Green Smart Cities Research Articles

Urban air quality index forecasting using multivariate convolutional neural network based customized stacked long short-term memory model

In the context of increasing urban pollution and its adverse health effects, this study focuses on enhancing the air quality prediction model by forecasting future concentrations of various Air Quality Index (AQI) levels to support the development of green smart cities. The primary objective of this study is to develop a robust multivariate time series forecasting model using a combination of Convolutional Neural Networks (CNN) and Customized Stacked-based Long Short-Term Memory (CSLSTM) networks, namely C2SLSTM. The proposed methodology involved preprocessing AQI and meteorological data, scaling the features, and employing a hybrid CNN-LSTM architecture to capture spatial and temporal dependencies in the data. Dynamic AQI pattern changes are computed using online learning. The proposed model was trained on a big dataset containing AQIs such as particulate matter (PM2.5), carbon monoxide (CO), and nitrogen dioxide (NO2), along with meteorological factors like weather count, temperature, wind, humidity, and pressure. Experimental results demonstrate that the model achieved a mean absolute error (MAE) of 0.25, mean square error (MSE) of 0.083, root mean square error (RMSE) of 0.289 and a R2-Score of 0.84 on the test set. Also, compared to Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Stacked Long Short-Term Memory (SLSTM), and Multilayer Perceptron (MLP) models, the C2SLSTM model showcases a performance enhancement of 35 %, 50 %, 30 %, and 32 %. Notably, the CNN component effectively extracted spatial features, while the LSTM layers captured temporal patterns, leading to precise predictions. These results indicate that the proposed approach effectively captures temporal dependencies and improves forecasting performance, with R2-Score values tending towards 1 for 12-hour prediction intervals. This research demonstrates the potential of advanced SLSTM architectures in accurately predicting AQI metrics, which can aid in better environmental monitoring and management. Also, the spatiotemporal correlation analysis is done through the designed ir metric.

D2A Performance with SSK Modulation and SWIPT in Green Smart Cities

D2A Performance with SSK Modulation and SWIPT in Green Smart Cities

How different can smart cities be? A typology of smart cities in China

China, renowned for being one of the world's largest smart city testing fields, has witnessed the emergence of numerous smart cities. Existing publications highlight the diversity in smart city development, wherein cities with different contexts and institutional factors exhibit distinct characteristics. To delve deeper into this subject, we employed a mixed methods approach to classify 49 Chinese smart cities and subsequently developed a typology for Chinese smart cities in this study. Our methodology involved three steps. Initially, we devised a classification framework based on the input-throughput-output model and conducted content analysis to identify the characteristics of the 49 cities. Subsequently, we utilized a combination of principal component analysis (PCA) and K-means clustering analysis to categorize these cases. Finally, we formulated a typology consisting of five types of Chinese smart cities: knowledge-technocratic smart cities, holistic smart cities, green smart cities, equipment-technocratic smart cities, and emerging smart cities. The findings reveal that different smart city types are characterized by distinct features and priorities in input, throughput, and output. The development of smart cities should comprehensively consider and respect the local urban contexts and the challenges they present. The insights from this study hold relevance for both policymakers and academic researchers.

Research on Regional Industrial Layout and Structural Adjustment and Upgrading under Digital Economy

Digital transformation promotes the change of urban governance. Based on the concept of "system", this paper focuses on exploring the influencing factors and evaluation indicators of the industrial structure adjustment and energy production and consumption structure adjustment of green smart cities in the new era under the promotion of digital economy, providing a strong theoretical basis for the comprehensive evaluation of the influencing factors in the later period.

