Evaluation of Three Algorithms and Forest Fire Risk Prediction in Zhejiang Province of China DOI Open Access

Richard Bian,

Keji Chen,

Guoqiang Li

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(12), P. 2146 - 2146

Published: Dec. 5, 2024

Forest fires represent a paramount natural disaster of global concern. Zhejiang Province has the highest forest coverage rate in China, and are one main disasters impacting management region. In this study, we comprehensively analyzed spatiotemporal distribution based on MODIS data from 2013 to 2023. The results showed that annual incidence shown an overall downward trend 2023, with occurring more frequently winter spring. By utilizing eight contributing factors fire occurrence as variables, three models were constructed: Logistic Regression (LR), Random (RF), eXtreme Gradient Boosting (XGBoost). RF XGBoost demonstrated high predictive ability, achieving accuracy rates 0.85 0.92, f1-score 0.84 AUC values 0.892 0.919, respectively. Further analysis using revealed elevation precipitation had most significant effects fires. Additionally, predictions risk generated by indicated is southern part Province, particularly Wenzhou Lishui areas, well southwest Hangzhou area north Quzhou area. future, can be predicted site models, providing scientific reference for aiding prevention mitigation impacts

Language: Английский

Spatial analysis and machine learning prediction of forest fire susceptibility: a comprehensive approach for effective management and mitigation DOI
Manoranjan Mishra, Rajkumar Guria, Biswaranjan Baraj

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 926, P. 171713 - 171713

Published: March 18, 2024

Language: Английский

Citations

24
Forest fire probability zonation using dNBR and machine learning models: a case study at the Similipal Biosphere Reserve (SBR), Odisha, India DOI
Rajkumar Guria, Manoranjan Mishra,

Samiksha Mohanta

et al.

Environmental Science and Pollution Research, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 30, 2025

Language: Английский

Citations

1
From Data to Insights: Modeling Urban Land Surface Temperature Using Geospatial Analysis and Interpretable Machine Learning DOI Creative Commons
Nhat‐Duc Hoang, Van-Duc Tran, Thanh‐Canh Huynh

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(4), P. 1169 - 1169

Published: Feb. 14, 2025

This study introduces an innovative machine learning method to model the spatial variation of land surface temperature (LST) with a focus on urban center Da Nang, Vietnam. Light Gradient Boosting Machine (LightGBM), support vector machine, random forest, and Deep Neural Network are employed establish functional relationships between LST its influencing factors. The approaches trained validated using remote sensing data from 2014, 2019, 2024. Various explanatory variables representing topographical characteristics, as well landscapes, used. Experimental results show that LightGBM outperforms other benchmark methods. In addition, Shapley Additive Explanations utilized clarify impact factors affecting LST. analysis outcomes indicate while importance these changes over time, density greenspace consistently emerge most influential attained R2 values 0.85, 0.92, 0.91 for years 2024, respectively. findings this work can be helpful deeper understanding heat stress dynamics facilitate planning.

Language: Английский

Citations

1
Post-Fire Burned Area Detection Using Machine Learning and Burn Severity Classification with Spectral Indices in İzmir: A SHAP-Driven XAI Approach DOI Creative Commons
Halil İbrahim Gündüz, Ahmet Tarık TORUN, Cemil Gezgin

et al.

Fire, Journal Year: 2025, Volume and Issue: 8(4), P. 121 - 121

Published: March 21, 2025

This study was conducted to precisely map burned areas in fire-prone forest regions of İzmir and analyze the spatial distribution wildfires. Using Sentinel-2 satellite imagery, burn severity first classified using dNBR dNDVI indices. Subsequently, machine learning (ML) algorithms—RF, XGBoost, LightGBM, AdaBoost—were employed classify unburned areas. To enhance model performance, hyperparameter optimization applied, results were evaluated multiple accuracy metrics. found that RF achieved highest with an overall 98.0% a Kappa coefficient 0.960. In comparison, classification based solely on spectral indices resulted accuracies 86.6% (dNBR) 81.7% (dNDVI). A key contribution this is integration Explainable Artificial Intelligence (XAI) through SHapley Additive exPlanations (SHAP) analysis, which used interpret influence environmental variables area classification. SHAP analysis made decision processes transparent identified dNBR, dNDVI, SWIR/NIR bands as most influential variables. Furthermore, analyses confirmed variations reflectance across fire-affected are critical for accurate delineation, particularly heterogeneous landscapes. provides scientific framework post-fire ecosystem restoration, fire management, disaster strategies, offering decision-makers data-driven effective intervention strategies.

