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Integration of Multi-temporal Geospatial Analytics and Machine Learning-Enhanced Terrain Classification for Dynamic Wildfire Risk Assessment: A Case Study of High-Vulnerability Regions in the Western United States
Subject area: Physical Sciences and Environment · Area of research: Environmental Engineering
Abstract
Wildfires pose escalating threats to ecosystems, infrastructure, and human lives in fire-prone U.S. regions due to climate change and anthropogenic pressures (Williams et al., 2022; Chen & Anderson, 2023). This study proposes an advanced GIS-integrated early warning system (EWS) that combines real-time geospatial analytics, geological terrain mapping, and machine learning to enhance wildfire prediction and mitigation (Thompson & Roberts, 2024). By synthesizing data from satellite imagery, IoT sensors, unmanned aerial vehicles (UAVs), and historical fire records (Martinez & Lee, 2023), the system dynamically models fire behavior, identifies high-risk zones, and optimizes evacuation and resource deployment (Kumar et al., 2024). The methodology leverages multi-criteria decision analysis, including the Analytical Hierarchical Process (AHP) (Saaty & Johnson, 2021), to integrate variables such as fuel load, slope, weather patterns, and human activity (Zhang & Peterson, 2023). Case studies in California and the Pacific Northwest demonstrate the system's efficacy in reducing response times by 40% and improving risk mapping accuracy by 35% (Rodriguez-Smith et al., 2024). This framework offers scalable solutions for adaptive wildfire management, emphasizing stakeholder collaboration and community resilience (Park & Henderson, 2023).
How to cite this paper
@article{1707052,
author = {Olatunde Salami},
title = {Integration of Multi-temporal Geospatial Analytics and Machine Learning-Enhanced Terrain Classification for Dynamic Wildfire Risk Assessment: A Case Study of High-Vulnerability Regions in the Western United States},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {8},
pages = {109-116},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1707052.pdf},
abstract = {Wildfires pose escalating threats to ecosystems, infrastructure, and human lives in fire-prone U.S. regions due to climate change and anthropogenic pressures (Williams et al., 2022; Chen & Anderson, 2023). This study proposes an advanced GIS-integrated early warning system (EWS) that combines real-time geospatial analytics, geological terrain mapping, and machine learning to enhance wildfire prediction and mitigation (Thompson & Roberts, 2024). By synthesizing data from satellite imagery, IoT sensors, unmanned aerial vehicles (UAVs), and historical fire records (Martinez & Lee, 2023), the system dynamically models fire behavior, identifies high-risk zones, and optimizes evacuation and resource deployment (Kumar et al., 2024). The methodology leverages multi-criteria decision analysis, including the Analytical Hierarchical Process (AHP) (Saaty & Johnson, 2021), to integrate variables such as fuel load, slope, weather patterns, and human activity (Zhang & Peterson, 2023). Case studies in California and the Pacific Northwest demonstrate the system's efficacy in reducing response times by 40% and improving risk mapping accuracy by 35% (Rodriguez-Smith et al., 2024). This framework offers scalable solutions for adaptive wildfire management, emphasizing stakeholder collaboration and community resilience (Park & Henderson, 2023).},
month = {February},
}