A Systematic Review of Applications of Machine Learning Techniques for Wildfire Management Decision Support
Abstract
:1. Introduction
Objective, Contribution, and Organization of the Work
- A summary of the applications developed by the studies mentioned;
- A classification according to the study type, main application of the model, machine learning technique, case study location, and performance metrics.
2. Systematic Review Methodology
2.1. Database and Search Terms
2.2. Eligibility Criteria
2.3. Data Collection Results
3. Results
3.1. Related Reviews
3.2. Machine-Learning-Based Applications
3.2.1. Pre-Fire Prevention and Preparedness
Wildfire Fuel Modelling
Risk Assessment and Ignition Prediction of Wildfires
Support to Dispatch
Landscape Planning and Prevention Measures for Severity Mitigation
Development of Inventory Data
3.2.2. Management of Active Wildfires (Detection and Response)
Wildfire Detection
Wildfire Spread Prediction
Wildfire Suppression
3.2.3. Post-Fire Wildfires (Restoration and Adaptation Activities)
Burned Area and Severity
Impacts Related to Social Factors
Impacts Related to Carbon Fluxes
Impacts Related to Forest Conditions
3.3. Machine-Learning-Based Model Features and Feature Selection Sensitivity Analysis
3.4. Identified Research Trends and Challenges
- The increase in the use of ensemble modelling, which can reduce the inaccuracy and the computational time of the models by combining different models and machine learning techniques.
- The shifting focus from obtaining detailed physical interpretations to obtaining faster results based on input and output variables, especially at the stages in which the computational time of the modelling process is of critical importance, such as when dealing with active wildfires.
- The increasing concern with reducing the subjective bias associated with expert-opinion-based methods, as well as with incorporating uncertainty analysis at the modelling or sub-modelling stages.
- The use in many papers of the association of remote sensing imageries, machine learning, and geospatial analysis to predict or classify variables of interest, in order to identify areas that are prone to wildfire occurrence.
- The exploration of classification and prediction using the main machine learning methods of random forest, support vector machine, and neural networks.
- The use, in many documents, of the association of multiple methods to obtain a more accurate set of candidate models for the same area, to provide a more refined sampling and to choose a final model, indicating that the researchers are exploring an integrated approach to benefit from the capabilities of different methods to assess the complex problem of wildfire modelling.
- The need for constant improvement, mainly to obtain faster models while enhancing the interpretability of the results. This is still a critical factor in machine-learning-based models.
- Overall, the studies did not present the computational time associated with the modelling process, which is necessary to evaluate the applicability of the models in such an important field as disaster management.
- Few studies developed feature selection to increase the efficiency of decision support.
- Lack of information about simulation platforms, precluding the comparison of the computational efficiency of different tools.
- Lack of experience of using global models that can be applied to different regions and with different datasets and thus of assessing the potential for generalization of the models via a parametric study of bias–variance trade-offs.
- Few studies are available that focus on the multifunctionality of forested landscape management planning, i.e., on integrating wildfire protection concerns in contexts characterized by demands for multiple ecosystem services.
- There is almost no analysis on the bare minimum amount of data needed for a useful model, especially for the active wildfire stage.
- Despite the fact that some methods deal with uncertainty, when models encompass forecasting the uncertainty may be substantial, thus impacting the results (as in the case of models that consider weather features) associated with management prescriptions.
- There are still few datasets for extensive wildfires, and most of the models are developed using smaller wildfire events for training, which may not correctly reflect the context of extreme wildfire events.
- The acquisition of landscape dynamics data usually requires migrating data between datasets to quantify spatial patterns and changes through space and time. More developments are still required in this field.
- There may be issues regarding model overfitting that still need to be addressed.
- There is a need to bridge the gaps between monitorization and learning, and the decision-making process.
- There is a need for broader models that can help integrate the different wildfire management stages.
