PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Framework to Create Dataset for Disaster Behavior Analysis using Google Earth Engine: A Case Study in Peninsular Malaysia for Historical Forest Fire Behavior Analysis
Version 1
: Received: 29 March 2024 / Approved: 1 April 2024 / Online: 1 April 2024 (10:35:32 CEST)
How to cite:
Chew, Y.J.; Ooi, S.Y.; Pang, Y.H.; Lim, Z.Y. Framework to Create Dataset for Disaster Behavior Analysis using Google Earth Engine: A Case Study in Peninsular Malaysia for Historical Forest Fire Behavior Analysis. Preprints2024, 2024040027. https://doi.org/10.20944/preprints202404.0027.v1
Chew, Y.J.; Ooi, S.Y.; Pang, Y.H.; Lim, Z.Y. Framework to Create Dataset for Disaster Behavior Analysis using Google Earth Engine: A Case Study in Peninsular Malaysia for Historical Forest Fire Behavior Analysis. Preprints 2024, 2024040027. https://doi.org/10.20944/preprints202404.0027.v1
Chew, Y.J.; Ooi, S.Y.; Pang, Y.H.; Lim, Z.Y. Framework to Create Dataset for Disaster Behavior Analysis using Google Earth Engine: A Case Study in Peninsular Malaysia for Historical Forest Fire Behavior Analysis. Preprints2024, 2024040027. https://doi.org/10.20944/preprints202404.0027.v1
APA Style
Chew, Y.J., Ooi, S.Y., Pang, Y.H., & Lim, Z.Y. (2024). Framework to Create Dataset for Disaster Behavior Analysis using Google Earth Engine: A Case Study in Peninsular Malaysia for Historical Forest Fire Behavior Analysis. Preprints. https://doi.org/10.20944/preprints202404.0027.v1
Chicago/Turabian Style
Chew, Y.J., Ying Han Pang and Zheng You Lim. 2024 "Framework to Create Dataset for Disaster Behavior Analysis using Google Earth Engine: A Case Study in Peninsular Malaysia for Historical Forest Fire Behavior Analysis" Preprints. https://doi.org/10.20944/preprints202404.0027.v1
Abstract
This research presents a comprehensive framework for efficiently generating forest fire datasets from Google Earth Engine data sources. The primary contribution of this work lies in providing a methodology to swiftly extract forest fire factors without the need for permissions or access to private datasets, rendering the dataset openly accessible and shared without barriers. Furthermore, given that the remote sensing data used is a global dataset, it can be applied in any region without restrictions. In this study, Peninsular Malaysia is chosen as a case study to demonstrate the framework's effectiveness. The generated dataset includes essential variables including the climate and environment, landcover, topography, and anthropogenic factors facilitating the analysis of fire occurrences. The methodology empowers data scientists, enabling them to leverage their analytical skills on the extracted dataset without requiring specialized remote sensing knowledge. Additionally, this study also showcases the adoption of large language models, specifically GPT-4 with the Noteable plugin, as a tool for conducting preliminary analyses on the generated dataset. Sample analyses reveal that several key features, including the KBDI, LST, PDSI, climate water deficit, and precipitation, significantly impact forest fire occurrences in Peninsular Malaysia. Despite the successful application of the GPT-4 with Noteable plugin, certain limitations and challenges are identified, highlighting the necessity for further validation of the tool's applicability and limitations. This study encourages future research to (1) adopt the proposed framework in other regions, (2) explore more detailed analyses encompassing all variables, and (3) leverage machine learning for advanced forecasting.
Keywords
Disaster behavior; forest fire behavior; forest fire dataset; data extraction framework; Google Earth Engine; remote sensing; Malaysia; ChatGPT; Noteable; Large Language Model
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
