Gis and fuzzy logic approach for forest fire risk modeling in the Cajamarca region, Peru
Forest fires are a potential threat to life, as they contribute to reducing forest areas, impact on the services we expect from ecosystems, the health of the inhabitants is affected by smoke and the economic costs for the recovery of affected areas is high. The objective of the study is to...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Growing Science
2023-01-01
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| Series: | Decision Science Letters |
| Online Access: | http://www.growingscience.com/dsl/Vol12/dsl_2023_1.pdf |
| _version_ | 1797865857875968000 |
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| author | Alex Vergara Anticona Candy Ocaña Zúñiga Alexandre Rosa dos Santos Alexandre Simões Lorenzon Plinio Guerra Filho |
| author_facet | Alex Vergara Anticona Candy Ocaña Zúñiga Alexandre Rosa dos Santos Alexandre Simões Lorenzon Plinio Guerra Filho |
| author_sort | Alex Vergara Anticona |
| collection | DOAJ |
| description | Forest fires are a potential threat to life, as they contribute to reducing forest areas, impact on the services we expect from ecosystems, the health of the inhabitants is affected by smoke and the economic costs for the recovery of affected areas is high. The objective of the study is to apply fuzzy logic to model the risk of forest fires in the Cajamarca-Peru region, incorporating variables that represent biological, topographic, socioeconomic, and meteorological factors. The analysis was based on the acquisition, editing and rasterization of the database, application of fuzzy membership functions and image fuzzification, fuzzy superposition and spatial reclassification of forest fire risk. The results obtained show that 71.68% of the area is under very low or medium forest fire risk. However, 28.32% of the study area has a high to very high fire risk, which makes the occurrence of fires susceptible to the lack of rain and water in the soil. It was found that biological, topographic, and socioeconomic factors with their respective variables are directly influenced by meteorological factor variables such as temperature, rainfall and water availability. Fuzzy logic offered flexibility in modeling wildfire risk in the region, proving to be a useful tool for predicting and mapping wildfire risk. |
| first_indexed | 2024-04-09T23:15:58Z |
| format | Article |
| id | doaj.art-0391eecf342f43319c59ed0a88bee6af |
| institution | Directory Open Access Journal |
| issn | 1929-5804 1929-5812 |
| language | English |
| last_indexed | 2024-04-09T23:15:58Z |
| publishDate | 2023-01-01 |
| publisher | Growing Science |
| record_format | Article |
| series | Decision Science Letters |
| spelling | doaj.art-0391eecf342f43319c59ed0a88bee6af2023-03-22T08:44:43ZengGrowing ScienceDecision Science Letters1929-58041929-58122023-01-0112235336810.5267/j.dsl.2023.1.002Gis and fuzzy logic approach for forest fire risk modeling in the Cajamarca region, PeruAlex Vergara AnticonaCandy Ocaña ZúñigaAlexandre Rosa dos SantosAlexandre Simões Lorenzon Plinio Guerra Filho Forest fires are a potential threat to life, as they contribute to reducing forest areas, impact on the services we expect from ecosystems, the health of the inhabitants is affected by smoke and the economic costs for the recovery of affected areas is high. The objective of the study is to apply fuzzy logic to model the risk of forest fires in the Cajamarca-Peru region, incorporating variables that represent biological, topographic, socioeconomic, and meteorological factors. The analysis was based on the acquisition, editing and rasterization of the database, application of fuzzy membership functions and image fuzzification, fuzzy superposition and spatial reclassification of forest fire risk. The results obtained show that 71.68% of the area is under very low or medium forest fire risk. However, 28.32% of the study area has a high to very high fire risk, which makes the occurrence of fires susceptible to the lack of rain and water in the soil. It was found that biological, topographic, and socioeconomic factors with their respective variables are directly influenced by meteorological factor variables such as temperature, rainfall and water availability. Fuzzy logic offered flexibility in modeling wildfire risk in the region, proving to be a useful tool for predicting and mapping wildfire risk.http://www.growingscience.com/dsl/Vol12/dsl_2023_1.pdf |
| spellingShingle | Alex Vergara Anticona Candy Ocaña Zúñiga Alexandre Rosa dos Santos Alexandre Simões Lorenzon Plinio Guerra Filho Gis and fuzzy logic approach for forest fire risk modeling in the Cajamarca region, Peru Decision Science Letters |
| title | Gis and fuzzy logic approach for forest fire risk modeling in the Cajamarca region, Peru |
| title_full | Gis and fuzzy logic approach for forest fire risk modeling in the Cajamarca region, Peru |
| title_fullStr | Gis and fuzzy logic approach for forest fire risk modeling in the Cajamarca region, Peru |
| title_full_unstemmed | Gis and fuzzy logic approach for forest fire risk modeling in the Cajamarca region, Peru |
| title_short | Gis and fuzzy logic approach for forest fire risk modeling in the Cajamarca region, Peru |
| title_sort | gis and fuzzy logic approach for forest fire risk modeling in the cajamarca region peru |
| url | http://www.growingscience.com/dsl/Vol12/dsl_2023_1.pdf |
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