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Ref. | Title | Addressed Issues |
---|---|---|
[8] | A review of machine learning applications in wildfire science and management | Overview of popular machine learning methods, review of applications and advantages and limitations of the methods |
[49] | Forest fire induced Natech risk assessment: A survey of geospatial technologies | Review methods based on geospatial information systems (GIS) for modelling wildfire risk and their Natural Hazards Triggering Technological Disasters (Natech) potential |
[50] | A Survey of Machine Learning Algorithms Based Forest Fires Prediction and Detection Systems | Review of various methods used in forest fires prediction and detection |
Ref. | Classification 1 | Detailed Application | Machine Learning Technique | Case Study Location | Model Performance Metrics Results |
---|---|---|---|---|---|
[51] | A | Methods of Fuel Management Zone improvement | Extreme Gradient Boosting, Support Vector Machines, K-Nearest Neighbours, and Random Forest. | F1-score ranging from 90.0% up to 94.0% and a Kappa ranging from 0.80 up to 0.89. | |
[53] | A | Determining fuel moisture content | Multiple Linear Regression, Random Forests, Gradient Boosted Regression, and Neural Networks | United States | Errors between 25.0–33.0% |
[54] | A | Estimating fine dead fuel load and understand its determining factors | Multiple Linear Regression, Random Forest, and Support Vector Machine | Random Forest: RMSE: 0.09; MSE: 0.01; r: 0.71; R-2: 0.50) | |
[55] | A | Estimating 10 h fuel moisture content | Random Forest and Support Vector Machine | (R-2= 0.77–0.82 and root mean squared error [RMSE] = 2.0–2.8%) | |
[56] | A | Detection of dead trees from aerial images | Mask Region-Based Convolutional Neural Network | Mean average precision score = 54.0% | |
[57] | A | Detection of ignitable liquids on ground-truth fire debris | Neural Networks | False positive rate of 0.07 and a true positive rate of 0.59 | |
[58] | A | Detection of ignitable liquid residue on fire debris | Linear and Quadratic Discriminant Analysis, K-Nearest Neighbours, and Support Vector Machines with Radial and Linear Kernels | Area under the receiver operating characteristic curve (0.86–0.92), Equal error rates (17.0–22.0%) | |
[60] | B | Methods for properly calibrating statistical and machine learning models for fine-scale, spatially explicit daily fire occurrence prediction | Classification Trees, Random Forests, Neural Networks, Logistic Regression Models, and Logistic Generalized Additive Models | Alberta, Canada | |
[61] | B | Monitoring fire risks over a large region | Transductive PU Learning | Southeast China | High sensitivity (>80.0%) |
[2] | B | Addressing the multidimensional effects of three groups of drivers in territorial planning under fire risk | Random Undersampling and Boosting | Chile | Area under the receiver operating characteristic curve of 0.967 and an overall accuracy over test data of 93.0% |
[63] | B | Modelling fire danger | Support Vector Machine, Generalized Linear Model, Functional Data Analysis, and Random Forest | Iran | Area under the receiver operating characteristic curve of 0.855 |
[64] | B | Analysing the influences of climate warming on fire risk | Random Forest, Support Vector Machine and Polynomial | Changsha, China | |
[65] | B | Determining the risk of fire | Maxent and Random Forest | Yakutia, Russia | |
[66] | B | Developing spatial prediction of wildfire susceptibility | Artificial Neural Network, Support Vector Machines, and Random Forest | Iran | Accuracies between 74.0–88.0% |
[67] | B | Defining a long-term wildfire warning index | Fuzzy K-Nearest Neighbours | Brazil | |
[68] | B | Optimizing risk indexing for fire risk assessment | Deep Neural Networks | Korea | |
[69] | B | Determining the main explanatory variables for forest fire occurrence and mapping of probability | Random Forest | Eastern Serbia | |
[70] | B | Developing an hourly forest fire risk index | CatBoost | South Korea | Area under the receiver operating characteristic curve = 0.8434 |
[71] | B | Estimating and analysing how human activity is influencing forest fire probability | Maximum Entropy (Maxent) and Random Forest | South Korea | |
