Monitoring of the water level and river discharge is an important task, necessary both for assessment of water supply in the current season and for forecasting water consumption and possible prevention of catastrophic events. A network of ground hydrometric stations is used to measure the water level and consumption in rivers. Rivers located in sparsely populated areas in developing countries of Central Asia have a very limited hydrometric network. In addition to the sparse network of stations, in some cases remote probing data (virtual hydrometric stations) are used, which can improve the reliability of water level and discharge estimates, especially for large mountain rivers with large volumes of suspended sediment load and significant channel instability. The aim of this study is to develop a machine learning model for remote monitoring of water levels in the large transboundary (Kazakhstan-People’s Republic of China) Ili River. The optical data from the Sentinel-2 satellite are used as input data. The in situ (ground-based) data collected at the Ili-Dobyn gauging station are used as target values. Application of feature engineering and ensemble machine learning techniques has achieved good accuracy of water level estimation (Nash–Sutcliffe model efficiency coefficient (NSE) >0.8). The coefficient of determination of the model results obtained using cross-validation of random permutations is NSE = 0.89. The method demonstrates good stability under different variations of input data and ranges of water levels (NSE > 0.8). The average absolute error of the method ranges from 0.12 to 0.18 meters against the background of the maximum river water level spread of more than 4 meters. The obtained result is the best current result of water level prediction in the Ili River using the remote probing data and can be recommended for practical use for increasing the reliability of water level estimation and reverse engineering of data in the process of river discharge monitoring.

Approximately 50% of the world’s uranium is mined in a closed way using underground well leaching. In the process of uranium mining at formation-infiltration deposits, an important role is played by the correct identification of the formation of reservoir oxidation zones (ROZs), within which the uranium content is extremely low and which affect the determination of ore reserves and subsequent mining processes. The currently used methodology for identifying ROZs requires the use of highly skilled labor and resource-intensive studies using neutron fission logging; therefore, it is not always performed. At the same time, the available electrical logging measurements data collected in the process of geophysical well surveys and exploration well data can be effectively used to identify ROZs using machine learning models. This study presents a solution to the problem of detecting ROZs in uranium deposits using ensemble machine learning methods. This method provides an index of weighted harmonic measure (f1_weighted) in the range from 0.72 to 0.93 (XGB classifier), and sufficient stability at different ratios of objects in the input dataset. The obtained results demonstrate the potential for practical use of this method for detecting ROZs in formation-infiltration uranium deposits using ensemble machine learning.

COVID-19 significantly impacted the management of investments in the residential property market. Changes in property demand, shifts in market volatility, financing challenges, changes in property management and changes in local regulations and policies are presented. The pandemic created a more challenging and unpredictable investment management (IM) environment in the residential property market. However, with proper knowledge management (KM) and investment strategies, investors may have opportunities to thrive in this changing market. Investment KM refers to identifying, capturing, organising and sharing information and expertise related to investment activities within an organisation. KM in residential investment involves developing a system for gathering, organising and using information and expertise related to buying, owning, and selling a property. COVID-19 significantly affected the property market. Effective KM in residential investment can help individuals make informed decisions, identify potential risks, and maximise the value of their investment. Like many others worldwide, the Turkish residential market was hit by COVID-19. The Turkish residential market has a few key points: financing, shifts in buyer preferences, and regulation changes. The pandemic hit Turkey's residential property market, leading to property price changes. The Turkish government implemented several residential policies to support the residential market and help individuals and families facing financial challenges due to the pandemic. Those policies did not eliminate the challenges but mitigated some of the negative effects of the pandemic on the market and helped individual investors. This article discusses house price changes in Turkey during the COVID-19 period, the factors affecting price changes, and the property policies that affect property price changes. This article analyses IM challenges and explores how KM can help individual property investors.

