Abstract We developed a model integrating 28 criteria spanning social, economic, community, environmental, and physical dimensions to evaluate earthquake resilience of Istanbul, a city with a population of 16 million and significant seismic risk, at both district and subdistrict/neighborhood levels. The resilience assessment uses the Bayesian Best-Worst Method, a multi-criteria decision-making framework that combines expert knowledge and statistical assessments. The results reveal that Istanbul’s overall Resilience Score (RS) is 0.48, on a 0-1 scale, suggesting a moderate capacity to endure and recover from seismic events. Catalca, Adalar, and Arnavutkoy rank among the most resilient districts, whereas Esenler and Gungoren exhibit lower resilience. On a subdistrict level, Suleymaniye (Fatih) has the highest RS at 0.59, while Yavuz Sultan Selim (Fatih) ranks the lowest with 0.22. These findings provide actionable and practical data-driven insights for policymakers and urban planners, underscoring the need for targeted interventions to improve resilience in high-risk areas in Istanbul.

The increasing risk of earthquakes in urban areas has made it crucial to develop accurate vulnerability models for city infrastructure and systems. We aimed to assess and compare the effectiveness of different models and vulnerability analysis techniques in predicting earthquake vulnerability in the specific context of Izmir, Turkey. One central hypothesis in this research aimed to determine whether integrating Eigenvector Spatial Filtering (ESF) into both regression models and machine learning algorithms would yield a comparable enhancement in model performance. We performed earthquake vulnerability modeling (EVM) by considering (ⅰ) only seismic-related variables (SRV) and (ⅱ) integrating ESF by using Moran's eigenvector maps (MEMs). For each approach, we evaluated the predictive performance of two simple regression-based models; generalized linear model (GLM) and generalized additive model (GAM), and two complex machine learning ones; generalized boosting model (GBM), and random forest (RF). The study utilized five primary indicators encompassing geotechnical, physical, structural, social, and facilities data. The predictive performance of the models was assessed using evaluation metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and adjusted R2. The results indicated that the optimal candidate model consisted of five key variables: altitude, building height, distance to safety gathering places, Peak Ground Acceleration (PGA), and population density. We found that decision-tree-based methods performed better than regression-based methods for both modeling schemes. RF exhibited the highest predictive performance for the training data (RMSE = 0.59, adjusted R2 = 0.71), while GBM outperformed other models for the test data (RMSE = 0.79, adjusted R2 = 0.78). However, incorporating ESF to the EVM analysis revealed that regression-based methods, particularly the GLM, obtained highest improvement in accuracy (RMSE 0.94 vs 0.76 and adjusted R2 0.56 vs 0.71 for the SRV and SRV + MEMs modeling approach). Significant differences were observed between GLM-GBM and GLM-RF comparisons, as well as GAM-GBM and GAM-RF comparisons. The findings of this research are expected to be helpful for informed decision-making, targeted risk reduction, and the development of effective policies and strategies to enhance preparedness and resilience in the face of seismic events in highly susceptible urban systems.

Brassica napus, a versatile crop with significant socioeconomic importance, serves as a valuable source of nutrition for humans and animals while also being utilized in biodiesel production. The expansion potential of B. napus is profoundly influenced by climatic variations, yet there remains a scarcity of studies investigating the correlation between climatic factors and its distribution. This research employs CLIMEX to identify the current and future ecological niches of B. napus under the RCP 8.5 emission scenario, utilizing the Access 1.0 and CNRM-CM5 models for the time frame of 2040-2059. Additionally, a sensitivity analysis of parameters was conducted to determine the primary climatic factors affecting B. napus distribution and model responsiveness. The simulated outcomes demonstrate a satisfactory alignment with the known current distribution of B. napus, with 98% of occurrence records classified as having medium to high climatic suitability. However, the species displays high sensitivity to thermal parameters, thereby suggesting that temperature increases could trigger shifts in suitable and unsuitable areas for B. napus, impacting regions such as Canada, China, Brazil, and the United States.

