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Mishra M, Guria R, Baraj B, Nanda AP, Santos CAG, Silva RMD, Laksono FAT. Spatial analysis and machine learning prediction of forest fire susceptibility: a comprehensive approach for effective management and mitigation. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 926:171713. [PMID: 38503392 DOI: 10.1016/j.scitotenv.2024.171713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/11/2024] [Accepted: 03/12/2024] [Indexed: 03/21/2024]
Abstract
Forest fires (FF) in tropical seasonal forests impact ecosystem. Addressing FF in tropical ecosystems has become a priority to mitigate impacts on biodiversity loss and climate change. The escalating frequency and intensity of FF globally have become a mounting concern. Understanding their tendencies, patterns, and vulnerabilities is imperative for conserving ecosystems and facilitating the development of effective prevention and management strategies. This study investigates the trends, patterns, and spatiotemporal distribution of FF for the period of 2001-2022, and delineates the forest fire susceptibility zones in Odisha State, India. The study utilized: (a) MODIS imagery to examine active fire point data; (b) Kernel density tools; (c) FF risk prediction using two machine learning algorithms, namely Support Vector Machine (SVM) and Random Forest (RF); (d) Receiver Operating Characteristic and Area Under the Curve, along with various evaluation metrics; and (e) a total of 19 factors, including three topographical, seven climatic, four biophysical, and five anthropogenic, to create a map indicating areas vulnerable to FF. The validation results revealed that the RF model achieved a precision exceeding 94 % on the validation datasets, while the SVM model reached 89 %. The estimated forest fire susceptibility zones using RF and SVM techniques indicated that 20.14 % and 16.72 % of the area, respectively, fall under the "Very High Forest Fire" susceptibility class. Trend analysis reveals a general upward trend in forest fire occurrences (R2 = 0.59), with a notable increase after 2015, peaking in 2021. Notably, Angul district was identified as the most affected area, documenting the highest number of forest fire incidents over the past 22 years. Additionally, forest fire mitigation plans have been developed by drawing insights from forest fire management strategies implemented in various countries worldwide. Overall, this analysis provides valuable insights for policymakers and forest management authorities to develop effective strategies for forest fire prevention and mitigation.
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Affiliation(s)
- Manoranjan Mishra
- Department of Geography, Fakir Mohan University, Vyasa Vihar, Nuapadhi, Balasore 756089, Odisha, India
| | - Rajkumar Guria
- Department of Geography, Fakir Mohan University, Vyasa Vihar, Nuapadhi, Balasore 756089, Odisha, India
| | - Biswaranjan Baraj
- Department of Geography, Fakir Mohan University, Vyasa Vihar, Nuapadhi, Balasore 756089, Odisha, India
| | - Ambika Prasad Nanda
- Tata Steel Rural Development Society, Kalinganagar, Above SBI ATM Duburi Chowk, Jajpur district 755026, Odisha, India.
| | - Celso Augusto Guimarães Santos
- Department of Civil and Environmental Engineering, Federal University of Paraíba, João Pessoa 58051-900, Paraíba, Brazil.
| | | | - Fx Anjar Tri Laksono
- Department of Geology and Meteorology, Institute of Geography and Earth Sciences, Faculty of Sciences, University of Pécs, H-7624 Pécs, Hungary; Department of Geological Engineering, Faculty of Engineering, Jenderal Soedirman University, 53371 Purbalingga, Indonesia.
