International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences XLIII-B3-2020:1229-1236.
    Karakas, G., S. Kocaman, and C. Gokceoglu. 2021. Aerial photogrammetry and machine learning based regional landslide susceptibility assessment for an earthquake prone area in Turkey. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021:713-720.
    Karakas, G., S. Kocaman, and C. Gokceoglu. 2022. Comprehensive performance assessment of landslide susceptibility mapping with MLP and random forest:A case study after Elazig earthquake (24 Jan 2020, Mw 6.8), Turkey. Environmental Earth Sciences 81(5):Article 144.
    Karakas, G., H.A. Nefeslioglu, S. Kocaman, M. Buyukdemircioglu, T. Yurur, and C. Gokceoglu. 2021. Derivation of earthquake-induced landslide distribution using aerial photogrammetry:The 24 January 2020 Elazig (Turkey) Earthquake. Landslides 18:2193-2209.
    Karpouza, M., K. Chousianitis, G.D. Bathrellos, H.D. Skilodimou, G. Kaviris, and A. Antonarakou. 2021. Hazard zonation mapping of earthquake-induced secondary effects using spatial multi-criteria analysis. Natural Hazards 109(1):637-669.
    Kayen, R., and J. Mitchell. 1997. Assessment of liquefaction potential during earthquakes by Arias intensity. Journal of Geotechnical and Geoenvironmental Engineering 123(12):1162-1174.
    Keefer, D.K. 1984. Landslides caused by earthquakes. Geological Society of America Bulletin 95(4):406-421.
    Keefer, D.K., and R.C. Wilson. 1989. Predicting earthquake induced landslides with emphasis on arid or semi-arid environments. Landslides in A Semi-Arid Environment 2(1):118-149.
    Keskin, I. 2011. 1:100.000 scale Turkish geological maps, L42 Quadrangle. Publication of General Directorate of the Mineral Research and Exploration, No. 170.
    Khatakho, R., D. Gautam, K.R. Aryal, V.P. Pandey, R. Rupakhety, S. Lamichhane, Y.-C. Liu, K. Abdouli, et al. 2021. Multi-hazard risk assessment of Kathmandu Valley, Nepal. Sustainability 13(10):Article 5369.
    Khosravi, K., B.T. Pham, K. Chapi, A. Shirzadi, H. Shahabi, I. Revhaug, I. Prakash, and D.T. Bui. 2018. A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Science of the Total Environment 627:744-755.
    Kocaman, S., B. Tavus, H.A. Nefeslioglu, G. Karakas, and C. Gokceoglu. 2020. Evaluation of floods and landslides triggered by a meteorological catastrophe (Ordu, Turkey, August 2018) using optical and radar data. Geofluids 2020:Article 8830661.
    Kolat, C., V. Doyuran, C. Ayday, and L. Süzen. 2006. Preparation of a geotechnical microzonation model using Geographical Information Systems based on multicriteria decision analysis. Engineering Geology 87:241-255.
    Kotha, S.R., F. Cotton, and D. Bindi. 2018. A new approach to site classification:Mixed-effects ground motion prediction equation with spectral clustering of site amplification functions. Soil Dynamics and Earthquake Engineering 110:318-329.
    Lari, S., P. Frattini, and G.B. Crosta. 2014. A probabilistic approach for landslide hazard analysis. Engineering Geology 182:3-14.
    Lee, C.T., C.C. Huang, J.F. Lee, K.-L. Pan, M.-L. Lin, and J.-J. Dong. 2008. Statistical approach to earthquake-induced landslide susceptibility. Engineering Geology 100(1-2):43-58.
    Lee, S., J.C. Kim, H.S. Jung, M.J. Lee, and S. Lee. 2017. Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea. Geomatics, Natural Hazards and Risk 8(2):1185-1203.
    Lima, P., S. Steger, T. Glade, and F.G. Murillo-García. 2022. Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility. Journal of Mountain Science 19(6):1670-1698.
    Liu, Y.-C., and C.S. Chen. 2007. A new approach for application of rock mass classification on rock slope stability assessment. Engineering Geology 89(1-2):129-143.
    Liu, K., M. Wang, Y. Cao, W. Zhu, and G. Yang. 2018. Susceptibility of existing and planned Chinese railway system subjected to rainfall-induced multi-hazards. Transportation Research Part A:Policy and Practice 117:214-226.
    Liu, L.-L., J. Zhang, J.-Z. Li, F. Huang, and L.-C. Wang. 2022. A bibliometric analysis of the landslide susceptibility research (1999-2021). Geocarto International 37:1-26. https://doi.org/10.1080/10106049.2022.2087753.
    Lundberg, S., and S.-I. Lee. 2017. A unified approach to interpreting model predictions. https://arxiv.org/abs/1705.07874v2.
    Mamdani, E.H., and S. Assilian. 1975. Experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7(1):1-13.