This article explores the impact of the smart economy on the infrastructure and logistics of the One Belt, One Road (BRI – Belt and Road Initiative) initiative. The integration of smart technologies into the project has the potential to transform traditional systems, improve communication, increase efficiency and contribute to sustainable development. Purpose. This study aims to fill the existing knowledge gaps and shed light on the impact of the smart economy on the infrastructure and logistics of the One Belt, One Road project. By examining the opportunities, challenges and implications, this study aims to contribute to the understanding and successful implementation of smart technologies in this ambitious initiative. Methodology. The article uses the methods of quantitative empirical research, statistical analysis and visualisation, analysis and evaluation of the analytical assessment of tools for forming an innovative logistics network within the framework of the One Belt, One Road project. Results. The article examines the impact of the smart economy on connectivity, efficiency and sustainability, and highlights the perspectives and recommendations of different stakeholders. It emphasises the importance of cooperation between governments, businesses and local communities for successful implementation. The findings suggest that while the smart economy offers immense opportunities, challenges such as the digital divide, data protection and investment requirements need to be addressed. The article concludes with recommendations to foster cooperation, develop supportive policies, invest in digital infrastructure and prioritise sustainability. By following these recommendations, the One Belt, One Road project can effectively harness the transformative potential of the smart economy and create a sustainable and inclusive future. Practical implications. The materials can be used to plan a development strategy, taking into account new technologies and the possibility of implementation in the national development strategy. Value/originality. The integration of smart technologies into the infrastructure and logistics systems of the One Belt, One Road project plays a pivotal role in shaping the impact of the smart economy. By leveraging advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), big data analytics and blockchain, the project aims to optimise connectivity, enhance operational efficiency and promote sustainable development. Key aspects of development include smart infrastructure projects, digitisation and automation in logistics, blockchain technology for transparency and security, and data-driven decision-making and predictive analytics.

Modern information and communication technologies are becoming the basis for a new quality of cities, the organisation of a comfortable living space in cities, improving the quality of life of the population, ensuring sustainable development and improving technology as a tool for implementing these tasks. The implementation of all these measures requires significant investments, which makes it necessary to find various ways of diversifying financial sources and mechanisms. The purpose of the article is to identify modern investment processes in the development of smart technologies of the world's smart cities in the conditions of large-scale digitalisation. Methodology. Graphic visualization methods are used to characterize trends in the penetration of the latest ICT technologies into the life of cities, the growth of the ICT market in smart cities, including 5G technologies. The methods of system analysis and generalisation allowed to determine the main directions of investment in smart city technologies and to identify the processes of formation of the newest forms of investment. Practical implications. The generalisation of the smart city practice made it possible to identify the following basic technologies necessary for the formation of a smart city ecosystem: Internet of Things (IoT), artificial intelligence (AI), Big Data, cloud computing, etc. These technologies are used to improve the quality of life, increase the efficiency of the economy and infrastructure, ensure community safety and promote the sustainable development of the smart city. These key areas require significant investment: communications infrastructure (5G network); traffic management systems; energy efficiency; security and surveillance systems; electronic service management. Global investment in smart city technology is predicted to increase many times over in the coming years. Value/originality. The need to attract significant investment in the development of the city's digital infrastructure has led to the emergence of the latest financial technologies that have widened the range of opportunities: direct investment, venture capital, corporate investment, corporate partnerships, state and local funds, crowdfunding, bonds, fintech, blockchain, etc.

Investing in residential property is a good investment instrument for individuals. The benefit of investing in a residential property is the potential for long-term appreciation in value. Historically, property values have increased over time, providing a solid return on investment for those who hold onto their properties for a significant amount; property provides a source of passive income through rental income. Investing in property is an attractive investment opportunity in emerging markets. Turkey is considered an emerging market. Like many emerging markets, it faces several economic and political challenges, including high inflation, a large current account deficit, and political instability. Investing in property in emerging markets also comes with certain risks, including currency fluctuations, political instability, and uncertain legal and regulatory environments. During the SARS outbreak, the property market in Hong Kong was significantly affected. The Spanish flu pandemic had a significant impact on the property market. During the 19th-century cholera pandemics, there were instances of substantial disruption to property markets. The COVID-19 had significant impacts on the residential market. Developing a methodology for individuals to use for their residential investment can be challenging, as it requires consideration of various factors. There are a variety of resources available for individuals who are interested in learning more about investing in property, and people are able to educate themselves in the field of property investment. In this study, we examined individuals' residential property investments in Turkey during COVID-19, and the factors that influenced their investments. We examined whether individuals' consideration of a single advantage in residential investments provides them with financial benefits. We examined the parameters that residential investors can follow to achieve success with their investments.

