Advancements in Forest Fire Prevention: A Comprehensive Survey

Sensors, Год журнала: 2023, Номер 23(14), С. 6635 - 6635

Опубликована: Июль 24, 2023

Nowadays, the challenges related to technological and environmental development are becoming increasingly complex. Among environmentally significant issues, wildfires pose a serious threat global ecosystem. The damages inflicted upon forests manifold, leading not only destruction of terrestrial ecosystems but also climate changes. Consequently, reducing their impact on both people nature requires adoption effective approaches for prevention, early warning, well-coordinated interventions. This document presents an analysis evolution various technologies used in detection, monitoring, prevention forest fires from past years present. It highlights strengths, limitations, future developments this field. Forest have emerged as critical concern due devastating effects potential repercussions climate. Understanding technology addressing issue is essential formulate more strategies mitigating preventing wildfires.

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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Assessment of Wildfire Susceptibility and Wildfire Threats to Ecological Environment and Urban Development Based on GIS and Multi-Source Data: A Case Study of Guilin, China

Remote Sensing, Год журнала: 2023, Номер 15(10), С. 2659 - 2659

Опубликована: Май 19, 2023

The frequent occurrence and spread of wildfires pose a serious threat to the ecological environment urban development. Therefore, assessing regional wildfire susceptibility is crucial for early prevention formulation disaster management decisions. However, current research on primarily focuses improving accuracy models, while lacking in-depth study causes mechanisms wildfires, as well impact losses they cause This situation not only increases uncertainty model predictions but also greatly reduces specificity practical significance models. We propose comprehensive evaluation framework analyze spatial distribution effects influencing factors, risks damage local In this study, we used information from period 2013–2022 data 17 factors in city Guilin basis, utilized eight machine learning algorithms, namely logistic regression (LR), artificial neural network (ANN), K-nearest neighbor (KNN), support vector (SVR), random forest (RF), gradient boosting decision tree (GBDT), light (LGBM), eXtreme (XGBoost), assess susceptibility. By evaluating multiple indicators, obtained optimal Shapley Additive Explanations (SHAP) method explain decision-making mechanism model. addition, collected calculated corresponding with Remote Sensing Ecological Index (RSEI) representing vulnerability Night-Time Lights (NTLI) development vulnerability. coupling results two represent ecology city. Finally, by integrating information, assessed risk disasters reveal overall characteristics Guilin. show that AUC values models range 0.809 0.927, ranging 0.735 0.863 RMSE 0.327 0.423. Taking into account all performance XGBoost provides best results, AUC, accuracy, 0.863, 0.327, respectively. indicates has predictive performance. high-susceptibility areas are located central, northeast, south, southwest regions area. temperature, soil type, land use, distance roads, slope have most significant Based assessments, potential can be identified comprehensively reasonably. article improve prediction provide important reference response wildfires.

Язык: Английский

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Comparison of diverse machine learning algorithms for forest fire susceptibility mapping in Antalya, Türkiye

Advances in Space Research, Год журнала: 2024, Номер 74(2), С. 647 - 667

Опубликована: Апрель 16, 2024

Язык: Английский

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Correlating Fire Incidents with Meteorological Variables in Dry Temperate Forest

Forests, Год журнала: 2025, Номер 16(1), С. 122 - 122

Опубликована: Янв. 10, 2025

Forest fires pose a significant ecological threat, particularly in the Diamer District, Gilgit-Baltistan, Pakistan, where climatic factors combined with human activities have resulted severe fire incidents. The present study sought to investigate correlation between incidence of forest and critical meteorological elements, including temperature, humidity, precipitation, wind speed, over period 25 years, from 1998 2023. We analyzed 169 recorded events, collectively burning approximately 109,400 hectares land. Employing sophisticated machine learning algorithms, Random (RF), Gradient Boosting Machine (GBM) revealed that temperature relative humidity during season, which spans May through July, are key influencing activity. Conversely, speed was found negligible impact. RF model demonstrated superior predictive accuracy compared GBM model, achieving an RMSE 5803.69 accounting for 49.47% variance burned area. This presents novel methodology risk modeling under climate change scenarios region, offering insights into management strategies. Our results underscore necessity real-time early warning systems adaptive strategies mitigate frequency intensity escalating driven by change.

