Wildfire Susceptibility Maps Flexible Querying and Answering

2013 ◽  
pp. 352-363 ◽  
Author(s):  
Paolo Arcaini ◽  
Gloria Bordogna ◽  
Simone Sterlacchini
Keyword(s):  
Susceptibility Maps ◽  
Wildfire Susceptibility ◽  
Flexible Querying

Wildfire susceptibility mapping via machine learning: the case study of Liguria Region, Italy

2020 ◽  
Author(s):  
Paolo Fiorucci ◽  
Mirko D'Andrea ◽  
Andrea Trucchia ◽  
Marj Tonini
Keyword(s):  
Machine Learning ◽  
Natural Hazards ◽  
Predisposing Factors ◽  
Susceptibility Mapping ◽  
Burned Area ◽  
Susceptibility Map ◽  
Susceptibility Maps ◽  
Vegetation Heterogeneity ◽  
Wildfire Susceptibility

<p>Risk and susceptibility analyses for  natural hazards are of great importance for the sake of  civil protection, land use planning  and risk reduction programs. Susceptibility maps are based on the assumption that future events are expected to occur under similar conditions as the observed ones. Each unit area is assessed in term of relative spatial likelihood, evaluating the potential to experience a particular hazard in the future based solely on the intrinsic local characteristics. These concept is well-consolidated in the research area related with the risk assessment, especially for landslides. Nevertheless, the need exist for developing new quantitative and robust methods allowing to elaborate susceptibility  maps and to apply this tool to the study of other natural hazards.  In  the presented work, such  task is pursued for the specific  case of wildfires in Italy. The  two main approaches for such studies are the adoption  of physically based models and the data driven methods. In  the presented work, the latter  approach is  pursued, using  Machine Learning techniques in order to learn  from and make prediction  on the available information (i.e. the observed burned area and the predisposing factors) . Italy is severely affected by wildfires due to the high topographic and vegetation heterogeneity of its territory  and  to  its   meteorological conditions. The present study has as its main objective the  elaboration of a wildfire susceptibility map for Liguria region (Italy) by making use of Random Forest, an ensemble ML algorithm based on decision trees. The quantitative evaluation of susceptibility is carried out considering two different aspects: the location of past  wildfire occurrences, in terms of burned area, and the related anthropogenic and geo-environmental  predisposing factors that may favor fire spread. Different implementation of the model  were performed and compared. In  particular,  the effect of  a pixel's  neighboring land cover (including the type of vegetation and no-burnable area) on the output susceptibility map is investigated. In order to assess the  performance  of the model, the spatial-cross validation has been carried  out, trying  out different  number of folders. Susceptibility maps for the two fire seasons (the  summer  and  the winter  one) were finally computed  and validated. The  resulting  maps show  higher susceptibility zones , developing closer to the coast in summer and along the interior part of  the region in winter. Such zones matched well with the testing burned area, thus  proving the  overall  good performance of the proposed method.</p><p><strong>REFERENCE</strong></p><p> Tonini M., D’Andrea M., Biondi G., Degli Esposti S.; Fiorucci P., A machine learning based approach for wildfire susceptibility mapping. The case study of Liguria region in Italy. <em>Geosciences</em> (2020, submitted)</p><p><br><br></p>

scholarly journals A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy

Geosciences ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 105 ◽  
Author(s):  
Marj Tonini ◽  
Mirko D’Andrea ◽  
Guido Biondi ◽  
Silvia Degli Esposti ◽  
Andrea Trucchia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Expert Knowledge ◽  
Vegetation Type ◽  
Climatic Conditions ◽  
Predisposing Factors ◽  
Machine Learning Algorithms ◽  
Susceptibility Map ◽  
Susceptibility Maps ◽  
A Site ◽  
Wildfire Susceptibility

Wildfire susceptibility maps display the spatial probability of an area to burn in the future, based solely on the intrinsic local proprieties of a site. Current studies in this field often rely on statistical models, often improved by expert knowledge for data retrieving and processing. In the last few years, machine learning algorithms have proven to be successful in this domain, thanks to their capability of learning from data through the modeling of hidden relationships. In the present study, authors introduce an approach based on random forests, allowing elaborating a wildfire susceptibility map for the Liguria region in Italy. This region is highly affected by wildfires due to the dense and heterogeneous vegetation, with more than 70% of its surface covered by forests, and due to the favorable climatic conditions. Susceptibility was assessed by considering the dataset of the mapped fire perimeters, spanning a 21-year period (1997–2017) and different geo-environmental predisposing factors (i.e., land cover, vegetation type, road network, altitude, and derivatives). One main objective was to compare different models in order to evaluate the effect of: (i) including or excluding the neighboring vegetation type as additional predisposing factors and (ii) using an increasing number of folds in the spatial-cross validation procedure. Susceptibility maps for the two fire seasons were finally elaborated and validated. Results highlighted the capacity of the proposed approach to identify areas that could be affected by wildfires in the near future, as well as its goodness in assessing the efficiency of fire-fighting activities.

