In this work, a practical Machine Learning (ML) approach is proposed to produce Landslide Susceptibility Mapping (LSM) by exploiting multiple data sources.
In this work, a practical Machine Learning (ML) approach is proposed to produce Landslide Susceptibility Mapping. (LSM) by exploiting multiple data sources. In ...
MACHINE LEARNING-BASED APPROACH FOR LANDSLIDE SUSCEPTIBILITY MAPPING USING MULTIMODAL DATA ; Session: TUP.P16: Machine Learning for Clouds, Dust, and Hazards ...
ABSTRACT. In this work, a practical Machine Learning (ML) approach is proposed to produce Landslide Susceptibility Mapping.
The present study attempts to quantify the uncertainty associated with landslide prediction models by developing a new methodological framework.
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Dec 16, 2021 · We developed a deep convolutional neural network (CNN–DNN) for mapping landslide susceptibility, and evaluated it on the Isfahan province, Iran.
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May 27, 2022 · The aim of this study is to conduct the first landslide susceptibility mapping (LSM) in Denmark, reducing the geographical bias existing in LSM studies.
Here, we present an overview of the most popular machine learning techniques available for landslide susceptibility studies.
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Important considerations in machine learning-based landslide ...
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Aug 3, 2024 · In this work, we introduce methodologies to address these challenges using XGBoost to train the landslide prediction model.
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We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an ensemble model (AB-ADTree) to spatially predict landslides in the ...
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