Aim: Generate fire susceptibility maps for the present and 2070, to identify the threat wildfires pose to koalas now and under future climate change. Location: Australia. Time period: Present and 2070. Major taxa studied: 60 main tree species browsed by koalas. Method: The Decision Tree machine learning algorithm was applied to generate a fire susceptibility index (a measure of the potential for a given area or region to experience wildfires) using a dataset of conditioning factors, namely: altitude, aspect, rainfall, distance from rivers, distance from roads, forest type, geology, koala presence and future dietary sources, land use-land cover (LULC), normalized difference vegetation index (NDVI), slope, soil, temperature, and wind speed. Results: We found a general increase in susceptibility of Australian vegetation to bushfires overall. The simulation for current conditions indicated that 39.56% of total koala habitat has a fire susceptibility rating of "very high"or "high", increasing to 44.61% by 2070. Main conclusions: Wildfires will increasingly impact koala populations in the future. If this iconic and vulnerable marsupial is to be protected, conservation strategies need to be adapted to deal with this threat. It is crucial to strike a balance between ensuring that koala habitats and populations are not completely destroyed by fire while also allowing for forest rejuvenation and regeneration through periodic burns.& COPY; 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Although it is hard to predict wildfires, risky areas can be systematically assessed and managed. Some of the factors for decision-making are hazard, vulnerability, and risk maps, which are the end product of wildfire mapping. This study deals with wildfire risk analysis in Queensland, Australia. A review of the previous studies focusing on each aspect has been done and used with wildfire records from 2011 to 2019 in Queensland, Australia, to compile the required input models to detect risky wildfire regions. Machine learning (ML) methods of Decision Tree (DT) and Support Vector Machine (SVM) were used to perform hazard assessment. The reason was to select the most accurate outcomes for the rest of the analysis. Among accuracy assessment techniques, the Area Under Curvature (AUC) method was used to evaluate the hazard maps. Prediction rates of 89.21% and 83.78% were obtained for DT and SVM, respectively. The DT prediction value showed that the DT-hazard map was more accurate than the SVM-hazard map. Vulnerability analysis was implemented by assigning weights to each factor according to the literature. Lastly, in order to create the wildfire risk map, the hazard and vulnerability indices were combined. The risk map showed that particularly dense urbanization regions are under future wildfire risk. To perform preliminary land use planning, this output can be used by local governmental authorities.

<p>On February 6th, 2023, a devastating earthquake with a magnitude of Mw7.7 occurred in the Kahramanmaras region of Türkiye. The earthquake is caused by the rupture of a NE-SW oriented left lateral strike-slip Pazarcık fault segment located between the East Anatolian Fault (EAF) and Dead Sea Fault (DSF) fault systems. The aftershock sequence of the earthquake indicated that post-seismic deformation continued along the EAF and DSF toward the NE and SW. Just 9 hours later, another earthquake with a magnitude of Mw7.6 occurred along the EW-oriented left lateral Sürgü Fault, located approximately 100 km north of the first event. These two earthquakes released a significant amount of energy and affected ten provinces in southeastern Türkiye. The earthquake region is characterized by a complex tectonic structure actively deforming through a network of strike-slip, thrust, and normal faults formed by the convergence of the Arabian Plate to the Eurasian Plate and the westward movement of the Anatolian Plate. It is of utmost importance to understand the co-seismic and post-seismic surface deformation behavior to make reliable seismic hazard assessments.</p> <p>To better understand the deformation patterns during and after the Kahramanmaraş earthquakes, we processed Interferometric Synthetic Aperture Radar (InSAR) data sets obtained before and after the earthquakes. We used both ascending and descending track SAR images of the ESA Sentinel-1 to detect the surface displacement. Then, we incorporated the post-seismic deformation patterns from the relocated aftershock events to the InSAR derived deformation field to gain insight into the source properties of the events. Our preliminary results revealed several meters of displacement across the faults.</p>

The level of destruction caused by an earthquake depends on a variety of factors, such as magnitude, duration, intensity, time of occurrence, and underlying geological features, which may be mitigated and reduced by the level of preparedness of risk management measures. Geospatial technologies offer a means by which earthquake occurrence can be predicted or foreshadowed; managed in terms of levels of preparation related to land use planning; availability of emergency shelters, medical resources, and food supplies; and assessment of damage and remedial priorities. This literature review paper surveys the geospatial technologies employed in earthquake research and disaster management. The objectives of this review paper are to assess: (1) the role of the range of geospatial data types; (2) the application of geospatial technologies to the stages of an earthquake; (3) the geospatial techniques used in earthquake hazard, vulnerability, and risk analysis; and (4) to discuss the role of geospatial techniques in earthquakes and related disasters. The review covers past, current, and potential earthquake-related applications of geospatial technology, together with the challenges that limit the extent of usefulness and effectiveness. While the focus is mainly on geospatial technology applied to earthquake research and management in practice, it also has validity as a framework for natural disaster risk assessments, emergency management, mitigation, and remediation, in general.

