Modelling the spatial variability of wildfire susceptibility in Honduras using remote sensing and geographical information systems

Valdez, Miguel; Chang, Kai H.; Chen, Chi Farn; Chiang, Shou Hao; Santos, Jorge Luis

doi:10.1080/19475705.2016.1278404

Cited by 54 publications

(26 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the outputs of decision tree assessments, the majority vote converges on a single decision tree for the final classification [44]. In order to overcome the uncertainty problem, a single decision tree can be used, and this will result in higher prediction accuracy [45]. The crucial step in the RF classification is to derive high variance from different decision trees.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping

et al. 2020

View full text Add to dashboard Cite

Climate change has increased the probability of the occurrence of catastrophes like wildfires, floods, and storms across the globe in recent years. Weather conditions continue to grow more extreme, and wildfires are occurring quite frequently and are spreading with greater intensity. Wildfires ravage forest areas, as recently seen in the Amazon, the United States, and more recently in Australia. The availability of remotely sensed data has vastly improved, and enables us to precisely locate wildfires for monitoring purposes. Wildfire inventory data was created 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 between 2012 and 2017 for the study area. The inventory data, along with sixteen conditioning factors selected for the study area, was used to appraise the potential of various machine learning (ML) methods for wildfire susceptibility mapping in Amol County. The ML methods chosen for this study are artificial neural network (ANN), dmine regression (DR), DM neural, least angle regression (LARS), multi-layer perceptron (MLP), random forest (RF), radial basis function (RBF), self-organizing maps (SOM), support vector machine (SVM), and decision tree (DT), along with the statistical approach of logistic regression (LR), which is very apt for wildfire susceptibility studies. The wildfire inventory data was categorized as three-fold, with 66% being used for training the models and 33% being used for accuracy assessment within three-fold cross-validation (CV). Receiver operating characteristics (ROC) was used to assess the accuracy of the ML approaches. RF had the highest accuracy of 88%, followed by SVM with an accuracy of almost 79%, and LR had the lowest accuracy of 65%. This shows that RF is better suited for wildfire susceptibility assessments in our case study area.

show abstract

Section: Random Forest (Rf)mentioning

confidence: 99%

Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The (AUROC) curve technique was selected for this comparison. It is commonly used to characterize the quality of the resulting susceptibility-prediction maps, particularly in the geosciences [107,108]. The plotted curves display the trade-offs between the false positive rate (FPR) and the true positive rate (TPR), on the X and Y axis, respectively.…”

Section: Accuracy Assessmentsmentioning

confidence: 99%

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

et al. 2019

View full text Add to dashboard Cite

Although snow avalanches are among the most destructive natural disasters, and result in losses of life and economic damages in mountainous regions, far too little attention has been paid to the prediction of the snow avalanche hazard using advanced machine learning (ML) models. In this study, the applicability and efficiency of four ML models: support vector machine (SVM), random forest (RF), naïve Bayes (NB) and generalized additive model (GAM), for snow avalanche hazard mapping, were evaluated. Fourteen geomorphometric, topographic and hydrologic factors were selected as predictor variables in the modeling. This study was conducted in the Darvan and Zarrinehroud watersheds of Iran. The goodness-of-fit and predictive performance of the models was evaluated using two statistical measures: the area under the receiver operating characteristic curve (AUROC) and the true skill statistic (TSS). Finally, an ensemble model was developed based upon the results of the individual models. Results show that, among individual models, RF was best, performing well in both the Darvan (AUROC = 0.964, TSS = 0.862) and Zarrinehroud (AUROC = 0.956, TSS = 0.881) watersheds. The accuracy of the ensemble model was slightly better than all individual models for generating the snow avalanche hazard map, as validation analyses showed an AUROC = 0.966 and a TSS = 0.865 in the Darvan watershed, and an AUROC value of 0.958 and a TSS value of 0.877 for the Zarrinehroud watershed. The results indicate that slope length, lithology and relative slope position (RSP) are the most important factors controlling snow avalanche distribution. The methodology developed in this study can improve risk-based decision making, increases the credibility and reliability of snow avalanche hazard predictions and can provide critical information for hazard managers.

show abstract

“…Research projects and works on wildfire hazard and risk mapping are numerous and diverse; complete compilations on such studies can be found in [18][19][20]. Regarding the scale, these studies include long-term, short-term and real-time approaches, from the time scale point of view, while global, regional, and local scales may be used as spatial scales.…”

Section: Introductionmentioning

confidence: 99%

Mapping Forest Fire Risk at a Local Scale—A Case Study in Andalusia (Spain)

2019

View full text Add to dashboard Cite

Forest fires are a critical environmental problem facing current societies, with serious repercussions at ecological, economic and personal safety levels. Detailed maps enabling identification of areas liable to be affected is an indispensable first step allowing different prevention and protection measures vis-à-vis this kind of phenomenon. These maps could be especially valuable for use in land management and emergency planning at a municipality scale. A methodology is shown for producing local maps of mid- and short-term forest fire risk, integrating both natural and human factors. Among natural factors, variables normally used in hazard models are considered as fuel models, slopes or vegetation moisture stress. From the human perspective, more novel aspects have been evaluated, meant either to assess human-induced hazard (closeness to forestland of causative elements or the ability of people to penetrate the forest environment), or to assess vulnerability, considering the population’s location in urban centres and scattered settlements. The methodology is applied in a municipality of Andalusia (Spain) and obtained results were compared to burned areas maps.

show abstract

Modelling the spatial variability of wildfire susceptibility in Honduras using remote sensing and geographical information systems

Cited by 54 publications

References 53 publications

Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping

Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

Mapping Forest Fire Risk at a Local Scale—A Case Study in Andalusia (Spain)

Contact Info

Product

Resources

About