Prediction of forest fire using ensemble method

Rosadi, Dedi; Andriyani, Widyastuti

doi:10.1088/1742-6596/1918/4/042043

Cited by 13 publications

(9 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hakim et al [90] employed only AdaBoost and LogitBoost algorithms, while Arabameri et al [91] tested VIKOR and Cforest models to create land subsidence susceptibility maps. Rosadi and Andriyani [57] applied the AdaBoost algorithm to predict the forest-fre occurrence and compared it with classical classifcation methods such as SVM (support vector machine) and decision tree methods. Michael et al [58] investigated long-term vegetation condition efects on wildfre risk mapping by applying RF,…”

Section: Evaluation and Comparison Of The Wildfre Susceptibilitymentioning

confidence: 99%

“…Te analysis was made using fve forest fre risk factors, and it was seen that the model and the actual fre points overlapped. Rosadi and Andriyani [57] compared the AdaBoost algorithm with decision tree and SVM method to predict the occurrence of forest fres. Te study explained that fuzzy cmeans clustering and AdaBoost methods provided good results in predicting forest fres.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey

Abujayyab

Kassem

Khan

et al. 2022

Advances in Civil Engineering

View full text Add to dashboard Cite

Forest fires caused by different environmental and human factors are responsible for the extensive destruction of natural and economic resources. Modern machine learning techniques have become popular in developing very accurate and precise susceptibility maps of various natural disasters to help reduce the occurrence of such calamities. The present study has applied and tested multiple algorithms to map the areas susceptible to wildfire in the Mediterranean Region of Turkey. Besides, the performance of XGBoost, CatBoost, Gradient Boost, AdaBoost, and LightGBM methods for wildfire susceptibility mapping is also examined. The results have revealed the higher testing accuracy of CatBoost (95.47%) algorithm, followed by LightGBM (94.70%), XGBoost (88.8%), AdaBoost (86.0%), and GBM (84.48%) algorithms. Resultant wildfire susceptibility maps provide proper inventories for forest engineers, planners, and local governments for future policies regarding disaster management in Turkey.

show abstract

Section: Evaluation and Comparison Of The Wildfre Susceptibilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey

Abujayyab

Kassem

Khan

et al. 2022

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

“…In the 21st century, the global frequency and harm of forest fire activity have attracted wide attention from the international community [7][8][9]. Thus, the application of Moderate Resolution Imaging Spectroradiometer (MODIS) data [10] or high-precision image data [11] for forest fires has been gradually enhanced to improve forest fire monitoring, modeling, and prediction [12,13] as well as to improve forest fire prevention and control capacity.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Dynamics and Climate Influence of Forest Fires in Fujian Province, China

Zeng

Yang

Zhu

et al. 2022

Forests

View full text Add to dashboard Cite

Climate determines the spatiotemporal distribution pattern of forest fires by affecting vegetation and the extent of drought. Thus, analyzing the dynamic change of the forest fire season and its response to climate change will play an important role in targeted adjustments of forest fire management policies and practices. In this study, we studied the spatiotemporal variations in forest fire occurrence in Fujian Province, China using the Mann–Kendall trend test and correlation analysis to analyze Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2001 to 2016 and meteorological data. The results show that forest fire occurrence rose first and then declined over the years, but the proportion of forest fires during the fire prevention period decreased. The forest fires increased significantly in spring and summer, exceeding the forest fires occurring in the fire prevention period in 2010. The spatial distribution of forest fires decreased from northwest to southeast coastal areas, among which the number of forest fires in the northwest mountainous areas was large in autumn and winter. The fire risk weather index was strongly and positively correlated with forest fire occurrence across various sites in the province. The findings accentuate the need for properly adjusting the fire prevention period and resource allocation, strengthening the monitoring and early warning of high fire risk weather, and publicizing wildfire safety in spring and summer. As the forest fire occurrence frequency is high in the western and northwest mountainous areas, more observation towers and forest fire monitoring facilities should be installed.

show abstract

“…Combining all factors into a general fire danger parameter is usually conducted using GIS operations such as overlay and raster algebra (Akbulak et al 2018;Yankovich et al 2019), with the recent addition of machine learning, neural networks (Bui et al 2018) and big data (Piralilou et al 2022) technologies. Modern methods have advanced to using ensembles of different techniques with the selection of the most optimal and accurate results (Rosadi and Andriyani 2021). In Russia, methods for fire prevention and fire danger assessment are currently developed for three major areas:…”

Section: Introductionmentioning

confidence: 99%

Prediction of Wildfires Based on the Spatio-Temporal Variability of Fire Danger Factors

Gizatullin

Алексеенко

2022

GES

View full text Add to dashboard Cite

Most methods in the field of wildfire prevention are based on expert assessment of fire danger factors. However, their weights are usually assumed constant for the entire application area despite the geographical and seasonal changes of factors. This study aimed to develop a wildfire prevention method based on partial and general fire danger ratings taking into account their spatio-temporal variability. The study was conducted for Krasnoyarsk territory, Orenburg region and the Meschera lowland as the most forest, steppe and peat fire dangerous regions of Russia respectively. Surface temperature, moisture, vegetation structure, anthropogenic load, topography and their variation over subzones and in time were used as fire danger factors. They were evaluated by measuring parameters such as radiobrightness temperature, Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), distance to settlements and roads, elevation, slope and aspect. Materials from the Terra/Aqua, Sentinel-3, Landsat-8, Sentinel-2 satellites, ASTER Global Digital Elevation Model and Open Street Maps vector layers were used in the study. Correlation between these parameters and the actual fires in 2016-2018 was analyzed. Linear relationships were established, and correlation coefficients, equations of partial ratings and prevention 90%-threshold values were identified. On their basis, the parameter weights were computed to integrate them into the general fire danger rating. The developed method was validated using data over 2019. The results showed 67% confidence and 61% reliability of fire prevention along with the spatio-temporal patterns of fire danger factors. The method is recommended for preventing wildfires within the study areas and can be extend to similar regions.

show abstract

Prediction of forest fire using ensemble method

Cited by 13 publications

References 5 publications

Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey

Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey

Spatiotemporal Dynamics and Climate Influence of Forest Fires in Fujian Province, China

Prediction of Wildfires Based on the Spatio-Temporal Variability of Fire Danger Factors

Contact Info

Product

Resources

About