The 1500km long, EW directional North Anatolian Fault Zone (NAFZ), and the 700km long, SSW directional East Anatolian Fault Zone (EAFZ) are the major geophysical features in Turkey. They are formed where the Eurasian, Anatolian, and Arabian plates meet. Both faults have produced devastating earthquakes (M>6) throughout history and are still actively deforming and threatening populous areas. Even though individual studies focus on the NAFZ and EAFZ, none of them offer a method for continuous monitoring. In this study, we are taking advantage of the Interferometric Synthetic Aperture Radar (InSAR) method and adopting the Small BAseline Subset (SBAS) time series approach to map deformation over large swaths (hundreds of km). We developed a web-based, automated system that takes publicly available Sentinel-1 SAR images and generates deformation maps. We chose the Elazig region as our pilot study area because of the destructive M6.7 earthquake that occurred in January 2020. Our initial results capture the co-seismic deformation coherently and also provide insights into pre-seismic and post-seismic deformation characteristics. Our goal is to provide the scientific community with accurate and easy-to-interpret deformation maps, without needing advanced remote sensing knowledge.

Landslides are among the most frequent secondary disasters caused by earthquakes in areas prone to seismic activity. Given the necessity of assessing the current seismic conditions for ensuring the safety of life and infrastructure, there is a rising demand worldwide to recognize the extent of landslides and map their susceptibility. This study involved two stages: First, the regions prone to earthquake-induced landslides were detected, and the data were used to train deep learning (DL) models and generate landslide susceptibility maps. The application of DL models was expected to improve the outcomes in both stages. Landslide inventory was extracted from Sentinel-2 data by using U-Net, VGG-16, and VGG-19 algorithms. Because VGG-16 produced the most accurate inventory locations, the corresponding results were used in the landslide susceptibility detection stage. In the second stage, landslide susceptibility maps were generated. From the total measured landslide locations (63,360 cells), 70% of the locations were used for training the DL models (i.e., convolutional neural network [CNN], CNN-imperialist competitive algorithm, and CNN-gray wolf optimizer [GWO]), and the remaining 30% were used for validation. The earthquake-induced landslide conditioning factors included the elevation, slope, plan curvature, valley depth, topographic wetness index, land cover, rainfall, distance to rivers, and distance to roads. The reliability of the generated susceptibility maps was evaluated using the area under the receiver operating characteristic curve (AUROC) and root mean square error (RMSE). The CNN-GWO model (AUROC = 0.84 and RMSE = 0.284) outperformed the other methods and can thus be used in similar applications. The results demonstrated the efficiency of applying DL in the natural hazard domain. The CNN-GWO predicted that approximately 38% of the total area consisted of high and very high susceptibility regions, mainly concentrated in areas with steep slopes and high levels of rainfall and soil wetness. These outcomes contribute to an enhanced understanding of DL application in the natural hazard domain. Moreover, using the knowledge of areas highly susceptible to landslides, officials can actively adopt steps to reduce the potential impact of landslides and ensure the sustainable management of natural resources.

Performing the most up-to-date and accurate vulnerability assessment is key to an effective earthquake disaster management. In cities like Istanbul (Turkey) with a high rate of urban expansion, the safety of the residents must not be neglected. The challenges in such studies are related to the lack of a training dataset. Some areas are highly prone to earthquakes, however, there have been no earthquakes in those areas recently. This research proposes and tests the ability of the k-mean clustering method to create the training dataset for earthquake vulnerability analysis. Subsequently, the derived sample dataset was used in four state-of-the-art models i.e. Decision Tree (DT), Support Vector Machine (SVM), Self-Organizing Map (SOM) and Logistic Regression (LR) for assessing earthquake vulnerability in Istanbul, Turkey. The multicollinearity among the variables was determined using tolerance (TOL) and variance inflation factor (VIF) which revealed no multicollinearity among the variables. The highest VIF belonged to the “distance to faults” factor. Vulnerability related variables were classified, weighed and using k-mean clustering, a training database was constructed. Then, the standardized variables were keyed in as input alongside the training site maps into DT, SVM, SOM and LR to construct an Earthquake Vulnerability Map (EVM). EVMs were created for all the four samples and graded as very-low, relatively-low, moderate, high, or extremely-high. Several statistical metrics such as Area under the ROC curve (AUC), sensitivity (SST), specificity (SPF), root-mean-squared-errors (RMSE), positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the accuracy of the resultant maps. The highest and lowest AUC prediction rates were 0.962 and 0.912 from the K-means-SOM and K-means-LR models, respectively. The lowest RSME results using the testing dataset (0.329) belonged to K-means-SVM model. The region's most prone vulnerability maps were found to be in the districts 9, 13, 20, 21 and 35. Finally, an analysis of the buildings and population distribution was carried out among the 39 districts of Istanbul considering the SOM outcomes. The research outcome could help in laying strategies for earthquake preparedness in the Istanbul city.