Язык: Английский

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A Forest Fire Susceptibility Modeling Approach Based on Integration Machine Learning Algorithm

Forests, Год журнала: 2023, Номер 14(7), С. 1506 - 1506

Опубликована: Июль 24, 2023

The subjective and empirical setting of hyperparameters in the random forest (RF) model may lead to decreased performance. To address this, our study applies particle swarm optimization (PSO) algorithm select optimal parameters RF model, with goal enhancing We employ optimized ensemble (PSO-RF) create a fire risk map for Jiushan National Forest Park Anhui Province, China, thereby filling research gap this region’s studies. Based on collinearity tests previous results, we selected eight driving factors, including topography, climate, human activities, vegetation modeling. Additionally, compare logistic regression (LR), support vector machine (SVM), models. Lastly, evaluate feature importance generate map. Model evaluation results demonstrate that PSO-RF performs best (AUC = 0.908), followed by (0.877), SVM (0.876), LR (0.846). In created 70.73% area belongs normal management zone, while 15.23% is classified as alert zone. analysis reveals NDVI key factor area. Through utilizing PSO optimize have addressed problems hyperparameter setting, model’s accuracy generalization ability.

Язык: Английский

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

Natural Hazards, Год журнала: 2024, Номер 120(7), С. 6569 - 6592

Опубликована: Фев. 29, 2024

Язык: Английский

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Assessing the extent of land degradation in the eThekwini municipality using land cover change and soil organic carbon

International Journal of Remote Sensing, Год журнала: 2024, Номер 45(4), С. 1339 - 1367

Опубликована: Фев. 2, 2024

More than 75% of the global land has already suffered degradation, leading to recognition degradation as one foremost challenges society faces. This stems from its profound adverse impacts on natural ecosystem functioning, biodiversity, soil productivity, and food availability. Consequently, understanding spatial distribution across all scales becomes imperative. study employed cover change organic carbon (SOC) stock assessments analyse within eThekwini Municipality beyond baseline period (2000–2015). Utilizing remote sensing machine learning techniques, this research examined over spanning 2000 2022. Landsat 7 (Enhanced Thematic Mapper Plus – ETM+), 8 (Operational Land Imager 1 - OLI1), 9 2 OLI2) images were extract variables for both SOC prediction through XGBoost, LightGBM, Random Forest (RF), Support Vector Machine (SVM) models. Among these models, LightGBM demonstrates superior performance, achieving an overall accuracy 80.646 in predictions 77.869 predictions. Analysis unveiled a shift forests shrubland landscapes cropland built-up areas. results municipality encountering losses between 2015 The model predicted that most occur at 20–50 cm depth (9.27%), comparison 7.21% loss 0–20 depth. These findings underscore pivotal role aiding policymakers assess implement pertinent measures enhance landscape.

Язык: Английский

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Modeling of Forest Fire Risk Areas of Amazonas Department, Peru: Comparative Evaluation of Three Machine Learning Methods

Forests, Год журнала: 2025, Номер 16(2), С. 273 - 273

Опубликована: Фев. 5, 2025

Forest fires are the result of poor land management and climate change. Depending on type affected eco-system, they can cause significant biodiversity losses. This study was conducted in Amazonas department Peru. Binary data obtained from MODIS satellite occurrence between 2010 2022 were used to build risk models. To avoid multicollinearity, 12 variables that trigger selected (Pearson ≤ 0.90) grouped into four factors: (i) topographic, (ii) social, (iii) climatic, (iv) biological. The program Rstudio three types machine learning applied: MaxENT, Support Vector Machine (SVM), Random (RF). results show RF model has highest accuracy (AUC = 0.91), followed by MaxENT 0.87) SVM 0.84). In fire map elaborated with model, 38.8% region possesses a very low occurrence, 21.8% represents high-risk level zones. research will allow decision-makers improve forest Amazon prioritize prospective strategies such as installation water reservoirs areas zone. addition, it support awareness-raising actions among inhabitants at greatest so be prepared mitigate control generate solutions event occurring under different scenarios.

Язык: Английский

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Enhancing landslide management with hyper-tuned machine learning and deep learning models: Predicting susceptibility and analyzing sensitivity and uncertainty

Frontiers in Ecology and Evolution, Год журнала: 2023, Номер 11

Опубликована: Март 8, 2023

Introduction Natural hazards such as landslides and floods have caused significant damage to properties, natural resources, human lives. The increased anthropogenic activities in weak geological areas led a rise the frequency of landslides, making landslide management an urgent task minimize negative impact. This study aimed use hyper-tuned machine learning deep algorithms predict susceptibility model (LSM) provide sensitivity uncertainty analysis Aqabat Al-Sulbat Asir region Saudi Arabia. Methods Random forest (RF) was used model, while neural network (DNN) model. models were using grid search technique, best hypertuned for predicting LSM. generated validated receiver operating characteristics (ROC), F1 F2 scores, gini value, precision recall curve. DNN based conducted analyze influence parameters landslide. Results showed that RF predicted 35.1–41.32 15.14–16.2 km 2 high very zones, respectively. area under curve (AUC) ROC LSM by achieved 0.96 AUC, 0.93 AUC. results rainfall had highest landslide, followed Topographic Wetness Index (TWI), curvature, slope, soil texture, lineament density. Discussion Road density geology map prediction. may be helpful authorities stakeholders proposing plans considering potential sensitive parameters.

Язык: Английский

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