nach oben

Bulletin of Engineering Geology and the Environment

Erschienen in:

01.06.2024 | Original Paper

Rockfall susceptibility assessment in Kuandian County (Northeast China) by combining information content method and analytic hierarchy process

verfasst von: Lianhuan Wei, Yaxin Xu, Donglin Lv, Huashuo Cui, Shanjun Liu, Meng Ao

Erschienen in: Bulletin of Engineering Geology and the Environment | Ausgabe 6/2024

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The Kuandian County (Northeast China) has suffered frequent rockfall disasters because of its topography, geological setting, and anthropogenic activities. This research work conducts rockfall susceptibility assessment in Kuandian County by comprehensively combining the Information Content Method (ICM) with the Analytic Hierarchy Process (AHP). Firstly, information contents of the evaluation factors are calculated via statistical analysis on the existing rockfall sites. Then, weights of the evaluation factors are determined by adopting AHP. In the final step, the susceptibility value of each evaluation unit is obtained by weighted summation of the information content of each evaluation factor. The combination of ICM and AHP is able to promote the objectiveness of the evaluation process and accuracy of the evaluation results. Furthermore, in order to analyze the influence of evaluation factor setting on susceptibility, assessments using reduced number of evaluation factors are also conducted for comparative analysis. Additionally, to analyze the influence of evaluation units on susceptibility, comparative analysis on the assessment results from both slope units and grid units in a small area is performed. The susceptibility assessment results presented in this study are generally consistent with the current situation of rockfall disasters in Kuandian County, providing reliable data support for disaster prevention and mitigation. Furthermore, the outcome of this study serves as a methodological reference for rockfall risk assessment in Northeast China and other mountainous regions.

Vorheriger Artikel Mechanical behavior of rock-like specimens with 3D nonpenetrating and nonpersistent rough joints under uniaxial compression: experimental study

Nächster Artikel Identifying potential debris flow hazards after the 2022 Mw 6.8 Luding earthquake in southwestern China

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Achour Y, Boumezbeur A, Hadji R, Chouabbi A, Cavaleiro V, Bendaoud EA (2017) Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine, Algeria. Arab J Geosci 10(8). https://doi.org/10.1007/s12517-017-2980-6

Akgun A, Dag S, Bulut F (2008) Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environ Geol 54(6):1127–1143. https://doi.org/10.1007/s00254-007-0882-8CrossRef

Alaba PA, Popoola SI, Olatomiwa L, Akanle MB, Ohunakin OS, Adetiba E, Alex OD, Atayero AAA, Daud WMAW (2019) Towards a more efficient and cost-sensitive extreme learning machine: a state-of-the-art review of recent trend. Neurocomputing 350:70–90. https://doi.org/10.1016/j.neucom.2019.03.086CrossRef

Alvioli M, Guzzetti F, Marchesini I (2020) Parameter-free delineation of slope units and terrain subdivision of Italy. Geomorphology 358. https://doi.org/10.1016/j.geomorph.2020.107124

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(1):73–81. https://doi.org/10.1007/s10346-003-0006-9CrossRef

Baioni D (2011) Human activity and damaging landslides and floods on Madeira Island. Nat Hazards Earth Syst Sci 11(11):3035–3046. https://doi.org/10.5194/nhess-11-3035-2011CrossRef

Bounemeur N, Benzaid R, Kherrouba H, Atoub S (2022) Landslides in Mila town (northeast Algeria): causes and consequences. Arab J Geosci 15. https://doi.org/10.1007/s12517-022-09959-7

China Geological Environment Monitoring Institute, Xi'an Geological Survey Centre of China Geological Survey, Institute of Geomechanics of Chinese Academy of Geological Sciences, Chengdu Geological Survey Centre of China Geological Survey, Aerial Remote Sensing Centre of Ministry of Land and Resources, Sichuan Geological Survey Institute (2014) Landslide collapse debris flow disaster investigation specification (1:50000). Ministry of Land and Resources of the People 's Republic of China, Beijing

Choi J, Oh HJ, Lee HJ, Lee C, Lee S (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol 124:12–23. https://doi.org/10.1016/j.enggeo.2011.09.011CrossRef

Chung CJ, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30(3):451–472. https://doi.org/10.1023/B:NHAZ.0000007172.62651.2bCrossRef