Wildfire susceptibility mapping can be affected by several factors. One of the most influential factors is inventory data, its extent, format, and reliability. This study aims to evaluate if the Support Vector Machine (SVM) has the capability to detect and map the forest fire susceptible areas under limited training data conditions. To test this hypothesis wildfires in Mugla province located in the Eastern Mediterranean Region of Turkey have been selected as a pilot study area. The wildfire started in Mugla, on 29 July 2021, that considerably affected the residential areas, animals, and vast areas of forests. Fourteen wildfire influential variables have been used in the analysis as independent variables. Accuracy assessment has been implemented using the Area Under the Curve (AUC) technique. Success rate and prediction rates were (91.42%) and (87.69%) respectively. According to the prediction rate, SVM successfully recognized other burnt areas as the most susceptible regions.

Landslide is one of the natural hazards that considers a serious threat to both humans’ lives and properties. Tasmania, Australia is one of those regions where landslides caused considerable damage to people and the State. Landslide damages can be reduced, even stopped, if proper land condition and planning assessment has been done. The main influencing parameters in landslide occurrences are topography, precipitation, and geological formation. Those parameters along with other influencing parameters have been used in landslide susceptibility mapping. In order to have a reliable analysis, a robust method of Decision Tree (DT) has been used to perform susceptibility mapping. According to the hierarchy structure of DT, geology and slope have been selected as the most influential parameters in landslide susceptibility. In order to evaluate the reliability of the outcomes, Area Under the Curve (AUC) has been utilized. Success and prediction rates were 87.64% and 84% respectively. Subsequently, risky features such as buildings, schools, hotels, etc. have been used in overlay analysis in a GIS environment with “very high” and “high” susceptibility classes. The outcome of this research can assist planning parties to secure vulnerable regions and consider those areas in their future decision-making strategies.

The survival of humanity is dependent on the survival of forests and the ecosystems they support, yet annually wildfires destroy millions of hectares of global forestry. Wildfires take place under specific conditions and in certain regions, which can be studied through appropriate techniques. A variety of statistical modeling methods have been assessed by researchers; however, ensemble modeling of wildfire susceptibility has not been undertaken. We hypothesize that ensemble modeling of wildfire susceptibility is better than a single modeling technique. This study models the occurrence of wildfire in the Brisbane Catchment of Australia, which is an annual event, using the index of entropy (IoE), evidential belief function (EBF), and logistic regression (LR) ensemble techniques. As a secondary goal of this research, the spatial distribution of the wildfire risk from different aspects such as urbanization and ecosystem was evaluated. The highest accuracy (88.51%) was achieved using the ensemble EBF and LR model. The outcomes of this study may be helpful to particular groups such as planners to avoid susceptible and risky regions in their planning; model builders to replace the traditional individual methods with ensemble algorithms; and geospatial users to enhance their knowledge of geographic information system (GIS) applications.

Invasive weed species (IWS) threaten ecosystems, the distribution of specific plant species, as well as agricultural productivity. Predicting the impact of climate change on the current and future distributions of these unwanted species forms an important category of ecological research. Our study investigated 32 globally important IWS to assess whether climate alteration may lead to spatial changes in the overlapping of specific IWS globally. We utilized the versatile species distribution model MaxEnt, coupled with Geographic Information Systems, to evaluate the potential alterations (gain/loss/static) in the number of potential ecoregion invasions by IWS, under four Representative Concentration Pathways, which differ in terms of predicted year of peak greenhouse gas emission. We based our projection on a forecast of climatic variables (extracted from WorldClim) from two global circulation models (CCSM4 and MIROC-ESM). Initially, we modeled current climatic suitability of habitat, individually for each of the 32 IWS, identifying those with a common spatial range of suitability. Thereafter, we modeled the suitability of all 32 species under the projected climate for 2050, incorporating each of the four Representative Concentration Pathways (2.6, 4.5, 6.0, and 8.5) in separate models, again examining the common spatial overlaps. The discrimination capacity and accuracy of the model were assessed for all 32 IWS individually, using the area under the curve and true skill statistic rate, with results averaging 0.87 and 0.75 respectively, indicating a high level of accuracy. Our final methodological step compared the extent of the overlaps and alterations under the current and future projected climates. Our results mainly predicted decrease on a global scale, in areas of habitat suitable for most IWS, under future climatic conditions, excluding European countries, northern Brazil, eastern US, and south-eastern Australia. The following should be considered when interpreting these results: there are many inherent assumptions and limitations in presence-only data of this type, as well as with the modeling techniques projecting climate conditions, and the envelopes themselves, such as scale and resolution mismatches, dispersal barriers, lack of documentation on potential disturbances, and unknown or unforeseen biotic interactions.

