Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area
•Five new ensemble models were developed for forest fire susceptibility modeling.•10 conditioning factors were considered in this study.•RF-FR ensemble model outperformed SVM-FR, MLP-FR, CART-FR and LR-FR models. Forest fire disaster is currently the subject of intense research worldwide. The develo...
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| Vydané v: | Ecological indicators Ročník 129; s. 107869 |
|---|---|
| Hlavní autori: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier Ltd
01.10.2021
Elsevier |
| Predmet: | |
| ISSN: | 1470-160X, 1872-7034 |
| On-line prístup: | Získať plný text |
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| Abstract | •Five new ensemble models were developed for forest fire susceptibility modeling.•10 conditioning factors were considered in this study.•RF-FR ensemble model outperformed SVM-FR, MLP-FR, CART-FR and LR-FR models.
Forest fire disaster is currently the subject of intense research worldwide. The development of accurate strategies to prevent potential impacts and minimize the occurrence of disastrous events as much as possible requires modeling and forecasting severe conditions. In this study, we developed five new hybrid machine learning algorithms namely, Frequency Ratio-Multilayer Perceptron (FR-MLP), Frequency Ratio-Logistic Regression (FR-LR), Frequency Ratio-Classification and Regression Tree (FR-CART), Frequency Ratio-Support Vector Machine (FR-SVM), and Frequency Ratio-Random Forest (FR-RF), for mapping forest fire susceptibility in the north of Morocco. To this end, a total of 510 points of historic forest fires as the forest fire inventory map and 10 independent causal factors including elevation, slope, aspect, distance to roads, distance to residential areas, land use, normalized difference vegetation index (NDVI), rainfall, temperature, and wind speed were used. The area under the receiver operating characteristics (ROC) curves (AUC) was computed to assess the effectiveness of the models. The results of conducting proposed models indicated that RF-FR achieved the highest performance (AUC = 0.989), followed by SVM-FR (AUC = 0.959), MLP-FR (AUC = 0.858), CART-FR (AUC = 0.847), LR-FR (AUC = 0.809) in the forecasting of the forest fire. The outcome of this research as a prediction map of forest fire risk areas can provide crucial support for the management of Mediterranean forest ecosystems. Moreover, the results demonstrate that these novel developed hybrid models can increase the accuracy and performance of forest fire susceptibility studies and the approach can be applied to other areas. |
|---|---|
| AbstractList | •Five new ensemble models were developed for forest fire susceptibility modeling.•10 conditioning factors were considered in this study.•RF-FR ensemble model outperformed SVM-FR, MLP-FR, CART-FR and LR-FR models.
Forest fire disaster is currently the subject of intense research worldwide. The development of accurate strategies to prevent potential impacts and minimize the occurrence of disastrous events as much as possible requires modeling and forecasting severe conditions. In this study, we developed five new hybrid machine learning algorithms namely, Frequency Ratio-Multilayer Perceptron (FR-MLP), Frequency Ratio-Logistic Regression (FR-LR), Frequency Ratio-Classification and Regression Tree (FR-CART), Frequency Ratio-Support Vector Machine (FR-SVM), and Frequency Ratio-Random Forest (FR-RF), for mapping forest fire susceptibility in the north of Morocco. To this end, a total of 510 points of historic forest fires as the forest fire inventory map and 10 independent causal factors including elevation, slope, aspect, distance to roads, distance to residential areas, land use, normalized difference vegetation index (NDVI), rainfall, temperature, and wind speed were used. The area under the receiver operating characteristics (ROC) curves (AUC) was computed to assess the effectiveness of the models. The results of conducting proposed models indicated that RF-FR achieved the highest performance (AUC = 0.989), followed by SVM-FR (AUC = 0.959), MLP-FR (AUC = 0.858), CART-FR (AUC = 0.847), LR-FR (AUC = 0.809) in the forecasting of the forest fire. The outcome of this research as a prediction map of forest fire risk areas can provide crucial support for the management of Mediterranean forest ecosystems. Moreover, the results demonstrate that these novel developed hybrid models can increase the accuracy and performance of forest fire susceptibility studies and the approach can be applied to other areas. Forest fire disaster is currently the subject of intense research worldwide. The development of accurate strategies to prevent potential impacts and minimize the occurrence of disastrous events as much as possible requires modeling and forecasting severe conditions. In this study, we developed five new hybrid machine learning algorithms namely, Frequency Ratio-Multilayer Perceptron (FR-MLP), Frequency Ratio-Logistic Regression (FR-LR), Frequency Ratio-Classification and Regression Tree (FR-CART), Frequency Ratio-Support Vector Machine (FR-SVM), and Frequency Ratio-Random Forest (FR-RF), for mapping forest fire susceptibility in the north of Morocco. To this end, a total of 510 points of historic forest