scholarly journals Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods

2021 ◽  
Vol 13 (8) ◽  
pp. 1572
Author(s):  
Qian He ◽  
Ziyu Jiang ◽  
Ming Wang ◽  
Kai Liu
Keyword(s):  
Machine Learning ◽  
Southeast Asia ◽  
Machine Learning Algorithms ◽  
Receiver Operating Curve ◽  
Susceptibility Assessment ◽  
Adaptive Boosting ◽  
Susceptibility Modeling ◽  
Ensemble Machine Learning ◽  
Susceptibility Maps ◽  
Wildfire Susceptibility

Southeast Asia (SEA) is a region affected by landslide and wildfire; however, few studies on susceptibility modeling for the two hazards together have been conducted for this region, and the intersection and the uncertainty of the two hazards are rarely assessed. Thus, the intersection of landslide and wildfire susceptibility and the spatial uncertainty of the susceptibility maps were studied in this paper. Reliable landslide and wildfire susceptibility maps are necessary for disaster management and land use planning. This work used three advanced ensemble machine learning algorithms: RF (Random Forest), GBDT (Gradient Boosting Decision Tree) and AdaBoost (Adaptive Boosting) to assess the landslide and wildfire susceptibility for SEA. A geo-database was established with 2759 landslide locations, 1633 wildfire locations and 18 predictor variables in total. The performances of the models were assessed using the overall classification accuracy (ACC), Precision, the area under the ROC (receiver operating curve) (AUC) and confusion matrix values. The results showed RF performs superior in both landslide (ACC = 0.81, Precision = 0.78 and AUC= 0.89) and wildfire (ACC= 0.83, Precision = 0.83 and AUC = 0.91) susceptibility modeling, followed by GBDT and AdaBoost. The overall superiority of RF over other models indicates that it is potentially an efficient model for landslide and wildfire susceptibility mapping. The landslide and wildfire susceptibility were obtained using the RF model. This paper also conducted an overlay analysis of the two hazards. The uncertainty of the susceptibility was further assessed using the coefficient of variation (CV). Additionally, the distance to roads is relatively important in both landslide and wildfire susceptibility, which is the most important in landslides and the second most important in wildfires. The result of this paper is useful for mastering the whole situation of hazard susceptibility and proves that RF is a robust model in the hazard susceptibility assessment in SEA.

scholarly journals A Machine Learning based Approach for Wildfire Susceptibility Mapping. The Case Study of Liguria Region in Italy

2020 ◽  
Author(s):  
Marj Tonini ◽  
Mirko D'Andrea ◽  
Guido Biondi ◽  
Silvia Degli Esposti ◽  
Andrea Trucchia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Expert Knowledge ◽  
Meteorological Conditions ◽  
Susceptibility Map ◽  
Occurrence Probability ◽  
The Past ◽  
Susceptibility Maps ◽  
Wildfire Susceptibility

Wildfire susceptibility maps display the wildfires occurrence probability, ranked from low to high, under a given environmental context. Current studies in this field often rely on expert knowledge, including or not statistical models allowing to assess the cause-effect correlation. Machine learning (ML) algorithms can perform very well and be more generalizable thanks to their capability of learning from and make predictions on data. Italy is highly affected by wildfires due to the high heterogeneity of the territory and to the predisposing meteorological conditions. The main objective of the present study is to elaborate a wildfire susceptibility map for Liguria region (Italy) by applying Random Forest, an ensemble ML algorithm based on decision trees. Susceptibility was assessed by evaluating the probability for an area to burn in the future considering where wildfires occurred in the past and which are the geo-environmental factors that favor their spread. Different models were compared, including or not the neighboring vegetation and using an increasing number of folds for the spatial-cross validation. Susceptibility maps for the two fire seasons were finally elaborated and validated and results critically discussed highlighting the capacity of the proposed approach to identify the efficiency of fire fighting activities.