[72] | B | Prediction of fire susceptibility and effects of sample patch sizes on the predictive performance of the algorithms | Deep Neural Network | Chile | Area under the curve = 0.953 |
[73] | B | Elaborating a wildfire susceptibility map | Random Forests | Italy | |
[74] | B | Developing a forest fire susceptibility map | LogitBoost Ensemble-Based Decision Tree | Vietnam | 92.0% prediction capability |
[75] | B | Weighted approach to characterizing the forest fire susceptibility | Artificial Neural Network, Generalized Linear Model, Multivariate Adaptive Regression Splines, Naive Bayesian Classifier, K-Nearest Neighbour, Support Vector Machine, Random Forest, Gradient Boosting Machine, Adaptive Boosting, and Maximum Entropy (Maxent) | Kerala, India | Receiver operating characteristics—area under curve values ranging from 0.869 to 0.924 |
[76] | B | Generating susceptibility maps of forest fires | Support Vector Machine, Random Forest, and Ensemble | Serbia | Ensemble model had an area under curve = 0.848 |
[77] | B | Developing a model, in which probabilistic outputs allowed elaboration of wildfire susceptibility maps. | Random Forest | Bolivia | |
[78] | B | Prediction and mapping of fire susceptibility | Bayes Network, Naive Bayes, Decision Tree, and Multivariate Logistic Regression | Pu Mat National Park, Nghe An Province, Vietnam | Area under curve = 0.96 |
[79] | B | Predicting the fire hazard in a fire-prone area | Boosted Regression Tree, Classification and Regression Trees, Functional Discriminant Analysis, Generalized Linear Model, Mixture Discriminant Analysis, Random Forest | Northeast Iran, Golestan Province. | Area under curve = 0.855 |
[80] | B | Producing an accurate multi-hazard risk map for a mountainous area | Support Vector Machine, Boosted Regression Tree, and Generalized Linear Model | Mountainous region of Iran | |
[81] | B | Investigating the impact of satellite-derived metrics that represent long-term vegetation status and dynamics on fire risk mapping | Logistic Regression, Random Forest, and Extreme Gradient Boosting | Mediterranean woodlands and forests | |
[82] | B | Mapping forest fire susceptibility | Frequency Ratio–Multilayer Perceptron, Frequency Ratio—Logistic Regression, Frequency Ratio–Classification and Regression Tree, Frequency Ratio–Support Vector Machine, and Frequency Ratio–Random Forest | North Morocco | Area under curve = 0.989 |
[83] | B | Prediction of forest fire susceptibility | Locally Weighted Learning Algorithm with the Cascade Generalization, Bagging, Decorate, and Dagging Ensemble Learning | Vietnam | Area under curve = 0.993 |
[84] | B | Computing the probability of hazard occurrence | Support Vector Machine and Genetic Algorithm | ||
[85] | B | Predicting and detecting changes in the spatial pattern of ignition probability over time. | Random Forest | Brazil | Area under curve = 0.72 |
[86] | B | Estimating wildfire probability occurrence as a function of biophysical and human-related drivers | Artificial Neural Network | Alpine and subalpine region | Area under curve = 0.68–0.72 |
[87] | B | Predicting the occurrence of wildfires | Artificial Neural Network and Support Vector Machine | Prediction accuracy = 98.3% | |
[3] | B | Estimating and predicting wildfire ignition risk | Deep Neural Network | ||
[4] | B | Proposing forest fire prediction map | Maximum Entropy | Brazil and Australia | Area under curve = 0.95 |
[1] | B | Predicting the wildfire risk | Random Forest | Monticello and Winters, California | Accuracy of 92.0% |
[90] | B | Proposing a spatial prediction model for forest fire susceptibility | Convolutional Neural Network | Yunnan Province, China | Area under curve = 0.86 |
[91] | B | Assessing forest fire susceptibility | Boosted Regression Tree, General Linear Model, and Mixture Discriminant Analysis | Fars Province, Iran | |
[92] | B | Predicting occurrence of forest fires | Boosted Decision Trees, Decision Forest Classifier, Decision Jungle Classifier, Averaged Perceptron, 2-Class Bayes Point Machine, Local Deep Support Vector Machine, Logistic Regression, and Binary Neural Network | Area under the curve = 0.78 | |
