<p><strong>Abstract.</strong> For quick and efficient response, as well as for recovery after any natural or artificial catastrophe, one of the most important things are accurate and reliable spatial data in real or near real-time. It is essential to know the location as well as to track and analyse passive and active threats to quickly identify the possible dangers and hazards. As technology evolves and advances, there is a broader spectrum of sensors that provide spatial data, and nowadays, decision-making processes also include nontraditional, informal sources of information. Apart from the offer, demand for new spatial data is increasing as well. For quicker and enhanced integration and analysis of data, artificial intelligence (AI) tools are increasingly used which, in addition to immediate rapid reactions, can help to make better and smarter decisions in the future. Such software algorithms that imitate human intelligence can help in generating conclusions from natural phenomena presented by spatial data. Using AI in the data analysis can identify risk areas and determine future needs. This paper presents an overview of the use of AI in geospatial analysis in disaster management.</p>
<p><strong>Abstract.</strong> One of the objectives of the Integrated Coastal Zone Management (ICZM) is to prevent and reduce the effects of natural hazards, particularly ones caused by climate changes. The ICZM methodologies include use of geographic information systems, from data collection and geo-analysis to dissemination of information to the public. As a part of the Interreg MED Co-evolve project cofinanced by the European regional development fund, the ICZM based action plan is being developed for the City of Kaštela in Croatia. Activities include assessing coastal vulnerability to climate change, focusing on sea flooding and storm damages and related socio-economic vulnerabilities. The paper presents development of large scale vulnerability analysis, adopted from the methodologies developed for mid and small scales. Suitability of the available data is assessed, either official or open source, and data gaps are described. The analysis’s results are presented in terms of the assets exposed to coastal flooding and storms, and future improvements of analysis towards house level vulnerability analysis is envisaged.</p>
Problems in real life usually involve uncertain, inconsistent and incomplete information. An example of such problems is strategic decision making with respect to remediation planning of historic pedestrian bridges. The multiple decision makers and experts, as well as the various mutually conflicting criteria, unknown criteria weights, and vagueness and duality in final decisions, provide motivation to develop a methodology that is able to resist the challenges implicit in this problem. Therefore, the aim of this research was to propose an algorithm based on the theory of rough neutrosophic sets in order to solve the problem of strategic planning with respect to the remediation of historic pedestrian bridges. A new multicriteria decision-making model is developed that is a fusion of rough set and neutrosophic set theory. A new cross entropy is proposed under a rough neutrosophic environment that does not possess the shortcomings of asymmetrical character and unknown occurrences. Additionally, a weighted rough neutrosophic symmetric cross entropy is proposed. Furthermore, a rough neutrosophic VIKOR method is introduced, with which the values of the utility measure, regret measure and VIKOR index are obtained. These values, as well as the weighted rough neutrosophic symmetric cross entropy measure, are used to provide a ranking of historic pedestrian bridges favorable to remediation. Finally, an illustrative example of the strategic planning of remediation for historic pedestrian bridges is solved and compared to other research, demonstrating the robustness, feasibility and efficacy of the model when dealing with complex multicriteria decision-making processes.
With the urbanization and expansion of cities, which have taken place over recent decades, new demands and problems are emerging, among which is the problem of inadequate transport infrastructure. The number of motor vehicles is growing, while transport infrastructure is not following that growth fast enough. One of the problems that arises is the insufficient number of garages and parking lots, causing an increase in illegal parking on sidewalks, which impedes and endangers pedestrian traffic. This paper proposes a new decision support concept (DSC) for the management of illegally parked cars in urban centers, which offers a method that can contribute to solving this problem and improving the flow of pedestrians on city roads. Due to its complexity, the problem addressed in this research is recognized as a multicriteria one and therefore the proposed model is based on the use of multicriteria analysis methods—more precisely, the Preference Ranking Organization Method for Enrichment Evaluation—PROMETHEE, and the analytic hierarchy process—AHP. The proposed DSC is validated in the city of Split (Croatia), more precisely in the neighborhood of Sućidar, which shows that this methodology is applicable and effective for finding not a temporary but a permanent solution to the problem described.
By establishing of the Common Land Registry and Cadastre Information System (ZIS) in the Republic of Croatia, a unique register of cadastre and land registry was created. Systems are interconnected and exchange property-related data. A unique database and application for managing and maintaining cadastral and land registry data have been established. In the year 2010, within the Official Topographic Cartographic Information System of the Republic of Croatia (STOKIS) the basic topographic database and cartographic database were completed, together with the topographic map scale 1: 25000. Currently STOKIS database update is in process. The question that logically implies itself is whether it is possible and how to link ZIS and STOKIS and eliminate, at least to a certain extent, double data collecting. This paper uses experiences from the German ATKIS
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