Design of a Forest Fire Early Alert System through a Deep 3D-CNN Structure and a WRF-CNN Bias Correction
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Areas
2.2. Occurrence Probability of a Wildfire
2.2.1. Description of the 3D Convolutional Networks and Evaluation
2.2.2. Description of the WRF-1D Model and Evaluation
2.2.3. Fire Land Cover Index
2.3. Global Vulnerability and Wildfire Risk
2.3.1. Global Vulnerability
2.3.2. Probability of Wildfire Occurrence
2.3.3. Wildfire Risk Management
- Phase 1—Design of sustainable Soil Management Practices: to determine preliminary management practices, public policy and management documents (15) from different Latin American countries (Mexico, Colombia, Peru and Chile) were evaluated (see Tables S6 and S7), taking into account their potential for replicability in the region. The evaluation consisted of a double-entry matrix where the presence of the practice was valued one and, conversely, zero if not present. Subsequently, the values were aggregated to prioritize the inclusion of the practices in the SMP protocol. It is important to note that the practices were divided into pre- (8) and postfire (11) practices.
- Phase 2—Creation of integrated recommendations: The SMP actions previously identified were redesigned to include the physical characteristics of the territory, such as: land use (to articulate recommendations related to the activities conducted in the zone), land cover, the susceptibility of the vegetation cover to ignition obtained from [48] and the change in the susceptibility of the cover to ignition over time. It is important to mention that in this study, the moisture content was not included as a physical parameter for two reasons: (i) the IDEAM [60] method allows the calculation of susceptibility without considering soil humidity, and without increasing uncertainties; and (ii) neither area has data for the soil moisture content. Despite the existence of reanalysis products for this variable, without in situ measurements to validate them, it is better to avoid its usage.
3. Results and Discussion
3.1. Meteorological Model Evaluation
3.1.1. 3D-Convulutional Networks Evaluation
3.1.2. WRF Bias Correction Technique Evaluation
3.2. Occurrence Probability of a Wildfire
3.3. Global Vulnerability
3.4. Risk Management
3.4.1. Risk Daily Maps for the Study Areas
3.4.2. Early Alert Actions
3.4.3. Prevention and Mitigation Strategies
4. Conclusions and Future Work
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Predominant Type of Coverage | Fuel Type | Susceptibility Category | Value |
---|---|---|---|
Forest | Shrubbery | Low | 2 |
Fragmented Forest | Trees | Medium | 3 |
Gallery and riparian forest | Trees | Low | 2 |
Shrubland | Shrubbery | High | 4 |
Mosaic of crops, pastures and natural spaces | Grass/Herbs | Very High | 5 |
Mosaic of pastures with natural spaces | Grass/Herbs | Very High | 5 |
Mosaic of pastures and crops | Grass/Herbs | Very High | 5 |
Crop Mosaic | Herbs | High | 4 |
Grass | Grass | Very High | 7 |
Grassland | Herbs | Very High | 6 |
Glacial and snowy areas | No combustible | - | 1 |
Urban areas | No combustible | - | 1 |
Category | FWI | Normalized FWI | WFLC | WFP | Global Vulnerability | Fire Risk |
---|---|---|---|---|---|---|
Very Low | <5.2 | 0. 052 | 0–0.2 | 0–0.126 | 1–3.8 | 0–0.48 |
Low | 5.2–11.2 | 0.052–0.11 | 0.2–0.4 | 0.126–0.255 | 3.8–6.6 | 0.48–1.68 |
Moderate | 11.2–21.3 | 0.11–0.213 | 0.4–0.6 | 0.255–0.405 | 6.6–9.4 | 1.68–3.81 |
High | 21.3–38 | 0.213–0.38 | 0.6–0.8 | 0.405–0.59 | 9.4–12.2 | 3.81–7.19 |
Very High/ Extreme | 38–100 | 0.38–1 | 0.8–1 | 0.59–1 | 12.2–15 | 7.19–15 |
Alert Level | General Recommendations for Public Authorities | Towns with Low Risk | Towns with Medium Risk | Towns with High Risk |
---|---|---|---|---|
Yellow Alert | Scan the area of alert for flammable objects (e.g., debris) or sparkling objects (e.g., lit cigarettes, lighters, fireworks) and remove them. Suspend campfires and other fire-related activities in the area and ridge the campsite with rocks or dirt. Water the grass around the area. | Identify and characterize the area and forest to be protected (risk map). Educate and train fire departments and other relief agencies that have forestry brigades. | Identify and provide the necessary tools, equipment and access. Have a historical fire log of events. Activate the environmental surveillance network. Review and verify the capacity of response agencies. | Verify the status and availability of resources. Identify water sources or storage tanks. Have an up-to-date risk map. |
