An artificial neural network model to predict debris-flow volumes caused by extreme rainfall in the central region of South Korea
- 19 December 2020
- journal article
- research article
- Published by Elsevier BV in Engineering Geology
- Vol. 281, 105979
- https://doi.org/10.1016/j.enggeo.2020.105979
Abstract
No abstract availableKeywords
Funding Information
- Ministry of Education (NRF-2020R1A6A3A01100247)
- Ministry of Science, ICT and Future Planning (NRF-2018R1A4A1025765)
- National Research Foundation of Korea
This publication has 32 references indexed in Scilit:
- Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modellingEnvironmental Modelling & Software, 2010
- Early warning of rainfall-induced shallow landslides and debris flows in the USALandslides, 2009
- The rainfall intensity–duration control of shallow landslides and debris flows: an updateLandslides, 2007
- The significance of channel recharge rates for estimating debris‐flow magnitude and frequencyEarth Surface Processes and Landforms, 2005
- Estimation of debris‐flow magnitude in the Eastern Italian AlpsEarth Surface Processes and Landforms, 2004
- Entrainment of debris in rock avalanches: An analysis of a long run-out mechanismGSA Bulletin, 2004
- Verification of the nonparametric characteristics of backpropagation neural networks for image classificationIEEE Transactions on Geoscience and Remote Sensing, 1999
- Importance of input data normalization for the application of neural networks to complex industrial problemsIEEE Transactions on Nuclear Science, 1997
- The angle of reach as a mobility index for small and large landslidesCanadian Geotechnical Journal, 1996
- Quantitative analysis of debris torrent hazards for design of remedial measuresCanadian Geotechnical Journal, 1984