LEF-YOLO: a lightweight method for intelligent detection of four extreme wildfires based on the YOLO framework

International Journal of Wildland Fire, Год журнала: 2023, Номер 33(1)

Опубликована: Дек. 18, 2023

Background Extreme wildfires pose a serious threat to forest vegetation and human life because they spread more rapidly are intense than conventional wildfires. Detecting extreme is challenging due their visual similarities traditional fires, existing models primarily detect the presence or absence of fires without focusing on distinguishing providing warnings. Aims To test system for real time detection four Methods We proposed novel lightweight model, called LEF-YOLO, based YOLOv5 framework. make model lightweight, we introduce bottleneck structure MobileNetv3 use depthwise separable convolution instead convolution. improve model’s accuracy, apply multiscale feature fusion strategy Coordinate Attention Spatial Pyramid Pooling-Fast block enhance extraction. Key results The LEF-YOLO outperformed comparison wildfire dataset constructed, with our having excellent performance 2.7 GFLOPs, 61 FPS 87.9% mAP. Conclusions speed accuracy can be utilised real-time in fire scenes. Implications facilitate control decision-making foster intersection between science computer science.

Язык: Английский

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Forest fire susceptibility mapping with sensitivity and uncertainty analysis using machine learning and deep learning algorithms

Advances in Space Research, Год журнала: 2023, Номер 72(2), С. 426 - 443

Опубликована: Март 21, 2023

Язык: Английский

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Spatial analysis and machine learning prediction of forest fire susceptibility: a comprehensive approach for effective management and mitigation

The Science of The Total Environment, Год журнала: 2024, Номер 926, С. 171713 - 171713

Опубликована: Март 18, 2024

Язык: Английский

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Integrating geospatial, remote sensing, and machine learning for climate-induced forest fire susceptibility mapping in Similipal Tiger Reserve, India

Forest Ecology and Management, Год журнала: 2024, Номер 555, С. 121729 - 121729

Опубликована: Янв. 31, 2024

Accurately assessing forest fire susceptibility (FFS) in the Similipal Tiger Reserve (STR) is essential for biodiversity conservation, climate change mitigation, and community safety. Most existing studies have primarily focused on climatic topographical factors, while this research expands scope by employing a synergistic approach that integrates geographical information systems (GIS), remote sensing (RS), machine learning (ML) methodologies identifying fire-prone areas STR their vulnerability to change. To achieve this, study employed comprehensive dataset of forty-four influencing including topographic, climate-hydrologic, health, vegetation indices, radar features, anthropogenic interference, into ten ML models: neural net (nnet), AdaBag, Extreme Gradient Boosting (XGBTree), Machine (GBM), Random Forest (RF), its hybrid variants with differential evolution algorithm (RF-DEA), Gravitational Based Search (RF-GBS), Grey Wolf Optimization (RF-GWO), Particle Swarm (RF-PSO), genetic (RF-GA). The revealed high FFS both northern southern portions area, nnet RF-PSO models demonstrating percentages 12.44% 12.89%, respectively. Conversely, very low zones consistently displayed scores approximately 23.41% 18.57% models. robust mapping methodology was validated impressive AUROC (>0.88) kappa coefficient (>0.62) across all validation metrics. Future (ssp245 ssp585, 2022–2100) indicated along edges STR, central zone categorized from susceptibility. Boruta analysis identified actual evapotranspiration (AET) relative humidity as key factors ignition. SHAP evaluation reinforced influence these FFS, also highlighting significant role distance road, settlement, dNBR, slope, prediction accuracy. These results emphasize critical importance proposed provide invaluable insights firefighting teams, management, planning, qualification strategies address future sustainability.

Язык: Английский

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Exploring forest fire susceptibility and management strategies in Western Himalaya: Integrating ensemble machine learning and explainable AI for accurate prediction and comprehensive analysis

Environmental Technology & Innovation, Год журнала: 2024, Номер 35, С. 103655 - 103655