Preliminary soil-slip susceptibility maps, southwestern California

2003 ◽  
Author(s):  
Douglas M. Morton ◽  
Rachel M. Alvarez ◽  
Russell H. Campbell ◽  
Kelly R. Digital preparation by Bovard ◽  
D.T. Brown ◽  
...  
Keyword(s):  
Soil Slip ◽  
Susceptibility Maps

DEVELOPMENT OF LANDSLIDE SUSCEPTIBILITY MAPS IN COLORADO

2016 ◽  
Author(s):  
Kassandra Lindsey ◽  
◽  
Matthew L. Morgan ◽  
Karen A. Berry
Keyword(s):  
Landslide Susceptibility ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

scholarly journals InSAR Monitoring of Landslide Activity in Dominica

2021 ◽  
Vol 13 (4) ◽  
pp. 815
Author(s):  
Mary-Anne Fobert ◽  
Vern Singhroy ◽  
John G. Spray
Keyword(s):  
Risk Mitigation ◽  
Disaster Mitigation ◽  
Aerial Images ◽  
Landslide Inventory ◽  
Landslide Risk ◽  
Susceptibility Map ◽  
Interferometric Synthetic Aperture Radar ◽  
Rainfall Events ◽  
Susceptibility Maps ◽  
Digital Elevation

Dominica is a geologically young, volcanic island in the eastern Caribbean. Due to its rugged terrain, substantial rainfall, and distinct soil characteristics, it is highly vulnerable to landslides. The dominant triggers of these landslides are hurricanes, tropical storms, and heavy prolonged rainfall events. These events frequently lead to loss of life and the need for a growing portion of the island’s annual budget to cover the considerable cost of reconstruction and recovery. For disaster risk mitigation and landslide risk assessment, landslide inventory and susceptibility maps are essential. Landslide inventory maps record existing landslides and include details on their type, location, spatial extent, and time of occurrence. These data are integrated (when possible) with the landslide trigger and pre-failure slope conditions to generate or validate a susceptibility map. The susceptibility map is used to identify the level of potential landslide risk (low, moderate, or high). In Dominica, these maps are produced using optical satellite and aerial images, digital elevation models, and historic landslide inventory data. This study illustrates the benefits of using satellite Interferometric Synthetic Aperture Radar (InSAR) to refine these maps. Our study shows that when using continuous high-resolution InSAR data, active slopes can be identified and monitored. This information can be used to highlight areas most at risk (for use in validating and updating the susceptibility map), and can constrain the time of occurrence of when the landslide was initiated (for use in landslide inventory mapping). Our study shows that InSAR can be used to assist in the investigation of pre-failure slope conditions. For instance, our initial findings suggest there is more land motion prior to failure on clay soils with gentler slopes than on those with steeper slopes. A greater understanding of pre-failure slope conditions will support the generation of a more dependable susceptibility map. Our study also discusses the integration of InSAR deformation-rate maps and time-series analysis with rainfall data in support of the development of rainfall thresholds for different terrains. The information provided by InSAR can enhance inventory and susceptibility mapping, which will better assist with the island’s current disaster mitigation and resiliency efforts.

Liquefaction susceptibility maps for the Aqaba–Elat region with projections of future hazards with sea-level rise

2020 ◽  
pp. qjegh2020-039 ◽  
Author(s):  
Abdel-Rahman A. Abueladas ◽  
Tina M. Niemi ◽  
Abdallah Al-Zoubi ◽  
Gideon Tibor ◽  
Mor Kanari ◽  
...  
Keyword(s):  
Sea Level ◽  
Water Table ◽  
Active Faults ◽  
Gulf Of Aqaba ◽  
Susceptibility Map ◽  
Northern Coast ◽  
Borehole Data ◽  
Ground Acceleration ◽  
Liquefaction Susceptibility ◽  
Susceptibility Maps

The cities of Aqaba, Jordan and Elat, Israel are vulnerable to seismic damage because they are built over the active faults of the Dead Sea Transform that are the source of historically destructive earthquakes. A liquefaction susceptibility map was generated for the Aqaba–Elat region. Borehole data from 149 locations and the water table depth were used to calculate effective overburden stress in the Seed–Idriss simplified method. The liquefaction analysis was based on applying a cyclic loading scenario with horizontal peak ground acceleration of 0.3 g in a major earthquake. The liquefaction map, compiled using a GIS platform, shows high and moderate liquefaction susceptibility zones along the northern coast of the Gulf of Aqaba that extend 800 m inland from the shoreline. In Aqaba, several hotels, luxury apartment complexes, archaeological sites, ports and commercial districts are located within high and moderate liquefaction zones. In Elat, the seaport and the coastal hotel district are located within a high susceptibility zone. Most residential areas, schools and hospitals in both cities are located within zones not susceptible to liquefaction based on the methods of this study. The total area with the potential to be liquefied along the Gulf of Aqaba is c. 10 km2. Given predictions for global sea-level, we ran three liquefaction models utilizing projected water table rises of 0.5, 1 and 2 m. These models yielded an increase in the area of high liquefaction ranging from 26 to 49%. Given the high potential of future earthquakes, our liquefaction susceptibility maps should help inform city officials for hazard mitigation planning.

Sign in / Sign up

Export Citation Format

Share Document