Corominas J, Van Westen C, Frattini P, Cascini L, Malet JP, Fotopoulou S, Catani F, Van Den Eeckhaut M, Mavrouli O, Agliardi F, Pitilakis K, Winter MG, Pastor M, Ferlisi S, Tofani V, Hervas J, Smith JT (2014) Recommendations for the quantitative analysis of landslide risk. B Eng Geol Environ 73:209–263. https://doi.org/10.1007/s10064-013-0538-8CrossRef

Edmonds C, Noy I (2018) The economics of disaster risks and impacts in the Pacific. Disaster Prev Manag 27:478–494. https://doi.org/10.1108/DPM-02-2018-0057CrossRef

Gawlikowski J, Tassi CRN, Ali M, Lee JS, Humt M, Feng JX, Kruspe A, Triebel R, Jung PT, Roscher R, Shahzad M, Yang W, Bamler R, Zhu XX (2023) A survey of uncertainty in deep neural networks. Artif Intell Rev 56(1):1513–1589. https://doi.org/10.1007/s10462-023-10562-9CrossRef

Ghiasi V, Ghasemi SAR, Yousefi M (2021) Landslide susceptibility mapping through continuous fuzzification and geometric average multi-criteria decision-making approaches. Nat Hazards 107(1):795–808. https://doi.org/10.1007/s11069-021-04606-yCrossRef

Gschwind S, Loew S, Wolter A (2019) Multi-stage structural and kinematic analysis of a retrogressive rock slope instability complex (Preonzo, Switzerland). Eng Geol 252:27–42. https://doi.org/10.1016/j.enggeo.2019.02.018CrossRef

Haggett P, Chorley RJ (2014) Physical and information models in geography (Routledge Revivals)

He K, Jia Y, Wang B, Wang R, Luo H (2013) Comprehensive fuzzy evaluation model and evaluation of the karst collapse susceptibility in Zaozhuang Region, China. Nat Hazards 68(2):613–629. https://doi.org/10.1007/s11069-013-0653-7CrossRef

Highland LM, Bobrowsky P (2008) The landslide handbook—a guide to understanding landslides. U.S. Geological Survey Circular 1325, Reston Virgini

Hong Q, Lai H, Liu Y (2023) Failure analysis and treatments of collapse accidents in loess tunnels. Eng Fail Anal 145:107037. https://doi.org/10.1016/j.engfailanal.2022.107037CrossRef

Hou J, Lv J, Chen X, Yu S (2016) China’s regional social vulnerability to geological disasters: evaluation and spatial characteristics analysis. Nat Hazards 84:S97–S111. https://doi.org/10.1007/s11069-015-1931-3CrossRef

Hu Y, Chapman A, Wen GH, Hall DW (2022) What can knowledge bring to machine learning? —a survey of low-shot learning for structured data. Acm T Intel Syst Tec 13(3):1–45. https://doi.org/10.1145/3510030CrossRef

Karpatne A, Ebert-Uphoff I, Ravela S, Babaie HA, Kumar V (2019) Machine learning for the geosciences: challenges and opportunities. IEEE Trans Knowl Data Eng 31(8):1544–1554. https://doi.org/10.1109/TKDE.2018.2861006CrossRef

Kherrouba H, Lamara M, Benzaid R (2022) Geological and geophysical characterization using electrical resistivity imaging of certain landslides at Djimla region (Jijel, Northeast Algeria). In: Advances in geophysics, tectonics and petroleum geosciences. pp. 305–308. https://doi.org/10.1007/978-3-030-73026-0_70

Kumar V, Azamathulla HM, Sharma KV, Mehta DJ, Maharaj KT (2023) The state of the art in deep learning applications, challenges, and future prospects: a comprehensive review of flood forecasting and management. Sustainability 15(13). https://doi.org/10.3390/su151310543

Li Y, Deng X, Ji P, Yang Y, Jiang W, Zhao Z (2022) Evaluation of landslide susceptibility based on CF-SVM in Nujiang prefecture. Int J Environ Res Public Health 19(21). https://doi.org/10.3390/ijerph192114248

Li Y, Sheng Y, Chai B, Zhang W, Zhang T, Wang J (2020) Collapse susceptibility assessment using a support vector machine compared with back-propagation and radial basis function neural networks. Geomat Nat Haz Risk 11(1):510–534. https://doi.org/10.1080/19475705.2020.1734101CrossRef

Li ZH, Chen JP, Tan C, Zhou X, Li YC, Han MX (2021) Debris flow susceptibility assessment based on topo-hydrological factors at different unit scales: a case study of Mentougou district, Beijing. Environ Earth Sci 80(9). ARTN 365 https://doi.org/10.1007/s12665-021-09665-9

