On the Application of Modelling Forest Fire in the Environment: A Bayesian Model Averaging Approach

Oladoja, Oladapo Muyiwa; Folorunso, Adesola G.; Adegoke, Taiwo Mobolaji; Bashiru, Sule Omeiza; Arum, Kingsley Chinedu; Mustapha, Adam

doi:10.1109/seb-sdg57117.2023.10124475

2023 International Conference on Science, Engineering and Business for Sustainable Development Goals (SEB-SDG) 2023

DOI: 10.1109/seb-sdg57117.2023.10124475

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On the Application of Modelling Forest Fire in the Environment: A Bayesian Model Averaging Approach

Oladapo Muyiwa Oladoja

Adesola G. Folorunso²,

Taiwo Mobolaji Adegoke

et al.

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2024

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Cited by 2 publications

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Forest fire mapping: a comparison between GIS-based random forest and Bayesian models

Noroozi,

Ghanbarian,

Safaeian

et al. 2024

Nat Hazards

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In recent decades, res in natural ecosystems, particularly forests and rangelands, have emerged as a signi cant threat. To address this challenge, our study aims to identify and prioritize forest re-prone areas while highlighting key environmental and anthropogenic factors contributing to forest res in Iran's Firouzabad region, Fars province. We compiled a forest re incident map using data from the Data Center of the Natural Resources Department in Fars province, cross-referenced with eld surveys. We examined 80 forest re sites, randomly divided into a "training dataset" (70%) and a "validation dataset" (30%). We created "Forest Fire Susceptibility" (FFS) maps using GIS-based Bayesian and Random Forest (RF) methodologies, incorporating twelve unique environmental and human-induced variables. The performance of these methodologies was evaluated using the "Area Under the Curve-AUC." RF outperformed the Bayesian model with AUC scores of 0.876 and 0.807, respectively. The RF model identi ed 37.86% of the area as having a high re risk, compared to the Bayesian model's estimate of 48.46%. Key factors in uencing re occurrences included elevation, mean annual precipitation, distance to roads, and mean annual temperature. Conversely, variables such as slope direction, topographic wetness index, and slope percent had a lesser impact. Given the presence of at-risk ora and fauna species in the area, our ndings provide essential tools for pinpointing high re susceptibility zones, aiding regional authorities in implementing preventive measures to mitigate re hazards in forest ecosystems. In conclusion, our methodologies allow for the rapid creation of contemporary re susceptibility maps based on fresh data.

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Forest fire mapping: a comparison between GIS-based random forest and Bayesian models

Noroozi,

Ghanbarian,

Safaeian

et al. 2024

Nat Hazards

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A Bayesian model for predicting monthly fire frequency in Kenya

Orero,

Omondi,

Omolo

2024

PLoS ONE

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This study presents a comprehensive analysis of historical fire and climatic data to estimate the monthly frequency of vegetation fires in Kenya. This work introduces a statistical model that captures the behavior of fire count data, incorporating temporal explanatory factors and emphasizing the predictive significance of maximum temperature and rainfall. By employing Bayesian approaches, the paper integrates literature information, simulation studies, and real-world data to enhance model performance and generate more precise prediction intervals that encompass actual fire counts. To forecast monthly fire occurrences aggregated from the Moderate Resolution Imaging Spectroradiometer (MODIS) data in Kenya (2000-2018), the study utilizes maximum temperature and rainfall values derived from global GeoTiff (.tif) files sourced from the WorldClim database. The evaluation of the widely used Negative Binomial (NB) model and the proposed Bayesian Negative Binomial (BNB) model reveals the superiority of the latter in accounting for seasonal patterns and long-term trends. The simulation results demonstrate that the BNB model outperforms the NB model in terms of Root Mean Square Error (RMSE), and Mean Absolute Scaled Error (MASE) on both training and testing datasets. Furthermore, when applied to real data, the Bayesian Negative Binomial model exhibits better performance on the test dataset, showcasing lower RMSE (163.22 vs. 166.67), lower MASE (1.12 vs. 1.15), and reduced bias (-2.52% vs. -2.62%) compared to the NB model. The Bayesian model also offers prediction intervals that closely align with actual predictions, indicating its flexibility in forecasting the frequency of monthly fires. These findings underscore the importance of leveraging past data to forecast the future behavior of the fire regime, thus providing valuable insights for fire control strategies in Kenya. By integrating climatic factors and employing Bayesian modeling techniques, the study contributes to the understanding and prediction of vegetation fires, ultimately supporting proactive measures in mitigating their impact.

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On the Application of Modelling Forest Fire in the Environment: A Bayesian Model Averaging Approach

Cited by 2 publications

References 7 publications

Forest fire mapping: a comparison between GIS-based random forest and Bayesian models

Forest fire mapping: a comparison between GIS-based random forest and Bayesian models

A Bayesian model for predicting monthly fire frequency in Kenya

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