Nurturing Spiritual Resilience to Promote Post-disaster Community Recovery: The 2016 Alberta Wildfire in Canada

Spatio-temporal feature attribution of European summer wildfires with Explainable Artificial Intelligence (XAI)

Wildfires are among the most destructive natural disasters globally. Understanding the drivers behind wildfires is a crucial aspect of preventing and managing them. Machine learning methods have gained popularity in wildfire modeling in recent years, but their algorithms are usually complex and challenging to interpret. In this study, we employed the SHapley Additive exPlanations (SHAP) value, an Explainable Artificial Intelligence method, to interpret the model and thus generate spatio-temporal feature attributions. Our research focuses on the forest, shrub and herbaceous vegetated areas of Europe during the summers from 2018 to 2022. Using burned areas, meteorology, vegetation, topography, and anthropogenic activity data, we established a wildfire occurrence model using random forest classification. The model was highly accurate, with an Area Under the Receiver Operating Characteristic Curve of 0.940. The SHAP results revealed six features that significantly influence wildfire occurrences: land surface temperature (LST), solar radiation (SR), Temperature Condition Index (TCI), Normalized Difference Vegetation Index (NDVI), precipitation (Prep), and soil moisture (SM). The tipping points for the positive or negative shifts in contributions are around 30 °C (LST), 2.20e7 J/m^2 (SR), 0.2 (TCI), 0.78 (NDVI), 2 mm/h (Prep), and 0.18 (SM). These predictors display strong spatial variability in their contribution levels. In Southern Europe, LST and SR emerge as the primary contributors to wildfires, making substantial impacts. Conversely, in regions at mid and high latitudes in Europe, NDVI, Prep, and SM assume a more prominent role in promoting wildfires, albeit with relatively smaller contributions. Furthermore, the disparities in SHAP values for TCI and SMCI across different years provide valuable insights into the effects of variation in regional meteorological conditions on wildfires. Our study provides a new approach to uncovering the spatio-temporal variations of feature contributions, which will help to better understand the mechanism of wildfire occurrence and enhance prevention and mitigation.

Exploring the experiences and learning of young primary care physicians in disaster-affected areas: A qualitative study on the Great East Japan Earthquake

Background

Primary care physicians (PCPs) play a critical role in disaster medicine. However, it is unclear how PCPs who provide chronic support to disaster-affected areas learn from their experiences.

Methods

This qualitative study investigates the learnings of young PCPs who provided medical care during the chronic phase of the Great East Japan Earthquake disaster.

Results

Thematic analysis of interviews with five physicians revealed the challenges faced by them and their learnings in providing medical support to disaster-affected areas.

Conclusions

They not only learned medical skills but also humanistic aspects such as empathizing with the survivors' loss.

Active Fire Detection from Landsat-8 Imagery Using Deep Multiple Kernel Learning

Active fires are devastating natural disasters that cause socio-economical damage across the globe. The detection and mapping of these disasters require efficient tools, scientific methods, and reliable observations. Satellite images have been widely used for active fire detection (AFD) during the past years due to their nearly global coverage. However, accurate AFD and mapping in satellite imagery is still a challenging task in the remote sensing community, which mainly uses traditional methods. Deep learning (DL) methods have recently yielded outstanding results in remote sensing applications. Nevertheless, less attention has been given to them for AFD in satellite imagery. This study presented a deep convolutional neural network (CNN) “MultiScale-Net” for AFD in Landsat-8 datasets at the pixel level. The proposed network had two main characteristics: (1) several convolution kernels with multiple sizes, and (2) dilated convolution layers (DCLs) with various dilation rates. Moreover, this paper suggested an innovative Active Fire Index (AFI) for AFD. AFI was added to the network inputs consisting of the SWIR2, SWIR1, and Blue bands to improve the performance of the MultiScale-Net. In an ablation analysis, three different scenarios were designed for multi-size kernels, dilation rates, and input variables individually, resulting in 27 distinct models. The quantitative results indicated that the model with AFI-SWIR2-SWIR1-Blue as the input variables, using multiple kernels of sizes 3 × 3, 5 × 5, and 7 × 7 simultaneously, and a dilation rate of 2, achieved the highest F1-score and IoU of 91.62% and 84.54%, respectively. Stacking AFI with the three Landsat-8 bands led to fewer false negative (FN) pixels. Furthermore, our qualitative assessment revealed that these models could detect single fire pixels detached from the large fire zones by taking advantage of multi-size kernels. Overall, the MultiScale-Net met expectations in detecting fires of varying sizes and shapes over challenging test samples.

Natural disasters and mental health

Natural disasters are large-scale adverse events resulting from natural processes of the earth, often associated with death, trauma, and destruction of property. They threaten harm or death to a large group of people; cause disruption of services and social networks and a communal loss of resources; and involve identifiable mental and physical health outcomes, among those affected. While majority of individuals who experience a traumatic event due to natural disasters do not develop psychopathology, natural disasters can threaten our psychological well-being in many ways and they can result in both short and long-term psychological distress and thus create a significant burden of mental health conditions on individuals and the community affected by them. In this paper we provide a narrative review that focuses on the mental health effects of natural disasters. We discuss effective, evidence-based interventions that can help enhance the sense of safety, hope, and optimism, as well as serve to promote social connectedness for those who are impacted. We describe how these interventions, developed by keeping in mind the cultural context and the needs of the community, can be provided pre, peri and post-disaster period to improve the adverse mental health effects of the disaster.