[93] | B | Analysing and predicting spatial patterns of forest fire danger | Multivariate Adaptive Regression Splines Optimized by Differential Flower Pollination | Lao Cai province (Vietnam) | Area under the curve = 0.91 |
[94] | B | Providing details on specific techniques being explored for performing low-cost, high fidelity fire predictions | Deep Neural Networks | ||
[60] | B | Developing methods for properly calibrating statistical and machine learning models for fine-scale, spatially explicit daily predictions | Classification Trees, Random Forests, Neural Networks, Logistic Regression Models, and Logistic Generalized Additive Models | Lac La Biche region of Alberta, Canada | |
[95] | B | Estimating the probability of fire occurrence | Random Forest | Colombian–Venezuelan plains (llanos) ecoregion in South America. | Accuracy of 94.0% |
[97] | B | Studying the impact of weather on the damage caused by fire incidents | Gradient Boosting Tree | United States | R-2 value of 0.933 and mean squared error (MSE) of 124.641 out of 10,000 |
[98] | B | Prediction of African fire one month in advance and generalizing to provide seasonal estimates of regional and global fire risk | Stepwise Generalized Equilibrium Feedback Assessment | Africa | |
[101] | D | Developing susceptibility maps considering the intersection of landslide and wildfire susceptibility and the spatial uncertainty | Random Forest, Gradient Boosting Decision Tree, and Adaptive Boosting | ||
[104] | E | Creating wildfire inventory data by integrating the polygons collected through field surveys using global positioning systems (GPS) and the data collected from the moderate resolution imaging spectrometer (MODIS) thermal anomalies product | Artificial Neural Network, Dmine Regression, DM Neural, Least Angle Regression, Multi-Layer Perceptron, Random Forest, Radial Basis Function, Self-Organizing Maps, Support Vector Machine, and Decision Tree | ||
[105] | E | Developing an automatized and cloud-based workflow for generating a training dataset of fire events at a continental level using freely available remote sensing data | Random Forest, Naive Bayes, and Classification and Regression Tree. | ||
[106] | E | Creating a wildfire data inventory by integrating global positioning system (GPS) polygons with data collected from the moderate resolution imaging spectroradiometer (MODIS) thermal anomalies product | Artificial Neural Network, Support Vector Machines, and Random Forest | Amol County, northern Iran. |
Ref. | Classification 1 | Detailed Application | Machine Learning Technique | Case Study Location | Model Performance Metrics Results |
---|---|---|---|---|---|
[110] | A | Proposing a two-module video smoke detection framework designed for embedded applications on local cameras | Lightweight Deep Convolutional Neural Network | ||
[111] | A | Proposing an intelligent fire detection method by investigating three approaches to detect fire based on three different colour models | Decision Rule and Gaussian Mixture Model | ||
[112] | A | Proposing a combined 3-step forest fire detection algorithm (i.e., thresholding, machine-learning-based modelling, and post-processing) | Random Forest | South Korea | Overall accuracy similar to 99.2%, probability of detection |
[113] | A | Proposing a multistage fire detection method | Convolutional Neural Networks and Long Short Term Memory Networks | ||
[114] | A | Proposing a multi-level forest fire detection method | General Advanced Networks, Adaptive Boosting, Convolutional Neural Networks, and Support Vector Machine | ||
[116] | A | Proposing a method using machine learning techniques for multimedia surveillance during fire emergencies | Adaptive Boosting and Many Multi-Layer Perceptron Neural Networks | ||
[117] | A | Proposing a fire detection method using sensors and image data | Adaptive Boosting, Multi-Layer Perceptron Neural Networks, and Convolutional Neural Networks | ||
[118] | A | Proposing a data-driven fire-flake simulation model | Neural Network | ||
[119] | A | Exploring the potential use of the PRISMA sensor for active wildfire characterization | Support Vector Machine | New South Wales | |