Forest fire is an environmental disaster that poses immense threat to public safety, infrastructure, and biodiversity. Therefore, it is essential to have a rapid and robust method to produce reliable forest fire maps, especially in a data-poor country or region. In this study, the knowledge-based qualitative Analytic Hierarchy Process (AHP) and the statistical-based quantitative Frequency Ratio (FR) techniques were utilized to model forest fire-prone areas in the Himalayan Kingdom of Bhutan. Seven forest fire conditioning factors were used: land-use land cover, distance from human settlement, distance from road, distance from international border, aspect, elevation, and slope. The fire-prone maps generated by both models were validated using the Area Under Curve assessment method. The FR-based model yielded a fire-prone map with higher accuracy (87% success rate; 82% prediction rate) than the AHP-based model (71% success rate; 63% prediction rate). However, both the models showed almost similar extent of ‘very high’ prone areas in Bhutan, which corresponded to coniferous-dominated areas, lower elevations, steeper slopes, and areas close to human settlements, roads, and the southern international border. Moderate Resolution Imaging Spectroradiometer (MODIS) fire points were overlaid on the model generated maps to assess their reliability in predicting forest fires. They were found to be not reliable in Bhutan, as most of them overlapped with fire-prone classes, such as ‘moderate’, ‘low’, and ‘very low’. The fire-prone map derived from the FR model will assist Bhutan’s Department of Forests and Park Services to update its current National Forest Fire Management Strategy.

In this study, we propose and test a novel ensemble method for improving the accuracy of each method in flood susceptibility mapping using evidential belief function (EBF) and support vector machine (SVM). The outcome of the proposed method was compared with the results of each method. The proposed method was implemented four times using different SVM kernels. Hence, the efficiency of each SVM kernel was also assessed. First, a bivariate statistical analysis using EBF was performed to assess the correlations among the classes of each flood conditioning factor with flooding. Subsequently, the outcome of the first stage was used in a multivariate statistical analysis performed by SVM. A highest prediction accuracy of 92.11% was achieved by an ensemble EBF-SVM-radial basis function method; the achieved accuracy was 7% and 3% higher than that offered by the individual EBF method and the individual SVM method, respectively. Among all the applied methods, both the individual EBF and SVM methods achieved the lowest accuracies. The reason for the improved accuracy offered by the ensemble methods is that by integrating the methods, a more detailed assessment of the flooding and conditioning factors can be performed, thereby increasing the accuracy of the final map.

River flooding can be a highly destructive natural hazard. Numerous approaches have been used to study the phenomenon; however, insufficient knowledge regarding flood conditioning factors continues to hinder prevention and control measures. This research examines the hypothesis that by adding further conditioning factors to a dataset used in river flood modeling, increases the accuracy of the final susceptibility mapping result. Additionally, this study assesses the impact of individual conditioning factors on flood susceptibility mapping and their importance in the construction of precise mapping of potential flood regions. Two robust machine learning approaches, Decision Tree (DT) and Support Vector Machine (SVM), were utilized to evaluate spatial correlations between flood conditioning factors and rate their level of importance for mapping the flood prone areas. For this purpose, two datasets were used; dataset 1 (DS1): Light Detection and Ranging (LiDAR) derived factors of altitude, slope, aspect, curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), Topographic Roughness Index (TRI), and Sediment Transport Index (STI) and dataset 2 (DS2): a combination of LiDAR derived factors supplemented by geology, soil, landuse/cover (LULC), distance from roads and distance from rivers parameters. An extreme flood event in 2011 in Brisbane, Australia was used as a case study, in which DT and SVM techniques were both applied, using both datasets. In addition, multi-collinearity, variance inflation factors (VIF), Pearson's correlation coefficients and Cohen's kappa analysis provided useful information regarding the inter-relationships of factors, as well as the influence of each factor on the precision of the final map. The area under curve (AUC) method was used for accuracy assessment. SVM and DT produced the highest accuracies of prediction, with rates of 85.52% and 88.47% respectively, using DS1 (the LiDAR dataset). Altitude, SPI and TRI were found to have a significant impact on the precision of the outcomes. It was concluded that the inclusion of additional factors in the modeling, does not necessarily guarantee the achievement of greater accuracy. However, the modeling method, can significantly alter outcomes.