fires as the forest fire inventory map and 10 independent causal factors including elevation, slope, aspect, distance to roads, distance to residential areas, land use, normalized difference vegetation index (NDVI), rainfall, temperature, and wind speed were used. The area under the receiver operating characteristics (ROC) curves (AUC) was computed to assess the effectiveness of the models. The results of conducting proposed models indicated that RF-FR achieved the highest performance (AUC = 0.989), followed by SVM-FR (AUC = 0.959), MLP-FR (AUC = 0.858), CART-FR (AUC = 0.847), LR-FR (AUC = 0.809) in the forecasting of the forest fire. The outcome of this research as a prediction map of forest fire risk areas can provide crucial support for the management of Mediterranean forest ecosystems. Moreover, the results demonstrate that these novel developed hybrid models can increase the accuracy and performance of forest fire susceptibility studies and the approach can be applied to other areas. Forest fire disaster is currently the subject of intense research worldwide. The development of accurate strategies to prevent potential impacts and minimize the occurrence of disastrous events as much as possible requires modeling and forecasting severe conditions. In this study, we developed five new hybrid machine learning algorithms namely, Frequency Ratio-Multilayer Perceptron (FR-MLP), Frequency Ratio-Logistic Regression (FR-LR), Frequency Ratio-Classification and Regression Tree (FR-CART), Frequency Ratio-Support Vector Machine (FR-SVM), and Frequency Ratio-Random Forest (FR-RF), for mapping forest fire susceptibility in the north of Morocco. To this end, a total of 510 points of historic forest fires as the forest fire inventory map and 10 independent causal factors including elevation, slope, aspect, distance to roads, distance to residential areas, land use, normalized difference vegetation index (NDVI), rainfall, temperature, and wind speed were used. The area under the receiver operating characteristics (ROC) curves (AUC) was computed to assess the effectiveness of the models. The results of conducting proposed models indicated that RF-FR achieved the highest performance (AUC = 0.989), followed by SVM-FR (AUC = 0.959), MLP-FR (AUC = 0.858), CART-FR (AUC = 0.847), LR-FR (AUC = 0.809) in the forecasting of the forest fire. The outcome of this research as a prediction map of forest fire risk areas can provide crucial support for the management of Mediterranean forest ecosystems. Moreover, the results demonstrate that these novel developed hybrid models can increase the accuracy and performance of forest fire susceptibility studies and the approach can be applied to other areas. |
| ArticleNumber | 107869 |
| Author | Laneve, Giovanni Costache, Romulus Karimi, Firoozeh Nguyen, Hoang Mohajane, Meriame Bao Pham, Quoc Oudija, Fatiha Essahlaoui, Ali |
| Author_xml | – sequence: 1 givenname: Meriame surname: Mohajane fullname: Mohajane, Meriame organization: Soil and Environment Microbiology Team, Department of Biology, Faculty of Sciences, Moulay Ismail University, Zitoune, Meknès BP 11201, Morocco – sequence: 2 givenname: Romulus orcidid: 0000-0002-6876-8572 surname: Costache fullname: Costache, Romulus organization: Department of Civil Engineering, Transilvania University of Brasov, 5, Turnului Str, 500152 Brasov, Romania – sequence: 3 givenname: Firoozeh orcidid: 0000-0002-3381-812X surname: Karimi fullname: Karimi, Firoozeh organization: Department of Geography, Environment, and Sustainability, University of North Carolina-Greensboro, Greensboro, NC 27402, USA – sequence: 4 givenname: Quoc surname: Bao Pham fullname: Bao Pham, Quoc email: phambaoquoc@tdmu.edu.vn organization: Institute of Applied Technology, Thu Dau Mot University, Binh Duong Province, Viet Nam – sequence: 5 givenname: Ali surname: Essahlaoui fullname: Essahlaoui, Ali organization: Geo-Engineering and Environment Laboratory,Water Sciences and Environment Engineering Team, Department of Geology, Faculty of Sciences, Moulay Ismail University,Zitoune, Meknès BP 11201, Morocco – sequence: 6 givenname: Hoang orcidid: 0000-0001-6122-8314 surname: Nguyen fullname: Nguyen, Hoang organization: Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Pho Vien, Duc Thang Ward, Bac Tu Liem District, Hanoi 100000, Viet Nam – sequence: 7 givenname: Giovanni surname: Laneve fullname: Laneve, Giovanni organization: University of Rome 'La Sapienza', Scuola di Ingegneria Aerospaziale, Via Salaria 851, 00138 Rome, Italy – sequence: 8 givenname: Fatiha surname: Oudija fullname: Oudija, Fatiha organization: Soil and Environment Microbiology Team, Department of Biology, Faculty of Sciences, Moulay Ismail University, Zitoune, Meknès BP 11201, Morocco |
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| Keywords | Mediterranean area Forest fire Remote sensing Hybrid machine learning algorithm |
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| SubjectTerms | Forest fire forest fires forests Hybrid machine learning algorithm hybrids inventories land use Mediterranean area Mediterranean region Morocco prediction rain regression analysis Remote sensing risk temperature wind speed |
| Title | Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area |
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