A data-driven method for predicting debris-flow runout zones by integrating multivariate adaptive regression splines and Akaike information criterion

Author:

Tian Mi,Li Lihua,Xiong Zimin

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hubei Province

Open Research Fund of Rock Mechanics in Hydraulic Structural Engineering of Ministry of Education, Wuhan University

Publisher

Springer Science and Business Media LLC

Subject

Geology,Geotechnical Engineering and Engineering Geology

Reference63 articles.

1. Asmar BN, Langston PA, Ergenzinger P (2003) The potential of the discrete element method to simulate debris flow. In: Rickenmann D, Chen C-L (eds) Proceedings of the Third International Conference on Debris Flow Hazard Mitigation: Mechanics, Prediction, and Assessment, Davos. Millpress, Rotterdam, Netherlands, pp 435–445

2. Bathurst JC, Burton A, Ward TJ (1997) Debris flow run-out and landslide sediment delivery model tests. J Hydraul Eng 123:410–419. https://doi.org/10.1061/(asce)0733-9429(1997)123:5(410)

3. Berti M, Simoni A (2007) Prediction of debris flow inundation areas using empirical mobility relationships. Geomorphology 90:144–161. https://doi.org/10.1016/j.geomorph.2007.01.014

4. Bishop CM (2006) Pattern recognition and machine learning. Springer, Singapore

5. Chang CW, Lin PS, Tsai CL (2011) Estimation of sediment volume of debris flow caused by extreme rainfall in Taiwan. Eng Geol 123:83–90. https://doi.org/10.1016/j.enggeo.2011.07.004

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