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All issues Volume 143 (2020) E3S Web Conf., 143 (2020) 02032 References
Open Access
Issue
E3S Web Conf.
Volume 143, 2020
2nd International Symposium on Architecture Research Frontiers and Ecological Environment (ARFEE 2019)
Article Number 02032
Number of page(s) 5
Section Environmental Science and Energy Engineering
DOI https://doi.org/10.1051/e3sconf/202014302032
Published online 24 January 2020
  1. Bălteanu Dan, Chendeş Viorel, Sima Mihaela., et al. A country-wide spatial assessment of landslide susceptibility in Romania[J]. Geomorphology, 124 (3-4):102-112(2010). [CrossRef] [Google Scholar]
  2. Chun-Hung Wu, Su-Chin Chen. Determining landslide susceptibility in Central Taiwan from rainfall and six site factors using the analytical hierarchy process method[J]. Geomorphology, 112(3-4):190-204(2009). [CrossRef] [Google Scholar]
  3. Pradhan B, Lee S. Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling[J]. Environmental Modelling & Software, 25(6):747-759(2010). [Google Scholar]
  4. Bonaventura Firman Dwi Wahono.Applications of Statistical and Heuristic Methods for Landslide Susceptibility Assessments[D]. Thesis of Gadjah Mada University(2010). [Google Scholar]
  5. Nandi A, Shakoor A. A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses[J]. Engineering Geology, 110(1-2):11-20(2010). [Google Scholar]
  6. Pradhan B. Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia[J]. Advances in Space Research, 45(10):1244-1256(2010). [CrossRef] [Google Scholar]
  7. Isik Y. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslide(Tokat-Turkey)[J]. Computers & Geosciences, 35(6):1125-1138(2009). [Google Scholar]
  8. Kawabata D, Bandibas J. Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN)[J]. Geomorphology, 113(1-2):97-109(2009). [CrossRef] [Google Scholar]
  9. Saro Lee, Moung-Jin Lee.Detecting landslide location using KOMPSAT 1 and its application to landslidesusceptibility mapping at the Gangneung area, Korea[J]. Advances in Space Research, 38(10):2261-2271(2006). [CrossRef] [Google Scholar]
  10. Dahal R K, Hasegawa S, Nonomura A, et al. GISbased weights-of-evidence modelling of rainfallinduced landslides in small catchments for landslide susceptibility mapping[J]. Environmental Geology, 54(2):311-324(2008). [CrossRef] [Google Scholar]
  11. Dahal R K, Hasegawa S, Nonomura A, et al. Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence[J]. Geomorphology, 102(3-4):0-510(2008). [Google Scholar]
  12. Dai F, Lee C. Frequency-volume relation and prediction of rainfall-induced landslides[J]. Engineering Geology, 59(3):253-266(2001). [Google Scholar]
  13. Sengupta A, Gupta S, Anbarasu K. Rainfall thresholds for the initiation of landslide at Lanta Khola in north Sikkim, India[J]. Natural Hazards, 52(1):31-42(2010). [CrossRef] [Google Scholar]
  14. Floris M, Bozzano F. Evaluation of landslide reactivation: A modified rainfall threshold model based on historical records of rainfall and landslides[J]. Geomorphology, 94(1-2):0-57(2008). [Google Scholar]
  15. Yan Tongzhen, Wu Faquan, Yin Kunlong. Static and Dynamic Regularity of Landslides and Space-time Prognosis of Slope Instability[J]. Earth Science(Journal of China University of Geosciences), 2:117-133(1989). [Google Scholar]

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