Fire danger forecasting using machine learning-based models and meteorological observation: a case study in Northeastern China

Chen, Zhenyu; Zhang, Chen; Li, Wendi; Gao, Liang; Liu, Li; Fang, Lei; Zhang, Changsheng

doi:10.1007/s11042-023-15881-1

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…In northeastern Chinam researchers compared different methods and approaches for forecasting fire danger. Models included long short-term memory, ANN, ARIMA, gradient boosting, and support vector regressor for the estimation method [85]. Other approaches involve more complex models using satellite images with deep generative models [86] and a combination of naïve Bayes with autoregressive approaches for forecasting burned area [87].…”

Section: Discussionmentioning

confidence: 99%

Stochastic Approaches Systems to Predictive and Modeling Chilean Wildfires

de la Fuente-Mella,

Elórtegui-Gómez,

Umaña-Hermosilla

et al. 2023

Mathematics

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Whether due to natural causes or human carelessness, forest fires have the power to cause devastating damage, alter the habitat of animals and endemic species, generate insecurity in the population, and even affect human settlements with significant economic losses. These natural and social disasters are very difficult to control, and despite the multidisciplinary human effort, it has not been possible to create efficient mechanisms to mitigate the effects, and they have become the nightmare of every summer season. This study focuses on forecast models for fire measurements using time-series data from the Chilean Ministry of Agriculture. Specifically, this study proposes a comprehensive methodology of deterministic and stochastic time series to forecast the fire measures required by the programs of the National Forestry Corporation (CONAF). The models used in this research are among those commonly applied for time-series data. For the number of fires series, an Autoregressive Integrated Moving Average (ARIMA) model is selected, while for the affected surface series, a Seasonal Autoregressive Integrated Moving Average (SARIMA) model is selected, in both cases due to the lowest error metrics among the models fitted. The results provide evidence on the forecast for the number of national fires and affected national surface measured by a series of hectares (ha). For the deterministic method, the best model to predict the number of fires and affected surface is double exponential smoothing with damped parameter; for the stochastic approach, the best model for forecasting the number of fires is an ARIMA (2,1,2); and for affected surface, a SARIMA(1,1,0)(2,0,1)4, forecasting results are determined both with stochastic models due to showing a better performance in terms of error metrics.

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Section: Discussionmentioning

confidence: 99%

Stochastic Approaches Systems to Predictive and Modeling Chilean Wildfires

de la Fuente-Mella,

Elórtegui-Gómez,

Umaña-Hermosilla

et al. 2023

Mathematics

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“…Physical-mathematical models that take into account meteorological data input are also tested and used to monitor risks in other parts of the world, such as New Zealand, where the authors of [17] investigated the wind vector change using a model integrated with the Fire Weather Index (FWI). In China, also using the same FWI model, the authors of [18] combined two machine learning models with the FWI, namely the long short-term memory (LSTM) and random forest (RF) models.…”

Section: Introductionmentioning

confidence: 99%

The Consequences of Climate Change in the Brazilian Western Amazon: A New Proposal for a Fire Risk Model in Rio Branco, Acre

Melo,

Delgado,

Pereira

et al. 2024

Forests

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The objective of this study was to verify the link between climate change, changes in land use, and the increasing frequency of forest fires in the state of Acre. Recognizing the importance of an accurate assessment of fire risk, we also proposed a new fire risk index for the capital Rio Branco, using meteorological data. Validated reanalysis data from 1961 to 2020 extracted for Rio Branco and different land uses were used. Data on fire foci, deforestation, and agricultural crops were also obtained. The new model was based on the Fire Risk Atlantic Forest (FIAF) Index, developed for the Atlantic Forest biome, and was subjected to multiple regression analysis. To validate the new model, projections were calculated using different scenarios from the Intergovernmental Panel on Climate Change (IPCC). The new model, entitled Rio Branco Fire Risk (FIRERBR), revealed an increase in fire risk, especially associated with agriculture, in future scenarios (SSP2-4.5 and SSP5-8.5) from 2023 onward. Rainfall and relative air humidity also showed a reduction in projections, indicating a higher degree of fire danger for the region.

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