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Tran TTK, Janizadeh S, Bateni SM, Jun C, Kim D, Trauernicht C, Rezaie F, Giambelluca TW, Panahi M. Improving the prediction of wildfire susceptibility on Hawai'i Island, Hawai'i, using explainable hybrid machine learning models. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 351:119724. [PMID: 38061099 DOI: 10.1016/j.jenvman.2023.119724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 11/13/2023] [Accepted: 11/25/2023] [Indexed: 01/14/2024]
Abstract
This study presents a comparative analysis of four Machine Learning (ML) models used to map wildfire susceptibility on Hawai'i Island, Hawai'i. Extreme Gradient Boosting (XGBoost) combined with three meta-heuristic algorithms - Whale Optimization (WOA), Black Widow Optimization (BWO), and Butterfly Optimization (BOA) - were employed to map areas susceptible to wildfire. To generate a wildfire inventory, 1408 wildfire points were identified within the study area from 2004 to 2022. The four ML models (XGBoost, WOA-XGBoost, BWO-XGBoost, and BOA-XGBoost) were run using 14 wildfire-conditioning factors categorized into four main groups: topographical, meteorological, vegetation, and anthropogenic. Six performance metrics - sensitivity, specificity, positive predictive values, negative predictive values, the Area Under the receiver operating characteristic Curve (AUC), and the average precision (AP) of Precision-Recall Curves (PRCs) - were used to compare the predictive performance of the ML models. The SHapley Additive exPlanations (SHAP) framework was also used to interpret the importance values of the 14 influential variables for the modeling of wildfire on Hawai'i Island using the four models. The results of the wildfire modeling indicated that all four models performed well, with the BWO-XGBoost model exhibiting a slightly higher prediction performance (AUC = 0.9269), followed by WOA-XGBoost (AUC = 0.9253), BOA-XGBoost (AUC = 0.9232), and XGBoost (AUC = 0.9164). SHAP analysis revealed that the distance from a road, annual temperature, and elevation were the most influential factors. The wildfire susceptibility maps generated in this study can be used by local authorities for wildfire management and fire suppression activity.
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Affiliation(s)
- Trang Thi Kieu Tran
- Department of Civil, Environmental and Construction Engineering and Water Resources Research Center, University of Hawai'i at Manoa, Honolulu, HI, 96822, USA.
| | - Saeid Janizadeh
- Department of Civil, Environmental and Construction Engineering and Water Resources Research Center, University of Hawai'i at Manoa, Honolulu, HI, 96822, USA.
| | - Sayed M Bateni
- Department of Civil, Environmental and Construction Engineering and Water Resources Research Center, University of Hawai'i at Manoa, Honolulu, HI, 96822, USA.
| | - Changhyun Jun
- Department of Civil and Environmental Engineering, College of Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea.
| | - Dongkyun Kim
- Department of Civil Engineering, Hongik University, Mapo-Gu, Seoul, Republic of Korea.
| | - Clay Trauernicht
- Department of Natural Resources and Environmental Management, University of Hawai'i at Manoa, Honolulu, HI, 96822, USA.
| | - Fatemeh Rezaie
- Department of Civil, Environmental and Construction Engineering and Water Resources Research Center, University of Hawai'i at Manoa, Honolulu, HI, 96822, USA; Geoscience Data Center, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124 Gwahak-ro, Yuseong-gu, Daejeon, 34132, Republic of Korea; Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro, Yuseong-gu, Daejeon, 34113, Republic of Korea.
| | - Thomas W Giambelluca
- Water Resources Research Center, University of Hawai'i at Manoa, Honolulu, HI, 96822, USA.
| | - Mahdi Panahi
- Department of Civil, Environmental and Construction Engineering and Water Resources Research Center, University of Hawai'i at Manoa, Honolulu, HI, 96822, USA.