Exploiting machine learning to tackle peculiar consumption of electricity in power grids: A step towards building green smart cities

AbstractThe increasing demand for electricity in daily life highlights the need for Smart Cities (SC) to use energy efficiently. Both technical and Non‐Technical Losses (NTL), particularly those resulting from electricity theft, present powerful obstacles; NTL alone can reach billions of dollars. Although Machine Learning (ML) based approaches for NTL detection have been embraced by numerous utilities, there is still a lack of thorough analysis of these methods. Limited research exists on NTL identification evaluation criteria and unbalanced data management in the context of SC. This research compares ML algorithms and data balancing methods to optimize electricity consumption detection. The given research applied the 15 ML techniques of Logistic regression, Bernoulli naive Bayes, Gaussian naive Bayes, K‐Nearest Neighbour, perceptron, passive‐aggressive classifier, quadratic discriminant analysis, SGD classifier, ridge classifier, linear discriminant analysis, decision tree, nearest centroid classifier, multi‐nomial naive Bayes, complement naive Bayes and dummy classifier. While SMOTE, AdaSyn, NRAS, and CCR are considered for data balancing. AUC, F1‐score, and seven relevant performance metrics were used for comparison. We have also implemented SHapely Additive exPlanations (SHAP) for feature importance and model interpretation. Results show varying classifier performance with different balancing methods, emphasizing data preprocessing's role in NTL detection for smart grid security.

Artificial Intelligence for Sustainable Dermatology in Smart Green Cities: Exploring Deep Learning Models for Accurate Skin Lesion Recognition

Smart and sustainable dermatology takes on a new dimension within Green Smart Cities with the integration of artificial intelligence (AI) into dermatological diagnosis. This study explores the success of deep learning models in accurately recognizing skin lesions, focusing on the use of the HAM10000 dataset. Our comparative analysis highlights the crucial impact of network architecture choices, data augmentation, and preprocessing on model performance.The results reveal that models leveraging transfer learning and fine-tuning on pre-trained networks excel in precision, underscoring their relevance in the context of smart green health. We also address opportunities for improvement in model generalization across diverse datasets and skin types. These findings provide a foundation for the development of more accurate skin lesion recognition models aligned with the principles of Green Smart Health, contributing to faster diagnostics, improved patient care, and ultimately, healthier Green Smart Cities.This work opens avenues for future research, such as exploring of the effectiveness of deep learning techniques in diverse health contexts and the integration of clinical data for more personalized dermatological diagnostics within Green Smart Cities.

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The Intersection of the Green and the Smart City: A Data Platform for Health and Well-Being through Nature-Based Solutions

An increasingly important aspect of analyzing the challenges facing cities today is the integration of nature. Nature-based solutions have the potential to successfully cope with the adverse effects of extensive urbanization and climatic change. On the other hand, the incorporation of smartness in cities is a critical issue. This paper aims to analyze the steps towards integrating nature-based solutions and smart city aspects to develop a web-based data platform that focuses on tackling and investigating the role of nature-based solutions in city health and well-being and returns a digital twin of the natural and built environment, including health-related key performance indicators. Seven pilot cities are used as a basis for the analysis. The architecture of a smart green city data platform is described. The interaction with the citizens is ensured through apps and games. The paper lays the foundation for a future “phygital” NBS world.

Usage of Airborne LiDAR Data and High-Resolution Remote Sensing Images in Implementing the Smart City Concept

The cities of the future should not only be smart, but also smart green, for the well-being of their inhabitants, the biodiversity of their ecosystems and for greater resilience to climate change. In a smart green city, the location of urban green spaces should be based on an analysis of the ecosystem services they provide. Therefore, it is necessary to develop appropriate information technology tools that process data from different sources to support the decision-making process by analysing ecosystem services. This article presents the methodology used to develop an urban green space planning tool, including its main challenges and solutions. Based on the integration of data from ALS, CLMS, topographic data, and orthoimagery, an urban green cover model and a 3D tree model were generated to complement a smart-city model with comprehensive statistics. The applied computational algorithms allow for reports on canopy volume, CO2 reduction, air pollutants, the effect of greenery on average temperature, interception, precipitation absorption, and changes in biomass. Furthermore, the tool can be used to analyse potential opportunities to modify the location of urban green spaces and their impact on ecosystem services. It can also assist urban planners in their decision-making process.

