An integrated approach of artificial intelligence and geoinformation techniques applied to forest fire risk modeling in Gachsaran, Iran

Feizizadeh, Bakhtiar; Omarzadeh, Davoud; Mohammadnejad, Vahid; Khallaghi, Hoda; Sharifi, Ayyoob; Karkarg, Bahaoldein Golmohmadzadeh

doi:10.1080/09640568.2022.2027747

Cited by 9 publications

(4 citation statements)

References 54 publications

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“…This work sought to include the environmental variables, which were more plausible of contributing to the prediction of a territory's fire susceptibility, represented in Figure 3. Susceptibility models created with the official Portuguese specifications [21] uses land use and terrain slope, as do most works on fire modeling [11,[27][28][29][30]. Terrain aspect is also used often [11,26,27].…”

Section: Datasetmentioning

confidence: 99%

A GIS Plugin for Susceptibility Modeling: Case Study of Wildfires in Vila Nova de Foz Côa

Padrão¹,

Duarte

Teodoro

2022

Land

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Risk mapping is a crucial part of spatial planning, as it optimizes the allocation of resources in its management. It is, therefore, of great interest to build tools that enhance its production. This work focuses on the implementation of a susceptibility model for different types of spatially distributed risk in a geographic information systems (GIS) Python plugin. As an example, the susceptibility model was applied to study the occurrence of wildfires in the municipality of Vila Nova de Foz Côa, Portugal. The plugin was developed to simplify the production and evaluation of susceptibility maps regarding the available geographical information. Regarding our case study, the data used corresponds to three training areas, ten years of burned areas and nine environmental variables. The model is applied to different combinations of these factors. The validation, performed with receiver operating characteristic (ROC) curves, resulted in an area under the curve (AUC) of 74% for a fire susceptibility model, calculated with the same environmental factors used in official Portuguese cartography (land use and slope) and with the optimal training area, years of information on burned area and level of land use classification. After experimenting with four variable combinations, a maximum AUC of 77% was achieved. This study confirms the suitability of the variables chosen for the production of official fire susceptibility models but leaves out the comparison between the official methodology and the methodology proposed in this work.

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Section: Datasetmentioning

confidence: 99%

A GIS Plugin for Susceptibility Modeling: Case Study of Wildfires in Vila Nova de Foz Côa

Padrão¹,

Duarte

Teodoro

2022

Land

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“…Researchers worldwide have made significant advancements in forest fire risk prediction technologies. For example, remote sensing and geographic information system (GIS) learning models are used to assess the probability of forest fire risk and monitor the susceptibility to forest fires [12][13][14]. Knowledge-based methods including fuzzy logic [15], analytic hierarchy process (AHP) [16], and network analysis methods are also being used for this purpose.…”

Section: Introductionmentioning

confidence: 99%

A Forest Fire Recognition Method Based on Modified Deep CNN Model

Zheng,

Zou,

Gao

et al. 2024

Forests

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Controlling and extinguishing spreading forest fires is a challenging task that often leads to irreversible losses. Moreover, large-scale forest fires generate smoke and dust, causing environmental pollution and posing potential threats to human life. In this study, we introduce a modified deep convolutional neural network model (MDCNN) designed for the recognition and localization of fire in video imagery, employing a deep learning-based recognition approach. We apply transfer learning to refine the model and adapt it for the specific task of fire image recognition. To combat the issue of imprecise detection of flame characteristics, which are prone to misidentification, we integrate a deep CNN with an original feature fusion algorithm. We compile a diverse set of fire and non-fire scenarios to construct a training dataset of flame images, which is then employed to calibrate the model for enhanced flame detection accuracy. The proposed MDCNN model demonstrates a low false alarm rate of 0.563%, a false positive rate of 12.7%, a false negative rate of 5.3%, and a recall rate of 95.4%, and achieves an overall accuracy of 95.8%. The experimental results demonstrate that this method significantly improves the accuracy of flame recognition. The achieved recognition results indicate the model’s strong generalization ability.

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“…In parallel, AI/ML and data analytics are becoming increasingly valuable in assessing and managing fire risk, as they can analyze large volumes of data, extract patterns, and make predictions. These technologies have been widely adopted by the scientific community and have resulted in accurate and analysis-ready results [11,19,20].…”

Section: Introductionmentioning

confidence: 99%

Geoinformatics and Machine Learning for Comprehensive Fire Risk Assessment and Management in Peri-Urban Environments: A Building-Block-Level Approach

Yfantidou,

Zoka,

Stathopoulos

et al. 2023

Applied Sciences

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Forest fires can result in loss of life, damage to infrastructure, and adverse environmental impacts. This study showcases an integrated approach for conducting high-detail fire risk assessment and supporting strategic planning and management of fire events in peri-urban areas that are susceptible to forest fires. The presented methodology encompasses fire hazard modeling, vulnerability and exposure assessment, and in situ observations. Numerous fire hazard scenarios were tested, simulating the spatiotemporal spread of fire events under different wind characteristics. The vulnerability of the studied areas was assessed by combining population data (density and age) and building characteristics, while the exposure parameter employed land value (EUR/m2) as an indicator for qualitatively estimating potential economic effects in the study area. Field campaigns facilitated the identification and recording of critical areas and points, including high-risk buildings and population gathering areas, which subsequently informed the mitigation and fire management planning suggestions. Moreover, field recordings acted as an iterative process for validating and updating the fire risk maps. This research work utilizes state-of-the-art techniques to achieve an analysis of fire risk at a building-block level. Overall, the study presents an applied and end-to-end methodology for effectively addressing forest fire risk in peri-urban areas.

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An integrated approach of artificial intelligence and geoinformation techniques applied to forest fire risk modeling in Gachsaran, Iran

Cited by 9 publications

References 54 publications

A GIS Plugin for Susceptibility Modeling: Case Study of Wildfires in Vila Nova de Foz Côa

A GIS Plugin for Susceptibility Modeling: Case Study of Wildfires in Vila Nova de Foz Côa

A Forest Fire Recognition Method Based on Modified Deep CNN Model

Geoinformatics and Machine Learning for Comprehensive Fire Risk Assessment and Management in Peri-Urban Environments: A Building-Block-Level Approach

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