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.

The effects of the spatial resolution of remote sensing (RS) data on wildfire susceptibility prediction are not fully understood. In this study, we evaluate the effects of coarse (Landsat 8 and SRTM) and medium (Sentinel-2 and ALOS) spatial resolution data on wildfire susceptibility prediction using random forest (RF) and support vector machine (SVM) models. In addition, we investigate the fusion of the predictions from the different spatial resolutions using the Dempster–Shafer theory (DST) and 14 wildfire conditioning factors. Seven factors are derived separately from the coarse and medium spatial resolution datasets for the whole forest area of the Guilan Province, Iran. All conditional factors are used to train and test the SVM and RF models in the Google Earth Engine (GEE) software environment, along with an inventory dataset from comprehensive global positioning system (GPS)-based field survey points of wildfire locations. These locations are evaluated and combined with coarse resolution satellite data, namely the thermal anomalies product of the moderate resolution imaging spectroradiometer (MODIS) for the period 2009 to 2019. We assess the performance of the models using four-fold cross-validation by the receiver operating characteristic (ROC) curve method. The area under the curve (AUC) achieved from the ROC curve yields 92.15% and 91.98% accuracy for the respective SVM and RF models for the coarse RS data. In comparison, the AUC for the medium RS data is 92.5% and 93.37%, respectively. Remarkably, the highest AUC value of 94.71% is achieved for the RF model where coarse and medium resolution datasets are combined through DST.

We live in a sphere that has unpredictable and multifaceted landscapes that make the risk arising from several incidences that are omnipresent. Floods and landslides are widespread and recurring hazards occurring at an alarming rate in recent years. The importance of this study is to produce multi-hazard exposure maps for flooding and landslides for the federal State of Salzburg, Austria, using the selected machine learning (ML) approach of support vector machine (SVM) and random forest (RF). Multi-hazard exposure maps were established on thirteen influencing factors for flood and landslides such as elevation, slope, aspect, topographic wetness index (TWI), stream power index (SPI), normalized difference vegetation index (NDVI), geology, lithology, rainfall, land cover, distance to roads, distance to faults, and distance to drainage. We classified the inventory data for flood and landslide into training and validation with the widely used splitting ratio, where 70% of the locations are used for training, and 30% are used for validation. The accuracy assessment of the exposure maps was derived through ROC (receiver operating curve) and R-Index (relative density). RF yielded better results for both flood and landslide exposure with 0.87 for flood and 0.90 for landslides compared to 0.87 for flood and 0.89 for landslides using SVM. However, the multi-hazard exposure map for the State of Salzburg derived through RF and SVM provides the planners and managers to plan better for risk regions affected by both floods and landslides.

Landslide susceptibility mapping (LSM) can serve as a basis for analyzing and assessing the degree of landslide susceptibility in a region. This study uses the object-based geons aggregation model to map landslide susceptibility for all of Austria and evaluates whether an additional implementation of the Dempster–Shafer theory (DST) could improve the results. For the whole of Austria, we used nine conditioning factors: elevation, slope, aspect, land cover, rainfall, distance to drainage, distance to faults, distance to roads, and lithology, and assessed the performance and accuracy of the model using the area under the curve (AUC) for the receiver operating characteristics (ROC). We used three scale parameters for the geons model to evaluate the impact of the scale parameter on the performance of LSM. The results were similar for the three scale parameters. Applying the Dempster–Shafer theory could significantly improve the results of the object-based geons model. The accuracy of the DST-derived LSM for Austria improved and the respective AUC value increased from 0.84 to 0.93. The resulting LSMs from the geons model provide meaningful units independent of administrative boundaries, which can be beneficial to planners and policymakers.