Keystones of green smart city—framework, e-waste, and their impact on the environment—a review

Keystones of green smart city—framework, e-waste, and their impact on the environment—a review

The analysis of electric vehicle integration based on big data technology

The potential of electric vehicles (EVs) to reduce greenhouse gas emissions in the transportation sector has given the adoption of EVs a considerable boost in recent years. Concurrently, the field of big data analytics has witnessed exponential growth, providing unprecedented opportunities for extracting valuable insights and optimizing various industrial sectors. This paper presents a comprehensive overview of the intersection between electric vehicles and big data analysis. Various EV-related data sources are explored along with the discussion of data computing platforms. Followed by this, this paper analyzes different use cases of big data analysis in EVs, covering key areas such as energy management, charging infrastructure optimization, and vehicle condition monitoring, which demonstrates how big data can be crucial for the successful integration of EVs into green smart cities. Finally, the author provides future research insight and opportunities for the use of big data techniques in EV adoptions. In particular, this paper serves as a roadmap for future research in the area of data analytics in EV integration.

Contemporary concepts of small town development

Motivation: The paradigms of sustainable development and knowledge and innovation-based economy (smart growth) are among the most important signposts for the development of contemporary towns, cities and regions. The growing competitiveness of cities and the high variation and complexity of the urban environment necessitate the search for such a model of a city that will enable its dynamic development in the economic, social and spatial spheres. The implemented development model determines the town’s ability to face the current challenges, such as population changes, increasing and diverse needs of the city’s users, limited natural resources which therefore require protection, a shift in the paradigm of local economy towards circular economy, thereby improving the town’s ability to deal with crisis (urban resilience). As a consequence, there is a growing number of smart cities as well as cities which have adopted another dominant development concept, such as green cities, slow cities.Aim: Purpose of the article is to discuss and compare some of these concepts, namely smart city, slow city, green city (and selected hybrid models), which can be applied in designing a policy for the development of small towns in Poland.Results: The diverse and increasingly varied conditions underlying the development of small towns mean that a local development policy should have a more reactive and scenario-like character. The process of evolution of the existing approaches can be observed, resulting in the hybridization of the development models, where new concepts arise by combining the assumptions of several approaches, for example a smart green city, etc. This process can be seen as the manifestation of improving the level of urban resilience to crises, which increases the chance of small towns to adapt to new conditions.

Bankruptcy problem in energy warehouse: Applications and challenges

Green smart cities that rely completely on sustainable renewable energy sources are being constructed to attract investors, diversify countries’ economies, and move away from dependence on fossil fuels. The main renewable energy sources used in such cities today include solar, wind, and—to a lesser extent—biomass. However, these sources are stochastic in nature, and the amount of energy they produce is not entirely controllable. This results in difficulty maintaining the balance between generation and demand in the power grid. Recent technological advances in smart grids, microgrids, virtual power plants, energy hubs, and energy interconnection technologies offer a wide range of plausible solutions to this problem. Such advances motivated the development of a complementary technology known as the energy warehouse (EW). An EW is a controlled and managed heterogeneous massive modular energy storage and wheeling system. It provides links between microgrids and bulk power systems (BPSs). After describing the structure of an EW and its energy management system and illustrating example EW operating scenarios, this paper uses the concept of bankruptcy to explain the business side of an EW. For the first time, this principle is used to address cases where the EW falls short of its obligations to the connected microgrids and BPSs. This treatment of EW contributes to a better understanding of its operation and opens further opportunities for contributions. Furthermore, our study confirms that a properly sized and located EW promises significant technical and economic benefits. These including load leveling and energy time-shift, as well as the potential to eliminate the typical transmission infrastructure, thereby reducing losses, mitigating congestion, and increasing efficiency.

Evaluation of plant species for air pollution tolerance and phytoremediation potential in proximity to a coal thermal power station: implications for smart green cities.