    Merghadi, A., A.P. Yunus, J. Dou, J. Whiteley, B. ThaiPham, D.T. Bui, R. Avtar, and B. Abderrahmane. 2020. Machine learning methods for landslide susceptibility studies:A comparative overview of algorithm performance. Earth-Science Reviews 207:Article 103225.
    METU (Earthquake Engineering Research Center). 2020. Elazig-Sivrice earthquake site observations of seismic and structural damage (24 January 2020 Mw 6.8) (24 Ocak 2020 Mw 6.8 Elazığ-Sivrice Depremi Sismik ve Yapısal Hasara İlişkin Saha Gözlemleri. Rapor No:ODTÜ/DMAM 2020-01). http://eerc.metu.edu.tr/tr/system/files/documents/Elaz%C4%B1%C4%9F-Sivrice%20Deprem%20Raporu.pdf. Accessed 4 Feb 2023.
    MGM (Meteoroloji Genel Müdürlüğü/Turkish State Meteorological Service). 2022. Official statistics. https://mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx. Accessed 15 Oct 2022 (in Turkish).
    MTA (General Directorate of Mineral Research & Exploration). 2022. GeoScience Map Viewer. http://yerbilimleri.mta.gov.tr/. Accessed 10 Oct 2022.
    Mukhopadhyay, A., S. Hazra, D. Mitra, C. Hutton, A. Chanda, and S. Mukherjee. 2016. Characterizing the multi-risk with respect to plausible natural hazards in the Balasore coast, Odisha, India:A multi-criteria analysis (MCA) appraisal. Natural Hazards 80(3):1495-1513.
    Natarajan, L., T. Usha, M. Gowrappan, B.P. Kasthuri, P. Moorthy, and L. Chokkalingam. 2021. Flood susceptibility analysis in Chennai corporation using frequency ratio model. Journal of the Indian Society Remote Sensing 49(7):1533-1543.
    Nava, L., K. Bhuyan, S.R. Meena, O. Monserrat, and F. Catani. 2022. Rapid mapping of landslides on SAR data by attention U-Net. Remote Sening 14(6):Article 1449.
    Nefeslioglu, H.A., and C. Gokceoglu. 2011. Probabilistic risk assessment in medium scale for rainfall-induced earthflows:Catakli catchment area (Cayeli, Rize, Turkey). Mathematical Problems in Engineering 2011:Article 280431.
    Nefeslioglu, H.A., E.A. Sezer, C. Gokceoglu, and Z. Ayas. 2013. A modified analytical hierarchy process (M-AHP) approach for decision support systems in natural hazard assessments. Computers & Geosciences 59:1-8.
    Osna, T., E.A. Sezer, and A. Akgun. 2014. GeoFIS:An integrated tool for the assessment of landslide susceptibility. Computers & Geosciences 66:20-30.
    Piramuthu, S. 2008. Input data for decision trees. Expert Systems with Applications 34(2):1220-1226.
    Pourghasemi, H.R., A. Gayen, M. Panahi, F. Rezaie, and T. Blaschke. 2019. Multi-hazard probability assessment and mapping in Iran. Science of the Total Environment 692:556-571.
    Pourghasemi, H.R., N. Kariminejad, M. Amiri, M. Edalat, M. Zarafshar, T. Blaschke, and A. Cerda. 2020. Assessing and mapping multi-hazard risk susceptibility using a machine learning technique. Scientific Reports 10(1):Article 3203.
    Pourghasemi, H.R., B. Pradhan, and C. Gokceoglu. 2012. Application of fuzzy logicand analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural Hazards 63(2):965-996.
    Pourghasemi, H.R., B. Pradhan, C. Gokceoglu, and K.D. Moezzi. 2013. A comparative assessment of prediction capabilities of Dempster-Shafer and Weights-of-evidence models in landslide susceptibility mapping using GIS. Geomatics, Natural Hazards and Risk 4(2):93-118.
    Pouyan, S., H.R. Pourghasemi, M. Bordbar, S. Rahmanian, and J.J. Clague. 2021. A multi-hazard map-based flooding, gully erosion, forest fires, and earthquakes in Iran. Scientific Reports 11(1):Article 14889.
    Rathje, E.M., N.A. Abrahamson, and J.D. Bray. 1998. Simplified frequency content estimates of earthquake ground motions. Journal of Geotechnical and Geoenvironmental Engineering 124(2):Article 150.
    Razavi Termeh, S.V., A. Kornejady, H.R. Pourghasemi, and S. Keesstra. 2018. Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Science of the Total Environment 615:438-451.
    Rusk, J., A. Maharjan, P. Tiwari, T.-H.K. Chen, S. Shneiderman, M. Turin, and K.C. Seto. 2022. Multi-hazard susceptibility and exposure assessment of the Hindu Kush Himalaya. Science of the Total Environment 804:Article 150039.
    Sahana, M., and P.P. Patel. 2019. A comparison of frequency ratio and fuzzy logic models for flood susceptibility assessment of the lower Kosi River Basin in India. Environmental Earth Sciences 78(10):Article 289.