Language: Английский

Citations

1
SHAP-Driven Explainable Artificial Intelligence Framework for Wildfire Susceptibility Mapping Using MODIS Active Fire Pixels: An In-Depth Interpretation of Contributing Factors in Izmir, Türkiye DOI Creative Commons
Muzaffer Can İban, Oktay Aksu

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(15), P. 2842 - 2842

Published: Aug. 2, 2024

Wildfire susceptibility maps play a crucial role in preemptively identifying regions at risk of future fires and informing decisions related to wildfire management, thereby aiding mitigating the risks potential damage posed by wildfires. This study employs eXplainable Artificial Intelligence (XAI) techniques, particularly SHapley Additive exPlanations (SHAP), map Izmir Province, Türkiye. Incorporating fifteen conditioning factors spanning topography, climate, anthropogenic influences, vegetation characteristics, machine learning (ML) models (Random Forest, XGBoost, LightGBM) were used predict wildfire-prone areas using freely available active fire pixel data (MODIS Active Fire Collection 6 MCD14ML product). The evaluation trained ML showed that Random Forest (RF) model outperformed XGBoost LightGBM, achieving highest test accuracy (95.6%). All classifiers demonstrated strong predictive performance, but RF excelled sensitivity, specificity, precision, F-1 score, making it preferred for generating conducting SHAP analysis. Unlike prevailing approaches focusing solely on global feature importance, this fills critical gap employing summary dependence plots comprehensively assess each factor’s contribution, enhancing explainability reliability results. analysis reveals clear associations between such as wind speed, temperature, NDVI, slope, distance villages with increased susceptibility, while rainfall streams exhibit nuanced effects. spatial distribution classes highlights areas, flat coastal near settlements agricultural lands, emphasizing need enhanced awareness preventive measures. These insights inform targeted management strategies, highlighting importance tailored interventions like firebreaks management. However, challenges remain, including ensuring selected factors’ adequacy across diverse regions, addressing biases from resampling spatially varied data, refining broader applicability.

Language: Английский

Citations

7
Predicting forest fire probability in Similipal Biosphere Reserve (India) using Sentinel-2 MSI data and machine learning DOI
Rajkumar Guria, Manoranjan Mishra, Richarde Marques da Silva

et al.

Remote Sensing Applications Society and Environment, Journal Year: 2024, Volume and Issue: 36, P. 101311 - 101311

Published: July 26, 2024

Language: Английский

Citations

6
Modeling of Forest Fire Risk Areas of Amazonas Department, Peru: Comparative Evaluation of Three Machine Learning Methods DOI Open Access
Alex J. Vergara, Sivmny V. Valqui-Reina, Dennis Cieza-Tarrillo

et al.

Forests, Journal Year: 2025, Volume and Issue: 16(2), P. 273 - 273

Published: Feb. 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.

Language: Английский

Citations

0
Global temporal and spatial changes of vegetation in desert steppe Ecosystems: Impacts of climate driving factors DOI Creative Commons
Xiaonan Chen, Bochao Cui, Dongwei GUI

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 172, P. 113333 - 113333

Published: March 1, 2025

Language: Английский

Citations

0
Aquifer vulnerability assessment in data-scarce areas: a spatially explicit assessment DOI Creative Commons
Changhyun Jun, Dongkyun Kim, Sayed M. Bateni

et al.

Geomatics Natural Hazards and Risk, Journal Year: 2025, Volume and Issue: 16(1)

Published: April 24, 2025

Citations

0
Forest Smoke-Fire Net (FSF Net): A Wildfire Smoke Detection Model That Combines MODIS Remote Sensing Images with Regional Dynamic Brightness Temperature Thresholds DOI Open Access
Yunhong Ding, Mingyang Wang, Yujia Fu

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(5), P. 839 - 839

Published: May 10, 2024

Satellite remote sensing plays a significant role in the detection of smoke from forest fires. However, existing methods for detecting fires based on images rely solely information provided by images, overlooking positional and brightness temperature fire spots This oversight significantly increases probability misjudging plumes. paper proposes model, Forest Smoke-Fire Net (FSF Net), which integrates wildfire with dynamic region. The MODIS_Smoke_FPT dataset was constructed using Moderate Resolution Imaging Spectroradiometer (MODIS), meteorological at site fire, elevation data to determine location threshold wildfires. Deep learning machine models were trained separately image spot area dataset. performance deep model evaluated metric MAP, while regression assessed Root Mean Square Error (RMSE) Absolute (MAE). selected organically integrated. results show that Mask_RCNN_ResNet50_FPN XGR performed best among models, respectively. Combining two achieved good (Precisionsmoke=89.12%). Compared use recognition, proposed this demonstrates stronger applicability improving precision detection, thereby providing beneficial support timely applications sensing.

Language: Английский

Citations

3