Orange Alert | Scan the area of alert for flammable objects (e.g., debris) or sparkling objects (e.g., lit cigarettes, lighters, fireworks) and remove them. Suspend campfires and other fire-related activities in the area and ridge the campsite with rocks or dirt. Water the grass around the area. Prepare and bring fire equipment and human resources to the area (e.g., firefighters, fire engines, extinguishers, protective gear). | Activate a response network in charge of monitoring the risk of fires in the area. | Verify the tools, equipment and accessories necessary for care. Prepare hydrometeorological reports to ascertain the behavior of the climate. | Activate the sound and/or visible siren (beacons) of vehicles. |
Red Alert | A timely (in the shortest possible time) and effective (location-based) warning related to the observation of columns of smoke or sources of fire, which can cause forest fires. Scan the area of alert for flammable objects (e.g., debris) or sparkling objects (e.g., lit cigarettes, lighters, fireworks) and remove them. Suspend any human activities and evacuate the area. Water the grass around the area. Prepare and bring fire equipment and human resources to the area (e.g., firefighters, fire engines, extinguishers, protective gear). Remove any vehicles or heavy machinery and tools that can cause a spark, produce heat or contain flammable liquids (e.g., fuel). Alert neighboring political authorities for a possible activation of their contingency plan, which can include greater efforts to prepare for a larger event (helicopters, international aid, among others). | Activate and mobilize resources of authorities responsible for control, extinction, liquidation and recovery. | Establish functional entry, exit and/or evacuation routes. | Prepare the line of defense or backfire to have a unit in each section that verifies that the possible fire does not exceed it. |
Type of Practice | Soil Management Practice | Definition |
---|---|---|
1. Practices before a wildfire | 1.1. Mechanical reduction, modification, use and/or elimination of forest fuels. | Modification of land covers susceptible to wildfires in order to reduce the risk of ignition. |
1.2. Education and training focused on fire management on the soil | Community training related to wildfire management. | |
1.3. Regulatory actions and government management. | Strengthening regulatory system actions towards wildfire management. | |
2. Practices after a wildfire | 2.1. Recovery of land cover in affected areas by wildfires | Changes towards land cover not susceptible to wildfires in affected areas. |
2.2. Sanitation on forest masses | Cleaning affected areas, eliminating fuel forest masses. | |
2.3. Postfire soil control | Actions to reduce susceptibility to erosion in affected areas. |
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Casallas, A.; Jiménez-Saenz, C.; Torres, V.; Quirama-Aguilar, M.; Lizcano, A.; Lopez-Barrera, E.A.; Ferro, C.; Celis, N.; Arenas, R. Design of a Forest Fire Early Alert System through a Deep 3D-CNN Structure and a WRF-CNN Bias Correction. Sensors 2022, 22, 8790. https://doi.org/10.3390/s22228790
Casallas A, Jiménez-Saenz C, Torres V, Quirama-Aguilar M, Lizcano A, Lopez-Barrera EA, Ferro C, Celis N, Arenas R. Design of a Forest Fire Early Alert System through a Deep 3D-CNN Structure and a WRF-CNN Bias Correction. Sensors. 2022; 22(22):8790. https://doi.org/10.3390/s22228790
Chicago/Turabian StyleCasallas, Alejandro, Camila Jiménez-Saenz, Victor Torres, Miguel Quirama-Aguilar, Augusto Lizcano, Ellie Anne Lopez-Barrera, Camilo Ferro, Nathalia Celis, and Ricardo Arenas. 2022. "Design of a Forest Fire Early Alert System through a Deep 3D-CNN Structure and a WRF-CNN Bias Correction" Sensors 22, no. 22: 8790. https://doi.org/10.3390/s22228790