Abstract. Sea-level rise in Southeast Asia is a consequence of climate change that will affect almost all coastal countries in the region. The results of this phenomenon may have severe consequences, from problems with food production, through mass migration of people, to the threat to unique ecological areas. Hence, the main aim of this research was to investigate the impact of sea level rise on the land cover structure in the region and how it may affect the situation of the countries in the region. For this purpose, GlobCover 2009 data and projections of sea level rise by one meter were used and a multiband raster image was created containing information about the land cover class, country and whether the area is threatened by sea level rise. All calculations have been made on the raster prepared in this way, which shows that 4.4% of South East Asia's areas are at risk of rising sea levels. Finally, the ratio was calculated for each land cover class. This showed the unusual vulnerability of some of the classes to rising sea levels like irrigated croplands and urban areas.

The author presents the results of research on the use of artificial neural networks in predicting voter turnout. He describes the principles of operation of artificial neural networks, as well as detailed results of two machine learning methods used to predict voter turnout. The research resulted in creation of a functional model that allows for prediction of voter turnout results with a considerable degree of accuracy. The entire research process was carried out using the cartographic research method.

Anthropogenically-induced climate change is expected to be the contributing cause of sea level rise and severe storm events in the immediate future. While Danish authorities have downscaled the future oscillation of sea level rise across Danish coast lines in order to empower the coastal municipalities, there is a need to project the local cascading effects on different sectors. Using geospatial analysis and climate change projection data, we developed a proposed workflow to analyze the impacts of sea level rise in the coastal municipalities of Guldborgsund, located in Southeastern Denmark as a case study. With current estimates of sea level rise and storm surge events, the island of Falster can expect to have up to 19% of its landmass inundated, with approximately 39% of the population experiencing sea level rise directly. Developing an analytical workflow can allow stakeholders to understand the extent of expected sea level rise and consider alternative methods of prevention at the national and local levels. The proposed approach along with the choice of data and open source tools can empower other communities at risk of sea level rise to plan their adaptation.