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2024 | OriginalPaper | Buchkapitel

1. Landslide Susceptibility Assessment Based on Machine Learning Techniques

verfasst von : Jierui Li, Wen He, Lingke Qiu, Wen Zeng, Baofeng Di

Erschienen in: Geomorphic Risk Reduction Using Geospatial Methods and Tools

Verlag: Springer Nature Singapore

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Abstract

In this chapter, we will introduce the landslide susceptibility assessment (LSA) methods based on machine learning techniques. The economic loss or even casualties caused by landslides indicate the significance of LSA. LSA can be regarded as either regression or classification problems, which can be processed by machine learning techniques. LSA provides administrators or researchers with information on potential disaster areas, which can be an efficient way to relieve the pressure of disaster reduction and mitigation. Several landslide inventories and disaster-related geo-environmental variable datasets were recommended. A total of 9 machine learning methods applied in LSA were simply introduced. The advantages and future work of LSA based on machine learning techniques were summarized from the aspects of scale, performance, modeling, and interpretability.

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Titel: Landslide Susceptibility Assessment Based on Machine Learning Techniques
verfasst von: Jierui Li
Wen He
Lingke Qiu
Wen Zeng
Baofeng Di
Verlag: Springer Nature Singapore
Buch: Geomorphic Risk Reduction Using Geospatial Methods and Tools
Print ISBN: 978-981-9977-06-2

Electronic ISBN: 978-981-9977-07-9

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-99-7707-9_1

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