From data to application: Harnessing big spatial data and spatially explicit machine learning model for landslide susceptibility prediction and mapping

From data to application: Harnessing big spatial data and spatially explicit machine learning model for landslide susceptibility prediction and mapping

Recent advancements in information and communication technology have significantly enhanced access to extensive geospatial data, presenting a valuable opportunity to leverage big spatial data for improved modeling and predictive capabilities in natural disaster risk assessment. This paper explores t...

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Bibliographic Details
Main Authors: KHANT, Min Naing, ANN, Mei Yi Victoria Grace, KAM, Tin Seong
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2024
Subjects:
Big Data
Geospatial Machine Learning
Landslide Susceptibility
Random Forest
Spatial Nonstationarity
Geographic Information Sciences
Numerical Analysis and Scientific Computing
Online Access:https://ink.library.smu.edu.sg/sis_research/9835
https://ink.library.smu.edu.sg/context/sis_research/article/10835/viewcontent/3681763.3698477_pvoa_cc_by.pdf
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/sis_research/9835
https://ink.library.smu.edu.sg/context/sis_research/article/10835/viewcontent/3681763.3698477_pvoa_cc_by.pdf

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