Liu S, Zhu J, Yang D, Ma B (2022) Comparative study of geological hazard evaluation systems using grid units and slope units under different rainfall conditions. Sustainability-Basel 14(23). https://doi.org/10.3390/su142316153

Lyu HM, Shen JS, Arulrajah A (2018) Assessment of geohazards and preventative countermeasures using AHP incorporated with GIS in Lanzhou, China. Sustainability-Basel 10(2). https://doi.org/10.3390/su10020304

Martinez AD, Labib SM (2023) Demystifying normalized difference vegetation index (NDVI) for greenness exposure assessments and policy interventions in urban greening. Environ Res 220. https://doi.org/10.1016/j.envres.2022.115155

Munier N, Hontoria E (2021) Uses and limitations of the AHP method a non-mathematical and rational analysis. Springer, Berlin https://doi.org/10.1007/978-3-030-60392-2

Osna T, Sezer EA, Akgun A (2014) GeoFIS: An integrated tool for the assessment of landslide susceptibility. Comput Geosci-UK 66:20–30. https://doi.org/10.1016/j.cageo.2013.12.016CrossRef

Papadopoulou-Vrynioti K, Bathrellos GD, Skilodimou HD, Kaviris G, Makropoulos K (2013) Karst collapse susceptibility mapping considering peak ground acceleration in a rapidly growing urban area. Eng Geol 158:77–88. https://doi.org/10.1016/j.enggeo.2013.02.009CrossRef

Peduzzi P, Dao H, Herold C, Mouton F (2009) Assessing global exposure and vulnerability towards natural hazards: the disaster risk index. Nat Hazards Earth Sys 9(4):1149–1159. https://doi.org/10.5194/nhess-9-1149-2009CrossRef

Pichler M, Hartig F (2023) Machine learning and deep learning—a review for ecologists. Methods Ecol Evol 14(4):994–1016. https://doi.org/10.1111/2041-210X.14061CrossRef

Pourghasemi HR, Rossi M (2017) Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods. Theor Appl Climatol 130(1–2):609–633. https://doi.org/10.1007/s00704-016-1919-2CrossRef

Reid CE, Kubzansky LD, Li JY, Shmool JL, Clougherty JE (2018) It’s not easy assessing greenness: A comparison of NDVI datasets and neighborhood types and their associations with self-rated health in New York City. Health Place 54:92–101. https://doi.org/10.1016/j.healthplace.2018.09.005CrossRef

Rossi M, Guzzetti F, Reichenbach P, Mondini AC, Peruccacci S (2010) Optimal landslide susceptibility zonation based on multiple forecasts. Geomorphology 114(3):129–142. https://doi.org/10.1016/j.geomorph.2009.06.020CrossRef

Saaty TL (1978) Modeling unstructured decision problems—the theory of analytical hierarchies. Math Comput Simulat 20(3):147–158. https://doi.org/10.1016/0378-4754(78)90064-2CrossRef

Shahabi H, Hashim M (2015) Landslide susceptibility mapping using GIS-based statistical models and remote sensing data in tropical environment. Sci Rep-UK 5. https://doi.org/10.1038/srep09899

Shi B, Zeng T, Tang C, Zhang L, Xie Z, Lv G, Wu Q (2021) Landslide risk assessment using granular fuzzy rule-based modeling: a case study on earthquake-triggered landslides. IEEE Access 9:135790–135802. https://doi.org/10.1109/ACCESS.2021.3116869CrossRef

Sun L, Ma C, Li Y (2019) Multiple geo-environmental hazards susceptibility assessment: a case study in Luoning County, Henan Province, China. Geomat Nat Haz Risk 10(1):2009–2029. https://doi.org/10.1080/19475705.2019.1658648CrossRef

Süzen ML, Doyuran V (2004) Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment ,Turkey. Eng Geol 71(3–4):303–321. https://doi.org/10.1016/S0013-7952(03)00143-1CrossRef

Tan Q, Huang Y, Hu J, Zhou P, Hu J (2021) Application of artificial neural network model based on GIS in geological hazard zoning. Neural Comput Appl 33(2):591–602. https://doi.org/10.1007/s00521-020-04987-4CrossRef

Wang F, Xu P, Wang C, Wang N, Jiang N (2017) Application of a GIS-Based slope unit method for landslide susceptibility mapping along the Longzi River, southeastern Tibetan Plateau, China. ISPRS Int J Geo-Inf 6(6). https://doi.org/10.3390/ijgi6060172

