Debris Flow Early Warning Model Based on Support Vector Machine

DOI

10.2991/978-94-6463-688-8_33How to use a DOI?

Keywords

Machine Learning; Support Vector Machine; Debris Flow Early Warning

Abstract

Debris flow is a significant geological hazard in mountainous regions, threatening human life and property. This study applies the Support Vector Machine (SVM) algorithm to predict debris flows in small watersheds, using Liangshan Yi Autonomous Prefecture as a case study. Eight key factors influencing debris flows were identified to construct a dataset, with each factor’s weight calculated. Model performance, evaluated using the Receiver Operating Characteristic (ROC) curve, showed an AUC of 0.84, indicating strong interpretability and efficiency. The model provides valuable technical support for debris flow disaster prevention in southwestern mountainous regions.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Shihai Ma
PY  - 2025
DA  - 2025/04/30
TI  - Debris Flow Early Warning Model Based on Support Vector Machine
BT  - Proceedings of the 2024 6th International Conference on Civil Architecture and Urban Engineering (ICCAUE 2024)
PB  - Atlantis Press
SP  - 325
EP  - 331
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-688-8_33
DO  - 10.2991/978-94-6463-688-8_33
ID  - Ma2025
ER  -