Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2024

Abstract

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 the integration of a comprehensive dataset comprising historical landslide events and various geo-environmental variables within a spatially explicit machine learning framework. The study empirically demonstrates that incorporating big spatial data allows a more nuanced understanding of local variations and spatial dependencies. Ultimately, this empirical assessment produces more accurate landslide risk predictions than traditional baseline models. Using Italy’s expansive Valtellina Valley as a case study covering over 3,308 km2, the study illustrates the potential of this integrated approach to enhance predictive outcomes and improve the granularity of the produced landslide susceptibility risk map. The study findings underscore the transformative potential of big spatial data in improving landslide susceptibility assessment and supporting informed decisions in disaster risk management and preparedness.

Keywords

Big Data, Geospatial Machine Learning, Landslide Susceptibility, Random Forest, Spatial Nonstationarity

Discipline

Geographic Information Sciences | Numerical Analysis and Scientific Computing

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

BigSpatial '24: Proceedings of the 12th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, Atlanta, October 29 - November 1

First Page

10

Last Page

19

ISBN

9798400711435

Identifier

10.1145/3681763.3698477

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3681763.3698477

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