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Chicas SD, Nielsen JØ, Robinson GM, Mizoue N, Ota T. The adoption of climate-smart agriculture to address wildfires in the Maya Golden Landscape of Belize: Smallholder farmers' perceptions. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 345:118562. [PMID: 37423190 DOI: 10.1016/j.jenvman.2023.118562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 05/16/2023] [Accepted: 06/30/2023] [Indexed: 07/11/2023]
Abstract
Ecosystems around the globe are enduring wildfires with greater frequency, intensity, and severity and this trend is projected to continue as a result of climate change. Climate-smart agriculture (CSA) has been proposed as a strategy to prevent wildfires and mitigate climate change impacts; however, it remains poorly understood as a strategy to prevent wildfires. Therefore, the authors propose a multimethod approach that combines mapping of wildfire susceptibility and social surveys to identify priority areas, main factors influencing the adoption of CSA practices, barriers to their implementation, and the best CSA practices that can be implemented to mitigate wildfires in Belize's Maya Golden Landscape (MGL). Farmers ranked slash and mulch, crop diversification, and agroforestry as the main CSA practices that can be implemented to address wildfires caused by agriculture in the MGL. In order to reduce wildfire risk, these practices should, be implemented in agricultural areas near wildlands with high wildfire susceptibility and during the fire season (February-May), in the case of slash and mulch. However, socio-demographic and economic characteristics, together with a lack of training and extension services support, inadequate consultation by agencies, and limited financial resources, hinder the broader adoption of CSA practices in the MGL. Our research produced actionable and valuable information that can be used to design policies and programs to mitigate the impacts of climate change and wildfire risk in the MGL. This approach can also be used in other regions where wildfires are caused by agricultural practices to identify priority areas, barriers and suitable CSA practices that can be implemented to mitigate wildfires.
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Affiliation(s)
- Santos Daniel Chicas
- Department of Agro-Environmental Science, Faculty of Agriculture, Kyushu University, Fukuoka, Japan.
| | - Jonas Østergaard Nielsen
- IRI-THESys and Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099, Germany.
| | - Guy M Robinson
- Department of Geography, Environment and Population, School of Social Sciences, University of Adelaide, Adelaide, South Australia, 5005, Australia; Department of Land Economy, University of Cambridge, Cambridge, CB3 9EP, United Kingdom.
| | - Nobuya Mizoue
- Department of Agro-Environmental Science, Faculty of Agriculture, Kyushu University, Fukuoka, Japan.
| | - Tetsuji Ota
- Department of Agro-Environmental Science, Faculty of Agriculture, Kyushu University, Fukuoka, Japan.
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Kartal S, Sekertekin A. Prediction of MODIS land surface temperature using new hybrid models based on spatial interpolation techniques and deep learning models. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:67115-67134. [PMID: 35522410 DOI: 10.1007/s11356-022-20572-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 04/28/2022] [Indexed: 06/14/2023]
Abstract
Land surface temperature (LST) prediction is of great importance for climate change, ecology, environmental and industrial studies. These studies require accurate LST map predictions considering both spatial and temporal dynamics. In this study, multilayer perceptron (MLP), long short-term memory (LSTM) and an integrated machine learning model, namely Convolutional LSTM (ConvLSTM), were utilized for one step ahead LST prediction. Data were gathered from 1-day (MYD11A1) and 8-day composite (MYD11A2) Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, which have 1-km × 1-km spatial resolution. Considering the inability of MODIS sensors to provide LST data under cloudy conditions, Inverse DISTANCE WEIGHTING (IDW), natural neighbor (NN), and cubic spline (C) methods were used to overcome the missing pixel problem. The proposed methods were tested over the Northern part of Adana province, Turkey, and the performances of the models were quantitatively evaluated through performance measures, namely, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The selected datasets range from 01 January 2017 to 01 November 2020 and from 01 January 2015 to 01 November 2020 for daily LST and 8-day composite LST, respectively. While 60% of the datasets were used as training set, the remaining 40% were used as validation (20%) and test (20%) sets. RMSE maps were generated to evaluate the pixelwise performance of the proposed method. On the other hand, the best average RMSE and MAE for the daily test set were obtained from the combination of ConvLSTM and NN (NN-ConvLSTM) as 3.62 °C and 2.85 °C, respectively, while they were acquired 3.57 °C and 2.69 oC from the combination of MLP and NN (NN-MLP) for the 8-day composite LST test set. The results revealed that the proposed hybrid models could be used for one step ahead spatiotemporal prediction of LST data.
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Affiliation(s)
- Serkan Kartal
- Department of Computer Engineering, Engineering Faculty, Cukurova University, Saricam/Adana, Turkey
| | - Aliihsan Sekertekin
- Department of Architecture and Town Planning, Vocational School of Higher Education for Technical Sciences, Igdir University, Igdir, Turkey.
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