[108] | A | Proposing a method that widens the view of fire detection from conventional two-class to multi-class classification problems to meet complex forest image background | K-Nearest Neighbour Decision Tree | ||
[120] | A | Exploring and discovering the numerical patterns from the contact to the ignition process between different upper-storey vegetations and the powerlines | Hybrid Step Extreme Gradient Boosting | ||
[61] | A | Developing a workflow process to monitor fires over a large region | Transductive PU Learning | Southeast China | |
[121] | A | Systematically testing and comparing reflectance and fractional cover candidate severity indices | Random Forest | ||
[123] | B | Estimating the fire arrival time from satellite data | Support Vector Machine | California, United States | 12.0% burned area absolute percentage error; 5.0% total burned area mean percentage error, a 0.21 false alarm ratio average, a 0.86 probability of detection average, and a 0.82 Sorensen’s coefficient average |
[124] | B | Estimating the time-resolved spatial evolution of a wildland fire front | Deep Convolutional Inverse Graphics Network | ||
[125] | B | Developing a fire progression model considering the uncertainties | U-Net Convolutional Neural Network | ||
[126] | B | Identifying the main environmental factors driving fire severity in extreme fire events | Random Forest | California, USA | |
[128] | B | Investigating the controls and predictability of final fire size at the time of ignition | Decision Tree | 50.4+/−5.2% accuracy | |
[131] | C | Creating a sound-wave fire-extinguishing system and performing firefighting tests | Artificial Neural Network, K-Nearest Neighbour, Random Forest, Stacking, and Deep Neural Network Methods | ||
[132] | C | Developing a sound-wave flame extinction system in order to extinguish the flames at an early stage of the fire | Adaptive-Network-Based Fuzzy Inference Systems), CN2 Rule and DT (Decision Tree) | ||
[133] | C | Creating an automated system that is capable of real-time, intelligent object detection and recognition and facilitating the improved situational awareness of firefighters during an emergency response | Convolutional Neural Network |
Ref. | Classification 1 | Detailed Application | Machine Learning Technique | Case study Location | Model Performance Metrics Results |
---|---|---|---|---|---|
[137] | A | Incorporating predictors of local meteorology, land-surface characteristics, and socio-economic variables to predict monthly burned area | Shapley Additive Explanation | United States | |
[138] | A | Examining how training data properties affect fire severity classification across forest, woodland, and shrubland communities | Southern Australia | ||
[139] | A | Determining the main environmental variables that control fire severity in large fires | Random Forest | Iberian Peninsula | |
[140] | A | Predicting the burned area of forest fires and the occurrence of large-scale forest fires | Portugal | ||
[141] | A | Topological data analysis to assess the final burned area | United States | ||
[142] | A | Mapping wildland fires | Support Vector Regression and the Adaptive Neuro-Fuzzy Inference System | Jerash Province, Jordan | |
[143] | A | Determining relationships existing between the triggering of landslides and burnt areas through processing of the thematic maps of the burnt areas and landslide susceptibility assessment | |||
[136] | A | Developing models for automatically mapping burned areas from unitemporal multispectral imagery | |||
[144] | A | Assessing burn severity across the burn scars and testing the effectiveness of several remote sensing methods for generating accurate map products | Random Forest and Support Vector Machine | Interior of Alaska | |
[145] | A | Mapping of burned areas using microwave data | Random Forests | ||
[146] | A | Identifying burned areas and estimating the fire history | Random Forest | North Carolina, United States | |
[147] | A | Using automatic algorithm approach to map burned areas from remote sensing | |||
[148] | A | Examining the use of sUAS imagery to train and validate burn severity and extent mapping of large wildland fires from various satellite images | |||
