GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh.

The frequency of landslides and related economic and environmental damage has increased in recent decades across the hilly areas of the world, no exception is Bangladesh. Considering the first step in landslide disaster management, different methods have been applied but no methods found as best one. As a result, landslide assessment using different methods in different geographical regions has significant importance. The research aims to prepare and evaluate landslide susceptibility maps (LSMs) of the Chattogram district using three machine learning algorithms of Logistic Regression (LR), Random forest (RF) and Decision and Regression Tree (DRT). Sixteen landslide conditioning factors were determined considering topographic, hydro-climatic, geologic and anthropogenic influence. The landslide inventory database (255 locations) was randomly divided into training (80 %) and testing (20 %) sets. The LSMs showed that almost 9-12 % of areas of the Chattogram district are highly susceptible to landslides. The highly susceptible zones cover the Chattogram district's hill ranges where active morphological processes (erosion and denudation) are dominant. The ROC values for training data were 0.943, 0.917 and 0.947 and testing data were 0.963, 0.934 and 0.905 for LR, RF and DRT models, respectively. The accuracy is higher than the previous research in comparison to the extent of the study area and the size of the inventory. Among the models, LR showed the highest prediction rate and DRT showed the highest success rate. According to susceptibility zones, DRT is the more realistic model followed by LR. The maps can be applied at the local scale for landslide hazard management.

Geographic variability of post-disaster mental health: case study after the 2017 flood in Bangladesh.

Every year Bangladesh faces enormous damages due to flooding. Facing these damages the Government adopts various recovery approaches. However, the psychological dimension of any disaster is generally overlooked in disaster management. Researchers have found that the spatial distribution of post-disaster mental health can help the authorities to apply recovery procedures where they are most needed. For this research, Posttraumatic Stress Checklist (PCL-5), Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) were used to estimate posttraumatic stress, major depressive disorder and anxiety following three episodes of severe floods in 2017 that affected at least 8 million people. To better understand the spatial pattern of psychological vulnerability and reach a comprehensive scenario of post-disaster mental health, Moran's I was applied for spatial autocorrelation and Pearson's correlation and regression analysis for a study of the relationship between the psychological aspects. It was found that psychological vulnerability showed a spatial clustering pattern and that there was a strong positive linear relationship among psychological aspects in the study area. This research might help to adopt disaster management policies that consider the psychological dimension and spatial distribution of various psychological aspects to identify areas characterized by high vulnerability and risk so that they can be reached without delay.

Role of transport network accessibility in the spread of COVID-19 - a case study in Savar Upazila, Bangladesh.

The aim of this research was to test the hypothesis that people in a typical high-transport zone are particularly vulnerable with respect to transmission of coronavirus disease 2019 (COVID-19), a new contagious disease that has rapidly developed into a highrisk global problem. A case study was carried out in Savar Upazila, a sub-district of the capital city Dhaka in Bangladesh, which is traversed by a prominent national highway (Dhaka- Aricha-N5) that also passes the concentric industrial export processing zone surrounding Dhaka. This municipality is thus part of a high-density transport network with extensive economic activities. COVID-19 data were collected from local records at the Upazila Health Complex, while spatial data of the Savar Upazila, including the pertinent road network, were identified and digitized using geographical information systems software. The presence of COVID-19 in in Savar Upazila was found to be strongly associated with the reach and mechanism of transport networks (Pearson correlation = 0.76 with 99% confidence interval).