Опубликована: Май 5, 2024

Forest fires pose a significant threat to ecosystems and socio-economic activities, necessitating the development of accurate predictive models for effective management mitigation. In this study, we present novel machine learning approach combined with Explainable Artificial Intelligence (XAI) techniques predict forest fire susceptibility in Nainital district. Our innovative methodology integrates several robust — AdaBoost, Gradient Boosting Machine (GBM), XGBoost Random Deep Neural Network (DNN) as meta-model stacking framework. This not only utilises individual strengths these models, but also improves overall prediction performance reliability. By using XAI techniques, particular SHAP (SHapley Additive exPlanations) LIME (Local Interpretable Model-agnostic Explanations), improve interpretability provide insights into decision-making processes. results show effectiveness ensemble model categorising different zones: very low, moderate, high high. particular, identified extensive areas susceptibility, precision, recall F1 values underpinning their effectiveness. These achieved ROC AUC above 0.90, performing exceptionally well an 0.94. The are remarkably inclusion confidence intervals most important metrics all emphasises robustness reliability supports practical use management. Through summary plots, analyze global variable importance, revealing annual rainfall Evapotranspiration (ET) key factors influencing susceptibility. Local analysis consistently highlights importance rainfall, ET, distance from roads across models. study fills research gap by providing comprehensive interpretable modelling that our ability effectively manage risk is consistent environmental protection sustainable goals.

Язык: Английский

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Spatial Prediction of Wildfire Susceptibility Using Hybrid Machine Learning Models Based on Support Vector Regression in Sydney, Australia

Remote Sensing, Год журнала: 2023, Номер 15(3), С. 760 - 760

Опубликована: Янв. 28, 2023

Australia has suffered devastating wildfires recently, and is predisposed to them due several factors, including topography, meteorology, vegetation, ignition sources. This study utilized a geographic information system (GIS) technique analyze understand the factors that regulate spatial distribution of wildfire incidents machine learning predict susceptibility in Sydney. Wildfire inventory data were constructed by combining fire perimeter through field surveys occurrence gathered from visible infrared imaging radiometer suite (VIIRS)-Suomi thermal anomalies product between 2011 2020 for Sydney area. Sixteen wildfire-related acquired assess potential based on support vector regression (SVR) various metaheuristic approaches (GWO PSO) mapping In addition, 2019–2020 “Black Summer” acted as validation dataset predictive capability developed model. Furthermore, gain ratio (IGR) method showed driving such land use, forest type, slope degree have large impact area, frequency (FR) represented how influence occurrence. Model evaluation area under curve (AUC) root average square error (RMSE) used, outputs hybrid-based SVR-PSO (AUC = 0.882, RMSE 0.006) model performed better than standalone SVR 0.837, 0.097) SVR-GWO 0.873, 0.080) models. Thus, optimizing with metaheuristics improved accuracy modeling The proposed framework can be an alternative approach adapted any research related different disturbances.

Язык: Английский

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Advancing forest fire prediction: A multi-layer stacking ensemble model approach

Earth Science Informatics, Год журнала: 2025, Номер 18(3)

Опубликована: Фев. 19, 2025

Язык: Английский

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Forest fire susceptibility assessment under small sample scenario: A semi-supervised learning approach using transductive support vector machine

Journal of Environmental Management, Год журнала: 2024, Номер 359, С. 120966 - 120966

Опубликована: Апрель 26, 2024

Язык: Английский

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Prediction and driving factors of forest fire occurrence in Jilin Province, China

Journal of Forestry Research, Год журнала: 2023, Номер 35(1)

Опубликована: Дек. 16, 2023

Язык: Английский

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Deep learning modeling of human activity affected wildfire risk by incorporating structural features: A case study in eastern China

Ecological Indicators, Год журнала: 2024, Номер 160, С. 111946 - 111946

Опубликована: Март 1, 2024

Wildfire risk prediction is a critical component of disaster prevention and mitigation, often closely associated with local human activities in most regions. Recent studies demonstrate that employing joint modeling techniques using diverse datasets alongside Convolutional Neural Networks-Long Short-Term Memory Networks (ConvLSTM) produces favorable predictive results. However, previous research inadequately explored the different impact factors across categories spatial orientations, neglected fuels inside samples. This study focuses on six eastern provinces China, utilizing multi-source dataset comprising satellite-monitored wildfire products from 2012 to 2022, along various indicating terrestrial activities, simulated meteorological elements high-resolution vegetation imagery. By introducing channel attention mechanisms visual transformer mode, this optimizes ConvLSTM model. Results indicate noteworthy enhancement, elevating accuracy, Kappa coefficient, AUC ROC curves 91.15%, 80.87%, 97.01% 92.79%, 84.48%, 97.90%, respectively. Consequently, it reinforces accuracy by increase structural features within samples quantifying differences importance factors, which also validated application entire year 2023. Sensitivity analysis reveals current model still highly dependent factors. Notably, significantly surpasses influence terrain ecology elements, should be considered further models. Thus, has developed methodology integrating multiple sample features, could furnish high-precision daily kilometer-level products. method improve efficiency control improving narrowing high-risk areas.

Язык: Английский

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