Flood is one of the most common natural disasters worldwide. The aim of this study was to evaluate the application of the Dempster–Shafer-based evidential belief function (EBF) for spatial prediction of flood-susceptible areas in Brisbane, Australia. This algorithm has been tested in landslide and groundwater mapping; however, it has not been examined in flood susceptibility modelling. EBF has an advantage over other statistical methods through its capability of evaluating the impacts of all classes of every flood-conditioning factor on flooding and assessing the correlation between each factor and flooding. EBF outcomes were compared with the results of well-known statistical methods, including logistic regression (LR) and frequency ratio (FR). Flood-conditioning factor data set consisted of elevation, aspect, plan curvature, slope, topographic wetness index (TWI), geology, stream power index (SPI), soil, land use/cover, rainfall, distance from roads and distance from rivers. EBF produced the highest prediction rate (82.60%) among all the methods. The research findings may provide a useful methodology for natural hazard and land use management.

Statistical methods are the most popular techniques to model and map flood-prone areas. Although a wide range of statistical methods have been used, application of the statistical index (Wi) method has not been examined in flood susceptibility mapping. The aim of this research was to assess the efficiency of the Wi method and compare its outcomes with the results of frequency ratio (FR) and logistic regression (LR) methods. Thirteen factors, namely, altitude, slope, aspect, curvature, geology, soil, landuse/cover (LULC), topographic wetness index (TWI), stream power index (SPI), terrain roughness index (TRI), sediment transport index (STI), and distance from rivers and roads, were utilized. A flood inventory was constructed from data captured from the destructive flood that occurred in Brisbane, Australia, in 2011. Model performances were compared using the area under the curve (AUC), Kappa index and five other statistical evaluation tools. The AUC prediction rates acquired for LR, Wi and FR were 79.45%, 78.18%, and 67.33%, respectively. A more realistic representation of the flood-prone area distribution was produced by the Wi method compared to those of the other two techniques. Our research shows that the Wi method can be used as an efficient approach to perform flood susceptibility analysis.

Aedes albopictus, the Asian Tiger Mosquito, vector of Chikungunya, Dengue Fever and Zika viruses, has proven its hardy adaptability in expansion from its natural Asian, forest edge, tree hole habitat on the back of international trade transportation, re-establishing in temperate urban surrounds, in a range of water receptacles and semi-enclosures of organic matter. Conventional aerial spray mosquito vector controls focus on wetland and stagnant water expanses, proven to miss the protected hollows and crevices favoured by Ae. albopictus. New control or eradication strategies are thus essential, particular in light of potential expansions in the southeastern and eastern USA. Successful regional vector control strategies require risk level analysis. Should strategies prioritize regions with non-climatic or climatic suitability parameters for Ae. albopictus? Our study used current Ae. albopictus distribution data to develop two independent models: (i) regions with suitable non-climatic factors, and (ii) regions with suitable climate for Ae. albopictus in southeastern USA. Non-climatic model processing used Evidential Belief Function (EBF), together with six geographical conditioning factors (raster data layers), to establish the probability index. Validation of the analysis results was estimated with area under the curve (AUC) using Ae. albopictus presence data. Climatic modeling was based on two General Circulation Models (GCMs), Miroc3.2 and CSIRO-MK30 running the RCP 8.5 scenario in MaxEnt software. EBF non-climatic model results achieved a 0.70 prediction rate and 0.73 success rate, confirming suitability of the study site regions for Ae. albopictus establishment. The climatic model results showed the best-fit model comprised Coldest Quarter Mean Temp, Precipitation of Wettest Quarter and Driest Quarter Precipitation factors with mean AUC value of 0.86. Both GCMs showed that the whole study site is highly suitable and will remain suitable climatically, according to the prediction for 2055, for Ae. albopictus expansion.