Historical Assessment and Mapping of Human Plague, Kazakhstan, 1926-2003.

Understanding Kazakhstan's plague history is crucial for early warning and effective health disaster management. We used descriptive-analytical methods to analyze spatial data for human cases in natural plague foci in Kazakhstan during 1926-2003. The findings revealed 565 human cases across 82 outbreaks in Almaty (32.

Inventory of landslides triggered by an extreme rainfall event in Marche-Umbria, Italy, on 15 September 2022.

Systematic and timely documentation of triggered (i.e. event) landslides is fundamental to build extensive datasets worldwide that may help define and/or validate trends in response to climate change.

Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms.

This study investigates uncertainty in machine learning that can occur when there is significant variance in the prediction importance level of the independent variables, especially when the ROC fails to reflect the unbalanced effect of prediction variables. A variable drop-off loop function, based on the concept of early termination for reduction of model capacity, regularization, and generalization control, was tested. A susceptibility index for airborne particulate matter of less than 10 μm diameter (PM10) was modeled using monthly maximum values and spectral bands and indices from Landsat 8 imagery, and Open Street Maps were used to prepare a range of independent variables.

Spatial identification and temporal prediction of air pollution sources using conditional bivariate probability function and time series signature.

Accurate identification of distant, large, and frequent sources of emission in cities is a complex procedure due to the presence of large-sized pollutants and the existence of many land use types. This study aims to simplify and optimize the visualization mechanism of long time-series of air pollution data, particularly for urban areas, which is naturally correlated in time and spatially complicated to analyze. Also, we elaborate different sources of pollution that were hitherto undetectable using ordinary plot models by leveraging recent advances in ensemble statistical approaches.