PurposeThis study aims to examine and analyse data relating to cultural heritage, smart tourism and smart tourism tools for preserving cultural heritage in the Baltic states – Estonia, Latvia and Lithuania.Design/methodology/approachThe paper is based on a qualitative review of data from National tourism board web sites, the UNESCO World Heritage Convention, and the European Heritage Label sites. The paper analyses various sources in terms of cultural heritage and smart tourism and examines different ways of applying smart tourism tools to cultural heritage.FindingsThe findings show that smart tourism, and especially the preservation of cultural heritage in the Baltic states, is not a new thing, and it is rapidly progressing by inventing new technological tools for tourists to use and as well by digitising cultural heritage. Comprehensive examination of three countries shows that there is significant potential already and that there is a way to grow further for each of the countries by applying new technological tools for preservation of a cultural heritage.Originality/valueThe paper reviews data on cultural heritage and smart tourism tools in the Baltic states where it already plays a steady and significant role. Specifically, it contributes to recognition of each of the states on a global level in terms of variety of cultural heritage that is being preserved and digitalised. The originality of the paper is determined by the assessment of the smart tools used for cultural tourism.

ABSTRACT This study aims to provide the users with formulas for petrol station value estimation activities in Turkey. Firstly, 432 advertisements were used to estimate the asking price of the real estate listings dataset. Secondly, data obtained from 239 valuation reports were used to estimate value. The data were analysed with regression analysis, the variables were found to explain 68.7% of the asking price in the advertisement data and 90.7% of the value in the valuation reports. The results indicated that users could estimate the value of a petrol station by using some of the advertisement data or valuation reports in the estimation. This study can help valuers, especially in cases where access to the actual sales data is required for the market approach, the data needed for the cost approach, and the time needed for value estimation are limited. Our findings indicate that the share of ancillary revenues in the total revenues of petrol stations increased. In Turkey, the revenues of petrol stations, and the revenues from sales of petroleum products increased by 189%, and ancillary revenues by 326% from 2016 to 2021. Moreover, the value of petrol stations belong to one of the five major dealers increase by 24.4%.

An important feature of the current stage of world development is the increase in turbulence and tension, the increase in threats and the actualization of security issues. The creation of military-political and economic associations of countries makes it possible to unite efforts in solving security problems and ensuring their development. One of such promising associations can be cooperation between the countries of the Baltic-Black Sea region. The purpose of the article is to assess the prerequisites and possibilities for creating the Baltic-Black Sea union, modeling its various combinations. The subject of the research is the model of the economic and military union of the countries of the Baltic-Black Sea region. The implementation of the analytic hierarchy process (AHP) method made it possible, using mathematical tools, to assess the conditions and probability of creating a BBS union, based on a multi-criteria analysis of the military, political or economic potential of states, and also to model options for its composition. Results. The idea of creating a BBS has a long history, which is due to a whole range of historical, geographical, political and military prerequisites. Ensuring the security of the BBS countries includes not only a military aspect, but also involves the development of economic cooperation and development. The composition of the BBS can vary considerably: from a basic number of core countries to a broad open participation of the countries of the Baltic, Black and Adriatic Sea regions and all neighboring countries. The Baltic-Black Sea cooperation can become a very powerful economic entity. Focusing on multilateral cooperation of partner countries in various fields will unite both resource-rich countries and fairly developed countries that have formed their specialization in the new conditions of the digital economy. Practical implications. Of particular importance is the creation of unified transport corridors between the northern and southern seas of the European continent. BBS can become part of a multi-stage and multi-level security system in Europe and in the world as a whole. The value/originality of this publication is due to the high relevance of the issue in the context of open military aggression and the need to find new mechanisms for ensuring collective security in Europe.

UAVs have a great potential of application for monitoring, search, detection, communication, delivery and transportation of cargo in various sectors of economy. In spite of this, the existing software and hardware, as well as legal limitations, prevent the wide application of UAVs. There is intensive research related to automation and optimization of missions of one or more UAVs in various application areas. However, the execution of missions of both individual vehicles and their homogeneous or heterogeneous groups depends on the reliability issues of these technical devices, UAVs fleets, and control systems. In this paper, we present models for assessing fleet reliability of UAVs that are managed centralized or decentralized. The method is based on the representation of the fleet as a Binary-State System. The following topologies are considered: (a) a homogenous irredundant drone fleet, (b) a homogenous hot stable redundant drone fleet, (c) a heterogeneous irredundant drone fleet, and (d) a heterogeneous hot stable redundant drone fleet. For the listed topologies, reliability estimates were obtained as a function of the number of primary and redundant UAVs.