[149] | A | Calculating tree mortality through the comparison of hyperspatial post-fire canopy cover and pre-fire canopy cover | Mask Region-Based Convolutional Neural Network | ||
[150] | A | Determining trees and burned pixels in a post-fire forest | Mask Region-Based Convolutional Neural Network and Support Vector Machine | ||
[151] | A | Analysing bi-temporal (pre- and post-fire) reflectance contrast of burn-sensitive spectral bands | Classification Regression Tree, Random Forest, and Support Vector Machine | ||
[155] | A | Mapping burned and unburned areas, differentiating fire occurrence dates, and distinguishing between old and more recent fires | K-Nearest Neighbours Algorithm (K-NN), Support Vector Machine (SVM) And Random Forest | Mediterranean area | |
[156] | A | Investigates the use capability of the free synthetic aperture radar data for burned area mapping | Portugal and Italy | ||
[157] | A | Test the applicability of a normalized difference spectral index with the shortwave infrared and blue spectral bands in accurately mapping burned areas | Random Forest | ||
[158] | A | Estimating burned areas in forest fires | Artificial Neural Network | ||
[159] | A | Examine the fine-scale association between burn severity and a suite of environmental drivers | Random Forest | California, United States | Accuracy of 79.0% in classifying categories of burn severity |
[160] | B | Evaluate public health impacts of wildfire smoke | Ordinary Multi-Linear Regression Method, Generalized Boosting Method, and Random Forest | United States | |
[161] | B | Prediction models for ground-level ozone during wildfires, evaluating the predictive accuracy | California, United States | ||
[162] | B | Explored different combinations of biophysical and social factors to characterize wildfire-affected areas | Classification Trees | Portugal | |
[163] | C | The carbon flux of the woodland was monitored to simulate daily net ecosystem production, ecosystem respiration, and gross primary production | |||
[164] | C | Calculating emissions associated with forest fires in Mexico, based on different satellite observation products | Random Forest | Mexico and United States | |
[135] | D | Proposes a unitemporal simulation approach based on the generation of synthetic spectral databases from linear spectral mixing to classify wildfire severity | Random Forest | Spain | Accuracy between 90.0–95.0% |
[165] | D | Prediction of post-fire tree mortality | Random Forest | Reduced the bias in comparison with logistic regression method | |
[166] | D | Focus on the post-fire debris flow hazards analysis | Decision Tree Algorithm | Sensitivity of 81.0% and specificity of 78.0% | |
[167] | D | Map forest disturbance after a wildfire | Multiple Linear Regression, Support Vector Machine, and Random Forest | Northeastern China | |
[168] | D | Identify temporal trends in post-fire regeneration and influences of climate on post-fire regeneration, with focus on post-fire establishment, initial post-fire density and radial growth | |||
[169] | D | Assessed the contributions of land cover composition, climate, and topography on the spatial forest regeneration | Boosted Regression Trees | New Mexico, United States | |
[170] | D | Evaluate the potential of a radiative transfer model inversion approach for estimating fractional vegetation cover after wildfire from satellite reflectance data at high spatial resolution | Gaussian Processes Regression | Mediterranean Basin | |
[171] | D | Assessed whether passive restoration of old trees could overcome constraints in time | Random Forest | Colorado, United States |
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Bot, K.; Borges, J.G. A Systematic Review of Applications of Machine Learning Techniques for Wildfire Management Decision Support. Inventions 2022, 7, 15. https://doi.org/10.3390/inventions7010015
Bot K, Borges JG. A Systematic Review of Applications of Machine Learning Techniques for Wildfire Management Decision Support. Inventions. 2022; 7(1):15. https://doi.org/10.3390/inventions7010015
Chicago/Turabian StyleBot, Karol, and José G. Borges. 2022. "A Systematic Review of Applications of Machine Learning Techniques for Wildfire Management Decision Support" Inventions 7, no. 1: 15. https://doi.org/10.3390/inventions7010015