Wang Y, Nie L, Zhang M, Wang H, Zuo T (2020) Assessment of debris flow risk factors based on meta-analysis—cases study of northwest and southwest China. Sustainability-Basel 12(17). https://doi.org/10.3390/su12176841

Wu S, Chen J, Zhou W, Iqbal J, Yao L (2019) A modified Logit model for assessment and validation of debris-flow susceptibility. B Eng Geol Environ 78(6):4421–4438. https://doi.org/10.1007/s10064-018-1412-5CrossRef

Xie YH, Zhang BH, Liu YX, Liu BC, Zhang CF, Lin YS (2022) Evaluation of the karst collapse susceptibility of subgrade based on the AHP method of ArcGIS and prevention measures: a case study of the Quannan expressway, section K1379+300-K1471+920. Water-SUI 14(9). https://doi.org/10.3390/w14091432

Xu D, Peng L, Liu S, Su C, Wang X, Chen T (2017) Influences of sense of place on farming households’ relocation willingness in areas threatened by geological disasters: evidence from China. Int J Disast Risk Sc 8(1):16–32. https://doi.org/10.1007/s13753-017-0112-2CrossRef

Yan JL, Wang J, Song SJ, Zhao ZH (2012) Zoning evaluation of geological hazard in YuLin coal mining area. Adv Mater 599:245–249. https://doi.org/10.4028/www.scientific.net/AMR.599.245CrossRef

Yang Y, Yu J, Fu Y, Hu J (2019) Research on geological hazard risk assessment based on the cloud fuzzy clustering algorithm. J Intrell Fuzzy Syst 37(4):4763–4770. https://doi.org/10.3233/JIFS-179311CrossRef

Yesilnacar E, Topal T (2005) Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79(3–4):251–266. https://doi.org/10.1016/j.enggeo.2005.02.002CrossRef

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). Comput Geosci-UK 35(6):1125–1138. https://doi.org/10.1016/j.cageo.2008.08.007CrossRef

Yuan X, Liu C, Nie R, Yang Z, Li W, Dai X, Cheng J, Zhang J, Ma L, Fu X, Tang M, Xu Y, Lu H (2022) A comparative analysis of certainty factor-based machine learning methods for collapse and landslide susceptibility mapping in Wenchuan county, China. Remote Sens 14(14). https://doi.org/10.3390/rs14143259

Zhang J, Chen Y, Liang BF, Pan B (2021) Damage evolution mechanism of buried pipeline crossing reverse fault. J Mech Sci Technol 35(1):71–77. https://doi.org/10.1007/s12206-020-1206-0CrossRef

Zhang Z, Zhang S, Hu C, Zhang X, Yang S, Yan H, Zhang Z (2023) Hazard assessment model of ground subsidence coupling AHP, RS and GIS—A case study of Shanghai. Gondwana Res 117:344–362. https://doi.org/10.1016/j.gr.2023.01.014CrossRef

Zheng Q, Lyu HM, Zhou A, Shen SL (2021) Risk assessment of geohazards along Cheng-Kun railway using fuzzy AHP incorporated into GIS. Geomat Nat Haz Risk 12(1):1508–1531. https://doi.org/10.1080/19475705.2021.1933614CrossRef

Zhou NQ, Zhao S (2013) Urbanization process and induced environmental geological hazards in China. Nat Hazards 67(2):797–810. https://doi.org/10.1007/s11069-013-0606-1CrossRef

Titel: Rockfall susceptibility assessment in Kuandian County (Northeast China) by combining information content method and analytic hierarchy process
verfasst von: Lianhuan Wei
Yaxin Xu
Donglin Lv
Huashuo Cui
Shanjun Liu
Meng Ao
Publikationsdatum: 01.06.2024
Verlag: Springer Berlin Heidelberg
Erschienen in: Bulletin of Engineering Geology and the Environment / Ausgabe 6/2024
Print ISSN: 1435-9529
Elektronische ISSN: 1435-9537
DOI: https://doi.org/10.1007/s10064-024-03739-1

The use of digital technology for rock mass discontinuity mapping: review of benchmarking exercise

Review Paper

Rock fracture identification algorithm based on the confidence score and non-maximum suppression

Original Paper

The dynamic damage pattern and fine-scale damage characteristics of weathered granite after ore leaching

Original Paper

A model for characterization of crack closure stage in stress–strain curves of Suizhou granite after exposure to various thermal shocks

Original Paper

Seismotectonic and anthropogenic factors controlling landslide hazard in Gafsa phosphates quarries

Original Paper

Analysis of loess water migration regularity and failure response of tunnel structure under rainfall environment

Review Paper