Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Concept on Landslides and Landslide Susceptibility Read chapter Read first chapter

2019 | OriginalPaper | Chapter

7. Knowledge-Driven Statistical Approach for Landslide Susceptibility Assessment Using GIS and Fuzzy Logic (FL) Approach

Authors : Sujit Mandal, Subrata Mondal

Published in: Statistical Approaches for Landslide Susceptibility Assessment and Prediction

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The present study is dealt with the application of fuzzy logic and preparation of landslide susceptibility zonation map of Darjeeling Himalaya on GIS environment. To accomplish fuzzy logic, several data layers such as elevation, slope, aspect, curvature, drainage density, distance to drainage, lineament density, distance to lineament, lithology, land use and land cover, soil, stream power index (SPI), topographic wetness index (TWI), and rainfall were made in consultation with topographical map, Google earth images, satellite imageries, and some other authorized maps. For computing fuzzy membership value and developing the model frequency ratio and cosine amplitude, values were derived corresponding to each class of the landslide causative factor. Then, fuzzy gamma operator value of 0.975 was used to prepare landslide susceptibility zonation map of Darjeeling Himalaya considering frequency ratio and cosine amplitude membership value. The accuracy study based on ROC curve revealed that the FR membership value based fuzzy gamma operator and landslide susceptibility map having the accuracy result of 80.9% and cosine amplitude membership value based landslide susceptibility having the validation result of 67.9%.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Weighted Overlay Analysis (WOA) Model, Certainty Factor (CF) Model and Analytical Hierarchy Process (AHP) Model in Landslide Susceptibility Studies
next chapter Comparison Between Statistical Models: A Review and Evaluation
Literature
Atkinson, P. M., & Massari, R. (1998). Generalized linear modelling of susceptibility to land sliding in the central Apennines, Italy. Computer and Geosciences, 24, 373–385.CrossRef
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.CrossRef
Carrara, A., Cardinali, M., Detti, R., Guzzetti, F., Pasqui, V., & Reichenbach, P. (1991). GIS techniques and statistical models in evaluating landslide hazard. Earth Surface Processes and Landforms, 16, 427–452.CrossRef
Champatiray, P. (2000). Perationalization of costeffective methodology for landslide hazard zonation using RS and GIS: IIRS initiative. In P. Roy, C. Van Westen, V. Jha, & R. Lakhera (Eds.), Natural disasters and their mitigation; Remote sensing and geographical information system perspectives (pp. 95–101). Dehradun, India: Indian Institute of Remote Sensing.
Dai, F. C., & Lee, C. F. (2002). Landslide characteristics and slope instability modelling usingGIS, Lantau Island, Hong Kong. Geomorphology, 42, 213–228.CrossRef
Dai, 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, 381–391.CrossRef
Ermini, L., Catani, F., & Casagli, N. (2005). Artificial neural network applied to landslide susceptibility assessment. Geomorphology, 66, 327–343.CrossRef
Kanungo, D. P., Arora, M. K., Sarkar, S., & Gupta, R. P. (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, 347–366.CrossRef
Lepore, C., Kamal, S. A., Shanahan, P., & Bras, R. L. (2012). Rainfall induced landslide susceptibility zonation of Puerto Rico. Environmental Earth Sciences, 66, 1–15.CrossRef
Liu, J. G., Mason, P. J., Clerici, N., Chen, S., Davis, A., Miao, F., Deng, F. L., & Liang, L. (2004). Landslide hazard assessment in the Three Gorges area of the Yangtze River using ASTER imagery: Zigui–Badong. Geomorphology, 61, 171–187.CrossRef
Mitchell, T. M. (1997). Machine learning (p. 414). New York: McGraw-Hill.
Montgomery, D. R., & Dietrich, W. E. (1989). Source areas, drainage density and channel initiation. Water Resources Research, 25(8), 1907–1918.CrossRef
Montgomery, D. R., & Dietrich, W. E. (1994). A physically based model for the topographic control on shallow land sliding. Water Resources Research, 30(4), 1153–1171.CrossRef
Pradhan, B. (2010). Application of an advanced fuzzy logic model for landslide susceptibility analysis. International Journal of Computational Intelligence Systems, 3(3), 370–381.CrossRef
Pradhan, B., Lee, S., & Buchroithner, M. F. (2009). Use of geospatial data for the development of fuzzy algebraic operators to landslide hazard mapping: A case study in Malaysia. Applied Geomatics, 1, 3–15.CrossRef
Remondo, J., & Oguchi, T. (2009). GIS and SDA applications in geomorphology. Geomorphology, 111, 1–3.CrossRef
Ren, D., Fu, R., Leslie, L. M., & Dickinson, R. E. (2011). Predicting storm-triggered landslides. Bulletin of the American Meteorological Society, 92, 129–139.CrossRef
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.CrossRef
Sidle, R. C. (1991). A conceptual model of changes in root cohesion in response to vegetation management. Journal of Environmental Quality, 20(1), 43–52.CrossRef
Tofani, V., Dapporto, S., Vannocci, P., & Casagli, N. (2006). Infiltration, seepage and slopeinstability mechanisms during the 20–21 November 2000 rainstorm in central Italy Tuscany. Natural Hazards and Earth System Sciences, 6, 1025–1033. https://​doi.​org/​10.​5194/​nhess-6-1025.
Van Westen, C. J., Rengers, N., & Soeters, R. (2003). Use of geomorphological information in indirect landslide susceptibility assessment. Natural Hazards, 30, 399–419.CrossRef
Varnes, D., & IAEG. (1984). Landslide hazard zonation: A review of principle and practice (pp. 1–6). Paris: UNESCO.
Zezere, J., Ferreira, A., & Rodrigues, M. (1999). The role of conditioning and triggering factors in the occurrence of landslides: A case study in the area north of Lisbon (Portugal). Geomorphology, 30, 133–146.CrossRef
Zhou, C. H., Lee, C. F., Li, J., & Xu, Z. W. (2002). On the spatial relationship between landslides and causative factors on Lantau Island, Hong Kong. Geomorphology, 43, 197–207.CrossRef
Zhu, A. X., Wang, R. X., Qiao, J., Chen, Y., Cai, Q., & Zhou, C. (2004). Mapping landslide susceptibility in the Three Gorges area, China using GIS, expert knowledge and fuzzy logic (pp. 385–391). In: Y. Chen (Ed.), GIS and remote sensing in hydrology. Water Resources and Environment, IAHS Publication 289 (IAHS Red Book), International Association of Hydrological Sciences, Wallingford, UK.
Metadata
Title
Knowledge-Driven Statistical Approach for Landslide Susceptibility Assessment Using GIS and Fuzzy Logic (FL) Approach
Authors
Sujit Mandal
Subrata Mondal
Copyright Year
2019
Book
Statistical Approaches for Landslide Susceptibility Assessment and Prediction

Print ISBN: 978-3-319-93896-7

Electronic ISBN: 978-3-319-93897-4

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-93897-4_7

Premium Partner

    Image Credits