In metropolitan areas, air pollution poses a significant threat, and it is crucial to carefully select plant species that can tolerate such conditions. This requires a scientific approach based on systematic evaluation before recommending them to executive bodies. This study aimed to determine the air pollution tolerance index (APTI), dust retention capacity, and phytoremediation ability of 10 plant species growing in and around a lignite-based coal thermal power station. The results showed that Ficus benghalensis L. had the highest APTI, followed by Mimusops elengi L., Ficus religiosa L., Azadirachta indica A. Juss., and Annona reticulata L. F. benghalensis also showed the highest pH of leaf extract, relative water content, total chlorophyll, and ascorbic acid content, as well as the highest dust capturing capacity. Among the ten plant species, F. benghalensis, M. elengi, F. religiosa, A. indica and F. racemosa were identified as a tolerant group that can be used for particulate matter suppression and heavy metal stabilization in and around thermal power plants. These findings can inform the selection of plants for effective green infrastructure in smart green cities, promoting the health and well-being of urban populations. This research is relevant to urban planners, policymakers, and environmentalists interested in sustainable urban development and air pollution mitigation.

Spatial Assessment of Urban Growth on Green Spaces in Rwanda: An insight from Rebero Mountain Landscape in Kicukiro District, City of Kigali

Green space is a crucial component of sustainable urban development that provides urban dwellers with several environmental and social benefits. This study investigates the impact of urban growth on green space management on hilly landscape using a case study of Rebero Mountain in Kicukiro District, Kigali City. The study used medium resolution satellite imagery (Landsat for 2000, 2010, and 2021) and high-resolution imagery (Quick Bird 2010 and Worldview 2 images of 2015) to assess Land Uses Land Covers (LULC) dynamics. To investigate the local community's perception on the effects of urban growth on green space, interviews were conducted. The LULC changes detection revealed a significant decrease in green space with an increase in the built-up areas. The urbanized area has been increasing from 7,422.10 Ha up to 14,730.94 Ha during the last two decades. The increasing built-up area was in parallel with reduction of green space, specifically 44 Ha (-6.7%) of urban forest and 255 Ha (- 26.2%) of agricultural land, for the period between 2000 and 2021. The reduction of green cover had a negative impact on the environment, such as soil erosion and biodiversity loss. In order to support decision-makers in fostering the development of sustainable and green smart cities, a spatially explicit assessment of the availability, management, and accessibility of urban green areas is also important. Based on this research findings, the restoration of green spaces, including the need for land-use and land-cover change monitoring, practical government physical planning interventions, and an inclusive strategy for managing the city's green spaces that involves NGOs and citizens, and advocates for green city development are strongly recommended.

A Residual Resource Fitness-Based Genetic Algorithm for a Fog-Level Virtual Machine Placement for Green Smart City Services

Energy efficient information and communication technology (ICT) infrastructure at all levels of a city’s edifice constitutes a core requirement within the sustainable development goals. The ICT infrastructure of smart cities can be considered in three levels, namely the cloud layer infrastructure, devices/sensing layer infrastructure, and fog layer infrastructure at the edge of the network. Efficiency of a data-centre’s energy infrastructure is significantly affected by the placement of virtual machines (VMs) within the data-centre facility. This research establishes the virtual machine (VM) placement problem as an optimisation problem, and due to its adaptability for such complicated search issues, this paper applies the genetic algorithm (GA) towards the VM placement problem solution. When allocating or reallocating a VM, there is a large quantity of unused resources that might be used, however these resources are inefficiently spread over several different active physical machines (PMs). This study aims to increase the data-centre’s efficiency in terms of both energy usage and time spent on maintenance, and introduces a novel fitness function to streamline the process of computing the fitness function in GAs, which is the most computationally intensive component in a GA. A standard GA and first fit decreasing GA (FFD-GA) are applied on benchmark datasets to compare their relative performances. Experimental results obtained using data from Google data-centres demonstrate that the proposed FFD-GA saves around 8% more energy than a standard GA while reducing the computational overhead by approximately 66%.

