Design of a Forest Fire Early Alert System through a Deep 3D-CNN Structure and a WRF-CNN Bias Correction

Casallas, Alejandro; Jiménez-Saenz, Camila; Torres, Victor J.; Quirama-Aguilar, Miguel; Lizcano, Augusto; López-Barrera, Ellie Anne; Ferro, Camilo; Celis, Nathalia; Arenas, Ricardo

doi:10.3390/s22228790

Cited by 10 publications

(11 citation statements)

References 60 publications

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“…Another important element to be mentioned is that these mountain ranges not only affect the behavior of convection but also difficult the implementation of well-suited ground-base stations (Casallas et al 2022c ). Colombia is formed by 1122 municipalities and has an extension of 1143 million km 2 .…”

Section: Methodsmentioning

confidence: 99%

Surface, satellite ozone variations in Northern South America during low anthropogenic emission conditions: a machine learning approach

Casallas

Castillo-Camacho

Sanchez

et al. 2023

Air Qual Atmos Health

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2020 presented the ideal conditions for studying the air quality response to several emission reductions due to the COVID-19 lockdowns. Numerous studies found that the tropospheric ozone increased even in lockdown conditions, but its reasons are not entirely understood. This research aims to better understand the ozone variations in Northern South America. Satellite and reanalysis data were used to analyze regional ozone variations. An analysis of two of the most polluted Colombian cities was performed by quantifying the changes of ozone and its precursors and by doing a machine learning decomposition to disentangle the contributions that precursors and meteorology made to form O 3 . The results indicated that regional ozone increased in most areas, especially where wildfires are present. Meteorology is associated with favorable conditions to promote wildfires in Colombia and Venezuela. Regarding the local analysis, the machine learning ensemble shows that the decreased titration process associated with the NO plummeting owing to mobility reduction is the main contributor to the O 3 increase (≈50%). These tools lead to conclude that (i) the increase in O 3 produced by the reduction of the titration process that would be associated with an improvement in mobile sources technology has to be considered in the new air quality policies, (ii) a boost in international cooperation is essential to control wildfires since an event that occurs in one country can affect others and (iii) a machine learning decomposition approach coupled with sensitivity experiments can help us explain and understand the physicochemical mechanism that drives ozone formation. Supplementary Information The online version contains supplementary material available at 10.1007/s11869-023-01303-6.

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Section: Methodsmentioning

confidence: 99%

Surface, satellite ozone variations in Northern South America during low anthropogenic emission conditions: a machine learning approach

Casallas

Castillo-Camacho

Sanchez

et al. 2023

Air Qual Atmos Health

Self Cite

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“…The higher the FWI is, the more favorable the meteorological conditions are to trigger a wildfire. [27] and implemented in recent studies [56,57]. Apart from the production of the existing codes of the FWI system, three more indices were calculated altogether.…”

Section: Calculation Of the Fwi And Other Indicesmentioning

confidence: 99%

Investigation of Fire Weather Danger under a Changing Climate at High Resolution in Greece

et al. 2023

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Future fire weather conditions under climate change were investigated based on the Fire Weather Index (FWI), Initial Spread Index (ISI) and threshold-specific indicators in Greece. The indices were calculated from climate datasets derived from high-resolution validated simulations of 5 km. The dynamical downscaled simulations with the WRF model were driven by EC-Earth output for historical (1980–2004) and future periods, under two Representative Concentration Pathways (RCPs), RCP4.5 and 8.5. The analysis showed that the FWI is expected to increase substantially, particularly in the southern parts with extreme values found above 100. In addition, the number of days with an FWI above the 90th percentile is projected to increase considerably (above 30 days), under both scenarios. Over the eastern and northern mainland, the increase is estimated with more than 70 days under RCP4.5, in the near future (2025–2049). Moreover, central and north-eastern parts of the country will be affected with 30 or more extreme consecutive days of prolonged fire weather, under RCP4.5, in the near future and under RCP8.5 in the far future (2075–2099). Finally, the expected rate of fire spread is more spatially extended all over the country and particularly from southern to northern parts compared to the historical state.

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“…In terrestrial ecosystems, it should not be forgotten that a fire is a natural ecological factor [8][9][10]. It has been present in the environment since long before humans existed [11].…”

Section: Theoretical Frameworkmentioning

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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Design of a Forest Fire Early Alert System through a Deep 3D-CNN Structure and a WRF-CNN Bias Correction

Cited by 10 publications

References 60 publications

Surface, satellite ozone variations in Northern South America during low anthropogenic emission conditions: a machine learning approach

Surface, satellite ozone variations in Northern South America during low anthropogenic emission conditions: a machine learning approach

Investigation of Fire Weather Danger under a Changing Climate at High Resolution in Greece

Stochastic Approaches Systems to Predictive and Modeling Chilean Wildfires

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