Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem
Forest fires are one of the most important factors in environmental risk assessment and it is the main cause of forest destruction in the Mediterranean region. Forestlands have a number of known benefits such as decreasing soil erosion, containing wild life habitats, etc. Additionally, forests are a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2016-09-01
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| Series: | Geomatics, Natural Hazards & Risk |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/19475705.2015.1084541 |
| _version_ | 1811284256548192256 |
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| author | Onur Satir Suha Berberoglu Cenk Donmez |
| author_facet | Onur Satir Suha Berberoglu Cenk Donmez |
| author_sort | Onur Satir |
| collection | DOAJ |
| description | Forest fires are one of the most important factors in environmental risk assessment and it is the main cause of forest destruction in the Mediterranean region. Forestlands have a number of known benefits such as decreasing soil erosion, containing wild life habitats, etc. Additionally, forests are also important player in carbon cycle and decreasing the climate change impacts. This paper discusses forest fire probability mapping of a Mediterranean forestland using a multiple data assessment technique. An artificial neural network (ANN) method was used to map forest fire probability in Upper Seyhan Basin (USB) in Turkey. Multi-layer perceptron (MLP) approach based on back propagation algorithm was applied in respect to physical, anthropogenic, climate and fire occurrence datasets. Result was validated using relative operating characteristic (ROC) analysis. Coefficient of accuracy of the MLP was 0.83. Landscape features input to the model were assessed statistically to identify the most descriptive factors on forest fire probability mapping using the Pearson correlation coefficient. Landscape features like elevation (R = −0.43), tree cover (R = 0.93) and temperature (R = 0.42) were strongly correlated with forest fire probability in the USB region. |
| first_indexed | 2024-04-13T02:26:05Z |
| format | Article |
| id | doaj.art-a150fd592ae140c4acfe0151ab6fdb0c |
| institution | Directory Open Access Journal |
| issn | 1947-5705 1947-5713 |
| language | English |
| last_indexed | 2024-04-13T02:26:05Z |
| publishDate | 2016-09-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj.art-a150fd592ae140c4acfe0151ab6fdb0c2022-12-22T03:06:46ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132016-09-01751645165810.1080/19475705.2015.10845411084541Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystemOnur Satir0Suha Berberoglu1Cenk Donmez2YuzuncuYil University,Cukurova University,Cukurova University,Forest fires are one of the most important factors in environmental risk assessment and it is the main cause of forest destruction in the Mediterranean region. Forestlands have a number of known benefits such as decreasing soil erosion, containing wild life habitats, etc. Additionally, forests are also important player in carbon cycle and decreasing the climate change impacts. This paper discusses forest fire probability mapping of a Mediterranean forestland using a multiple data assessment technique. An artificial neural network (ANN) method was used to map forest fire probability in Upper Seyhan Basin (USB) in Turkey. Multi-layer perceptron (MLP) approach based on back propagation algorithm was applied in respect to physical, anthropogenic, climate and fire occurrence datasets. Result was validated using relative operating characteristic (ROC) analysis. Coefficient of accuracy of the MLP was 0.83. Landscape features input to the model were assessed statistically to identify the most descriptive factors on forest fire probability mapping using the Pearson correlation coefficient. Landscape features like elevation (R = −0.43), tree cover (R = 0.93) and temperature (R = 0.42) were strongly correlated with forest fire probability in the USB region.http://dx.doi.org/10.1080/19475705.2015.1084541Forest fire probability and hazardlandscape featureweightingartificial neural networkMediterranean regionfire weather index |
| spellingShingle | Onur Satir Suha Berberoglu Cenk Donmez Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem Geomatics, Natural Hazards & Risk Forest fire probability and hazard landscape feature weighting artificial neural network Mediterranean region fire weather index |
| title | Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem |
| title_full | Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem |
| title_fullStr | Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem |
| title_full_unstemmed | Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem |
| title_short | Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem |
| title_sort | mapping regional forest fire probability using artificial neural network model in a mediterranean forest ecosystem |
| topic | Forest fire probability and hazard landscape feature weighting artificial neural network Mediterranean region fire weather index |
| url | http://dx.doi.org/10.1080/19475705.2015.1084541 |
| work_keys_str_mv | AT onursatir mappingregionalforestfireprobabilityusingartificialneuralnetworkmodelinamediterraneanforestecosystem AT suhaberberoglu mappingregionalforestfireprobabilityusingartificialneuralnetworkmodelinamediterraneanforestecosystem AT cenkdonmez mappingregionalforestfireprobabilityusingartificialneuralnetworkmodelinamediterraneanforestecosystem |