Sensory gardens as a new form of urban green space in smart sustainable cities

Sensory gardens have been known since antiquity, but they are becoming more and more important in terms of smart city development. The article presents a literature review on the diverse functions of sensory gardens, the concept of a smart and green city, and the sensory garden as a new form of urban green spaces (UGS). The aim of the research was to identify expectations regarding the development of modern gardens in urban spaces, in the context of a smart city. The primary data collection method was social surveys on the expectations regarding the development of parks and city gardens, carried out among the inhabitants of Poland and Russia. The studied sample had a total of 230 people. The research was conducted in January 2021 using an internet survey. The questions were both open and closed, and related to issues such as: (1) the most important features of gardens and city parks; (2) the definition of a sensory garden; (3) the main functions that gardens and city parks should fulfil in the future. The results of the conducted research confirmed the adopted hypothesis that sensory gardens as green enclaves with different functions constitute new, prospective forms of development of urban green spaces in smart sustainable cities. The social needs for urban green spaces are clear and reflect current environmental problems regarding the quality of atmospheric air and access to green spaces in the vicinity. The respondents agree that the most important features of gardens and parks include fresh air and the possibility of resting. Keywords: green city, sustainable city, smart city, urban areas, sensory gardens; smart green city, smart green integration, and smart gardens.

Jeju Special Self-Governing Province Smart Green City Policy Review

This study aims to present the spread of a smart green city model centered on water and waste management among Jeju's environmental policies, and to provide sustainable green business services. To this end, a frame was introduced for the smart green policy at the SDGs and circular economy level, and a strategy was proposed after creating a problem map for linking with the upper plan for smart green policy at the central government and Jeju Island level and solving regional problems. Furthermore, the spatial linkage of Jeju with SDGs and sustainable green governance through improving resident-centered global cooperation were illustrated.

Theoretical and analytical assessment of smart green cities

As a locus of technological innovation, a smart city (SC) is a prototypical city of the future. Moreover, according to scholars, a smart city is also sustainable city. Nonetheless, the environmental aspects of urban sustainability are often de-emphasized in favor of discourses around the technical characteristics of SC technologies. In order to integrate the two, the article introduces the notion of a smart green city (SGC) in which technological means and environmental outcomes are in sustainable balance. SGC is presented here as a unifying concept integrating smart city and green city concepts through the environmental, social, and governance (ESG) framework. To illustrate the positive synergy between a city's greenness and smartness, we derive operational definitions of both based on the online media's attention to the related technology and sustainability initiatives. After calculating the two indices for all the cities worldwide with over one million inhabitants (498 cities), regression analysis is performed to determine the strength and direction of the relationships between a city's greenness and smartness. We find that a city's greenness is positively related to its smartness. Principal component analysis reveals a potential relationship between a city's population and the two indices. In particular, a large city's population negatively affects its greenness but positively affects its smartness. A joint index of smartness and greenness is negatively related to a city's population. Hence, the containment of uncontrolled urban growth is critical for successfully implementing SGC initiatives. The analysis results are of use to policy-makers, city managers, and planners intending to integrate the ESG framework into their future urban development strategies. Moreover, to our knowledge, a joint evaluation of a city's greenness and smartness has never been performed before on the inter-regional level of analysis. Accordingly, such a holistic assessment can be of methodological interest to scholars of smart and sustainable cities.

Towards Smart Green Cities: Analysis of Integrated Renewable Energy Use in Smart Cities

This study aims to estimate and explore the experience of introducing renewable energy use in the context of the world’s smart cities. In this regard, the study points out that the use of green energy is an important part of sustainable development. Environmental problems are a matter of global concern. Hence sustainable development is one of the approaches to end the harmful anthropogenic impact. The work includes quantitative assessment methods, for example, statistics, quantitative analysis, analogy, and synthesis. As a result, the analysis confirms that the effective development of a smart green city is impossible without the introduction of several renewable energy sources, the integrated use of which will reduce the likelihood of problems with the city’s energy supply. Likewise, the outcome accentuates that the desire to fully switch to renewable energy sources (RES) can be accompanied by several problems as the creation of RES technologies does not always take the risk of abnormal situations into account. In conclusion, the research findings are recommended to be taken into consideration by researchers in the field of smart and sustainable cities development, as well as urbanists and economists for designing future smart green cities based on renewable energy sources.