An interpretable model for landslide susceptibility assessment based on Optuna hyperparameter optimization and Random Forest-Reference-Cited by-同舟云学术

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An interpretable model for landslide susceptibility assessment based on Optuna hyperparameter optimization and Random Forest

Published:2024-05-11 Issue:1 Volume:15 Page:
ISSN:1947-5705
Container-title:Geomatics, Natural Hazards and Risk
language:en
Short-container-title:Geomatics, Natural Hazards and Risk

Author:

Xiao Xin¹,Zou Yi¹,Huang Jiangcheng¹²,Luo Xuan¹,Yang Luyi¹,Li Meng³,Yang Pengwu³,Ji Xuan¹²,Li Yungang¹²

Affiliation:

1. Institute of International Rivers and Eco-security, Yunnan University, Kunming, China

2. Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security, Yunnan University, Kunming, China

3. Yunnan Meteorological Bureau, Kunming, China

Funder

National Key Research and Development Program of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/19475705.2024.2347421

Reference76 articles.

1. Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model

2. Akiba T, Sano S, Yanase T, Ohta T, Koyama M. 2019. Optuna: a next-generation hyperparameter optimization framework. KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data; p. 2623–2631. doi: 10.1145/3292500.3330701.

3. Landslide susceptibility mapping and rock slope stability assessment using frequency ratio and kinematic analysis in the mountains of Mgulat area, Northern Ethiopia

4. Comparing models of debris-flow susceptibility in the alpine environment

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1. Integrating ensemble machine learning and explainable AI for enhanced forest fire susceptibility analysis and risk assessment in Türkiye’s Mediterranean region;Earth Science Informatics;2024-09-06

2. Ensemble learning landslide susceptibility assessment with optimized non-landslide samples selection;Geomatics, Natural Hazards and Risk;2024-07-17

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