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The Model of the Formation of Values and the Information System of Their Determination in the Projects of the Creation of Territorial Emergency and Rescue Structures
(2023-05-19) Тryhuba, Аnatoliy; Ratushnyi, Andrii; Lub, Pavlo; Rudynets, Mykola; Visyn, Olena
Based on the performed analysis, the need to create territorial rescue structures to eliminate the consequences of emergencies of a military nature was established. A model of value formation from the implementation of projects for the creation of territorial rescue structures to eliminate the consequences of emergencies of a military nature is proposed. The model involves five stages that relate to the assessment of benefits for different groups of stakeholders (community population, community administration, business structures, project team, and the state). The peculiarities of the formation and structure of values, as well as their indicators, which are the basis of the quantitative assessment of value components, are defined. Based on the proposed model, an information system for decision support was developed. With the use of the information system, a quantitative assessment of the indicators of the basic cost of the projects for the creation of territorial rescue structures was carried out, taking into account the state of the project environment. It has been established that there is a weak correlation between the studied indicators of the basic value of projects for the creation of territorial rescue structures, which determines the use of intelligent methods for their forecasting. The distribution of the number of property losses of the population of communities from individual emergencies is substantiated. It was established that the implementation of the project to create an additional rescue formation will ensure a 33.2% reduction in the loss of community property.
A Data-Driven Risk-Informed Decision Support Framework for Sustainable Municipal Organic Waste Management in Smart Cities
(2026-06-08) Rudynets, Mykola; Fedorchuk-Moroz, Valentyna
The rapid growth of organic waste volumes in urban areas and increasing environmental pressures necessitate the transition toward sustainable and risk-informed municipal waste management systems. This study aims to develop a data-driven decision support framework for the risk-informed management of municipal organic waste within the context of sustainable urban development. The proposed approach integrates multi-source municipal data, advanced preprocessing techniques, entropy-based feature weighting, and an ensemble of machine learning models, including Random Forest, Gradient Boosting, and XGBoost. An integrated environmental risk index is formulated to quantify the state of the waste management system and to support predictive analytics. The results demonstrate high predictive performance and reveal that key risk drivers include demographic pressure, transport accessibility, infrastructure characteristics, and seasonal variability of waste generation. The developed framework enables the integration of predictive risk analytics into municipal decision support systems, facilitating optimized waste collection logistics, infrastructure planning, and early identification of critical conditions. The findings confirm that data-driven approaches can significantly enhance the efficiency and adaptability of urban waste management systems. The proposed framework contributes to sustainable urban development by supporting circular economy principles and enabling proactive, risk-aware governance of municipal organic waste systems.

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