B I Savelyev’s research while affiliated with Russian Academy of Sciences and other places

Sustainable development scientific concepts can be presented in the form of an integrated hierarchical network of frames. A knowledge base ontology model has been developed, which allows calculating the importance of frames, due to which the mechanism of adaptation to a given interdisciplinary field of scientific research is implemented. The ontology is identically represented as a multiplexed semantic network. An algorithm for determining the relevant frames based on the multisets metric spaces multiplicities has been developed. Mathematical and software support for the functioning of the decision support system has been developed, depending on the class of tasks for ensuring sustainable development.

Distributed ledger technologies can support a rapid transition to smart cities and provide a high level of urban quality. Despite the large number of approaches to the problem of synthesizing smart city management systems, there is still no universal solution. One of the most promising areas is the construction of neural network control systems. The optimization module for a neurosimulator is developed that can operate in real time. The study of the neurosimulator on various data of anthropogenic load showed the possibility of obtaining high control accuracy.

Sustainability science can produce different theories in different sub-sectors, but the overall scientific goal is to combine such theories within a general scientific coordinate system. Scientific and methodological approaches to determining the effectiveness of using the transdisciplinary research results have been developed. A system of criteria for assessing the transdisciplinary research results has been built in the form of a hierarchical structure. Qualitative scales of criteria ranking have been developed. The developed scientific and methodological approaches can be used in the formation of a system of sustainable development indicators.

Data envelopment analysis is an effective method for assessing the relative sustainability of ecosystems in megalopolises, which are characterized by a variety of environmental and socio-economic indicators. The existing models do not allow adapting the parameters of data envelopment analysis models to the preferences of the decision-maker and do not provide a consistent assessment of the effectiveness of complex socio-economic systems. The most important characteristics of complex socio-economic systems have to be calculated by indirect methods by solving a number of optimization problems. Adaptive data envelopment analysis models are developed to determine the sustainability of ecosystems in megalopolises on the basis of ecological and socio-economic indicators of anthropogenic load. In the proposed adaptive models, intermediate results at the previous stage of the functioning of the megalopolis ecosystem are only partially consumed at the next stage. A part of the input parameters of the functioning of the ecosystems of the megalopolis can be freely distributed. Additional input parameters are directly consumed at the next stage of the functioning of the megalopolis ecosystem. The optimal values of the parameters of the models are determined on the basis of the dialogue procedure of sequential cutting of multidimensional sets using affine subspaces. Indicators of sustainability of ecosystems of megalopolises, which characterize a low level of anthropogenic load, are calculated.

The use of modern information and communication technologies is an essential condition for the formation of the transport infrastructure of a smart city. Scientific and methodological approaches are developed to effectively monitor the transport infrastructure of a smart city based on multi-channel metric learning in the Internet of Things. The proposed solutions provide invariance to the type and nature of the movement of objects. The principles of technical implementation of the proposed method are substantiated using the characteristics of unmanned aerial vehicles of a smart city. Adaptive automatic switching of transport infrastructure monitoring channels is implemented in the form of a neural network analyzer software.

Environmental projects have a high degree of uncertainty and risk of their implementation. In the process of assessing their effectiveness, it is necessary to take into account the interests of all the participants in the environmental project. The environmental projects effectiveness evaluation is formalized in the form of a mathematical problem of nonlinear programming with Boolean variables with constraints such as equality and inequality. To solve it, the interactive genetic algorithm for the environmental projects implementation trajectory formation was developed.