    Samanta, S., D.K. Pal, and B. Palsamanta. 2018. Flood susceptibility analysis through remote sensing, GIS and frequency ratio model. Applied Water Science 8(2):Article 66.
    Sepehri, M., H. Malekinezhad, F. Jahanbakhshi, A.R. Ildoromi, J. Chezgi, O. Ghorbanzadeh, and E. Naghipour. 2020. Integration of interval rough AHP and fuzzy logic for assessment of flood prone areas at the regional scale. Acta Geophysica 68(2):477-493.
    Sevgen, E., S. Kocaman, H.A. Nefeslioglu, and C. Gokceoglu. 2019. A novel performance assessment approach using photogrammetric techniques for landslide susceptibility mapping with logistic regression, ANN and random forest. Sensors 19(18):Article 3940.
    Skilodimou, H.D., G.D. Bathrellos, K. Chousianitis, A.M. Youssef, and B. Pradhan. 2019. Multi-hazard assessment modeling via multi-criteria analysis and GIS:A case study. Environmental Earth Sciences 78(2):Article 47.
    Sozer, B., S. Kocaman, H.A. Nefeslioglu, O. Firat, and C. Gokceoglu. 2018. Preliminary investigations on flood susceptibility mapping in Ankara (Turkey) using modified analytical hierarchy process (M-AHP). ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5:361-365.
    Swain, K.C., C. Singha, and L. Nayak. 2020. Flood susceptibility mapping through the GIS-AHP technique using the cloud. ISPRS International Journal of Geo-Information 9(12):Article 720.
    Tehrany, M.S., M.J. Lee, B. Pradhan, M.N. Jebur, and S. Lee. 2014. Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environmental Earth Sciences 72(10):4001-4015.
    Tehrany, M.S., B. Pradhan, and M.N. Jebur. 2013. Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of Hydrology 504:69-79.
    Tehrany, M.S., B. Pradhan, S. Mansor, and N. Ahmad. 2015. Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena 125:91-101.
    Tiryaki, M., and O. Karaca. 2018. Flood susceptibility mapping using GIS and multicriteria decision analysis:Saricay-Çanakkale (Turkey). Arabian Journal of Geosciences 11(14):Article 364.
    Ullah, K., and J. Zhang. 2020. GIS-based flood hazard mapping using relative frequency ratio method:A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan. PLOS ONE 15(3):Article e0229153.
    Ullah, K., Y. Wang, Z. Fang, L. Wang, and M. Rahman. 2022. Multi-hazard susceptibility mapping based on convolutional neural networks. Geoscience Frontiers 13(5):Article 101425.
    Wang, H.B., K. Sassa, and W.Y. Xu. 2007. Analysis of a spatial distribution of landslides triggered by the 2004 Chuetsu earthquakes of Niigata Prefecture, Japan. Natural Hazards 41(1):43-60.
    Wang, L.-J., M. Guo, K. Sawada, J. Yin, and J. Zhang. 2016. A comparative studyof landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network. Geosciences Journal 20(1):117-136.
    Wang, Y., Z. Fang, H. Hong, and L. Peng. 2020a. Flood susceptibility mapping using convolutional neural network frameworks. Journal of Hydrology 582:Article 124482.
    Wang, Z., Q. Liu, and Y. Liu. 2020b. Mapping landslide susceptibility using machine learning algorithms and GIS:A case study in Shexian County, Anhui Province, China. Symmetry 12(12):Article 1954.
    Wubalem Azeze, A., G. Tesfaw, Z. Dawit, B. Getahun, T. Mekuria, and J. Muralitharan. 2021. Comparison of statistical and analytical hierarchy process methods on flood susceptibility mapping:In a case study of the Lake Tana sub-basin in northwestern Ethiopia. Open Geosciences 13(1):1668-1688.
    Yanar, T., S. Kocaman, and C. Gokceoglu. 2020. Use of Mamdani Fuzzy Algorithm for multi-hazard susceptibility assessment in a developing urban settlement (Mamak, Ankara, Turkey). ISPRS International Journal of Geo-Information 9(2):Article 114.
    Yi, Y., Z. Zhang, W. Zhang, Q. Xu, C. Deng, and Q. Li. 2019. GIS-based earthquake-triggered-landslide susceptibility mapping with an integrated weighted index model in Jiuzhaigou region of Sichuan Province, China. Natural Hazards and Earth System Sciences 19(9):1973-1988.
    Youssef, A.M., A.M. Mahdi, M.M. Al-Katheri, S. Pouyan, and H.R. Pourghasemi. 2023. Multi-hazards (landslides, floods, and gully erosion) modeling and mapping using machine learning algorithms. Journal of African Earth Sciences 197:Article 104788.
    Yousefi, S., H.R. Pourghasemi, S.N. Emami, S. Pouyan, S. Eskandari, and J.P. Tiefenbacher. 2020. A machine learning framework for multi-hazards modeling and mapping in a mountainous area. Scientific Reports 10(1):Article 12144.
  • 加载中


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (259) PDF downloads(0) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint