Advancing Urban Resilience Amid Rapid Urbanization: An Integrated Interdisciplinary Approach for Tomorrow’s Climate-Adaptive Smart Cities—A Case Study of Wuhan, China

Abstract

This research addresses the urgent challenges posed by rapid urbanization and climate change through an integrated interdisciplinary approach combining advanced technologies with rigorous scientific exploration. The comprehensive analysis focused on Wuhan, China, spanning decades of meteorological and land-use data to trace extreme urbanization trajectories and reveal intricate temporal and spatial patterns. Employing the innovative 360° radial Fibonacci geometric growth framework, the study facilitated a meticulous dissection of urban morphology at granular scales, establishing a model that combined fixed and mobile observational techniques to uncover climatic shifts and spatial transformations. Geographic information systems and computational fluid dynamics were pivotal tools used to explore the intricate interplay between urban structures and their environments. These analyses elucidated the nuanced impact of diverse morphosectors on local conditions. Furthermore, genetic algorithms were harnessed to distill meaningful relationships from the extensive data collected, optimizing spatial arrangements to enhance urban resilience and sustainability. This pioneering interdisciplinary approach not only illuminates the complex dynamics of urban ecosystems but also offers transformative insights for designing smarter, more adaptable cities. The findings underscore the critical role of green spaces in mitigating urban heat island effects. This highlights the imperative for sustainable urban planning to address the multifaceted challenges of the 21st century, promoting long-term environmental sustainability and urban health, particularly in the context of tomorrow’s climate-adaptive smart cities.

1. Introduction

Urbanization, alongside land-use and land-cover transformations (LULCTs), and climate change, represents critical challenges of the 21st century profoundly impacting environmental sustainability and human well-being [1]. Rapid urban growth alters local microclimates [2], disrupts carbon dynamics [3], reduces resilience to extreme weather events [4], intensifies urban heat island (UHI) effects [5], and drives extensive land-use changes [6]. Addressing these challenges is paramount for enhancing urban resilience and livability amidst accelerating pressures from urbanization, evolving land-use patterns, and climate variability. Within this context, Wuhan serves as an exemplary case study, shedding light on the intricate interplay between rapid urban expansion and environmental dynamics in contemporary China [7,8,9].

The significance of Wuhan as a research site is underscored by its remarkable ecosystem, characterized by 166 lakes and extensive natural landscapes spanning a significant portion of its territory [10,11]. The complex relationship between Wuhan’s diverse natural environment and its urban infrastructure presents a compelling opportunity to enhance the city’s ecological resilience. However, the rapid pace of urbanization poses multifaceted challenges in effectively regulating urban microclimates. This raises critical questions: How does rapid urbanization alter local microclimates and exacerbate UHI effects? Can the integration of ecosystem-based approaches effectively mitigate the impacts of urbanization on microclimates? Thus, a paradigm shift towards proactive climate-adaptive methodologies becomes imperative to bolster urban resilience amidst swift urban expansion. Recent trends in urban studies emphasize the critical necessity of fostering resilience, particularly in developing regions, as highlighted by Almulhim et al. [12].

Extensive research highlights the crucial role of nature-based solutions, like green infrastructure, in mitigating UHI effects and enhancing adaptation [7,13]. Concurrently, a wealth of studies have diligently endeavored to advance urban resilience [4,14,15,16,17]. As Amegavi et al. illustrates, urban resilience has evolved into a strategic framework, empowering urban systems not only to withstand and respond to extreme events but also to proactively mitigate their impacts [18]. This strategic approach is essential for addressing contemporary environmental challenges, including rapid urbanization, dynamic land-use changes, and the escalating impacts of climate change. Within this framework, Wang et al.’s research on ecosystem resilience aims to strengthen ecological networks to enhance ecological benefits. Using Wuhan as a case study, Wang et al. construct and evaluate ecological networks, select key ecological sources based on landscape connectivity, and analyze resilience characteristics under different scenarios [19]. Recent research underscores “An Eco-Sustainable Design Strategy” as a pivotal approach to climate change adaptation amidst rapid urbanization [7].

Furthermore, Dai et al. highlight that integrating advanced artificial intelligence (AI) technologies into urban planning plays a crucial role in developing climate-adaptive smart cities [20]. This prompts further investigation: How do advanced AI technologies contribute to optimizing urban design for climate adaptation? Can interdisciplinary approaches integrating technological innovations enhance urban resilience? AI-driven optimization techniques, such as evolutionary algorithms, significantly enhance urban design and planning by simulating diverse scenarios and identifying optimal solutions for complex urban challenges, as argued by Zhang et al. [21]. These advanced techniques foster the creation of efficient, sustainable, and resilient urban environments. The substantial impact of urban form and building diversity (low, mid, and high rise) on microclimate dynamics underscores the significance of urban morphology [22]. Consequently, advanced AI technologies facilitate precise analysis and optimization of these morphological elements, thereby enhancing urban climate adaptation strategies [23]. This integration fosters the development of resilient urban environments capable of withstanding and adapting to climate variability.

Propelled by advancing technological capabilities [24] and escalating urban challenges [25], the pursuit of developing smarter and more adaptable cities has gained significant momentum, as emphasized by Ibanescu et al. [26]. Existing literature underscores the diversity of smart city development, shaped by various contextual and institutional factors and examined through mixed methodologies [27]. Consequently, comprehending the urbanization gradient is imperative for the future development of climate-adaptive smart cities. Particularly in Wuhan, China’s sixth-most populous city, highlighting the urgency to address the impacts of urbanization on the environment and human welfare [28]. Despite policies aimed at mitigating environmental degradation since 2005, Wuhan’s rapid urbanization continues to strain its natural resources and exacerbate UHI effects [7]. Hence, there is an urgent imperative to explore sustainable urban design strategies for the climate-adaptive smart cities of tomorrow, balancing development growth with environmental preservation.

Urban redevelopment strategies [29], with potential consequences including the demolition or repurposing of existing buildings, aim to create more sustainable and resilient urban spaces [30,31]. For instance, replacing older buildings with green infrastructure can mitigate UHI effects and enhance urban resilience. Demolishing enterprises may initially impact local economies but can lead to long-term economic benefits such as improved urban infrastructure and increased property values. Social displacement due to urban restructuring underscores the need for community involvement in planning processes to mitigate negative impacts and improve overall livability. While implementing advanced urban resilience strategies involves significant initial costs, the long-term economic benefits, including reduced healthcare costs and enhanced economic stability, justify these investments aligned with Wuhan’s economic policies and China’s sustainable development goals (SDGs) [32,33,34]. The strategic integration of green spaces alongside cutting-edge urban planning practices presents a promising avenue for significantly enhancing urban quality of life and fostering improved health outcomes and well-being among city dwellers.

Finally, this study seeks to address a critical question: What strategies can strengthen megacities’ ecological resilience like Wuhan through strategic urban planning and nature-based solutions? Situated within the broader spectrum of urbanization studies, this research specifically delves into Wuhan’s urban transformation, aiming to advance urban resilience through an integrated interdisciplinary approach tailored for the future development of climate-adaptive smart cities. The study seeks to bridge this critical gap by scrutinizing the primary factors influencing urban microclimates and their formation mechanisms in Wuhan. By rigorously analyzing significant morphological and climatic changes due to rapid urbanization across various scales, from provincial to block levels, this study aims to delineate strategies to mitigate urban heat island (UHI) intensity within the context of future climate-adaptive smart cities. Drawing upon an interdisciplinary framework, the research integrates mathematical and statistical analyses, simulation techniques, remote sensing, and geographic information system (GIS) data. Through long-term observational studies, mathematical and statistical analyses, remote sensing, GIS data, and simulation methods, the study aspires to comprehensively understand the dynamics of urban microclimates and their interaction with land-use changes, from the nascent stages to the apex of urbanization.

Additionally, a novel 360° radial Fibonacci geometric growth (360° RFGG) model is introduced for comprehensive analysis of urban growth trajectories, applicable across macro to micro scales within a city. This innovative approach is being utilized for the first time. Subsequently, through meticulous on-site measurements, the intricate dynamics intertwining urban microclimates with land-use transitions are revealed, thereby illuminating the nuanced interplay between environmental factors and urban development. Seminal works in the domains of urban morphology, LULCTs, climate change, urban resilience and adaptation, urban microclimates, and advanced technologies for smarter urban systems form the theoretical foundation of this study. By synthesizing existing knowledge and harnessing modern technologies, this research aims to explore the intricate relationship between urbanization and environmental sustainability. Aligned with Yang et al.’s emphasis on strategic planning for sustainability and the integration of cutting-edge technologies [35], the research endeavors to offer pioneering perspectives on fostering resilient, climate-adaptive smart cities. This effort not only advances theoretical understanding but also proposes actionable strategies to guide future urban development towards enhanced environmental sustainability.

In essence, this study seeks to uncover the dynamics of urban microclimates in Wuhan and propose evidence-based design strategies for sustainable urban development towards the climate-adaptive smart cities of tomorrow. By systematically addressing the challenges posed by rapid urbanization, the resilience, livability, adaptive capacity, and sustainability of cities can be significantly enhanced amidst evolving environmental conditions. Subsequently, this pioneering interdisciplinary endeavor harnesses the computational power of genetic algorithms (GAs) to distill meaningful relationships from vast data pools. This endeavor not only illuminates the intricate dynamics of urban ecosystems but also highlights the transformative potential in shaping the cities of the future—smarter, more adaptable, and resilient. Furthermore, by deploying an innovative approach that empowers the optimization of pivotal facets of urban morphology, this study exemplifies substantial strides in advancing carbon neutrality strategies. Holistically, this research delivers indispensable insights for stakeholders and urban planners, contributing significantly to sustainable urban development.

2. Materials and Methods

2.1. Study Area

The research focuses on Wuhan, China, a rapidly urbanizing metropolis with a unique environmental and urban landscape, as illustrated in Figure 1a–c. Wuhan, situated at the confluence of the Yangtze and Han rivers, features a diverse topography encompassing 166 lakes and extensive green spaces [7]. This distinctive setting provides an ideal case study to examine the interplay between urbanization and environmental dynamics [19]. Key urban zones [22], representative of varying degrees of urbanization and morphosectors (distinct morphological sectors within the urban fabric), were selected to capture a comprehensive range of microclimatic conditions.

2.2. Data Collection

2.2.1. Meteorological Data and Observational Techniques

Comprehensive meteorological data spanning from 1980 to 2016 were acquired from the Wuhan Meteorological Bureau, providing an extensive dataset for analysis. This dataset includes key parameters such as air temperature, relative humidity, and wind speed, offering a detailed view of climatic conditions over time. The high-resolution temporal and spatial data were crucial for analyzing long-term climatic trends and extreme weather events, providing a rich temporal perspective essential for the analysis. To complement these meteorological insights, a combination of fixed and mobile observational techniques was utilized to monitor urban microclimates [7]. Fixed monitoring stations were strategically positioned across various urban zones to capture continuous environmental data. Additionally, mobile sensing units, equipped with advanced weather sensors and GPS, conducted transect studies, yielding high-resolution spatial data on microclimatic variations within urban blocks. This methodology enabled the meticulous gathering of nuanced environmental data, thereby offering an in-depth and comprehensive insight into the microclimate dynamics across diverse urban landscapes. The apparatus utilized for both fixed and mobile observations is illustrated in Figure 2.

2.2.2. Land-Use and Urban Morphology Data

The research capitalized on land-use and urban morphology data directly obtained from Wuhan’s Municipal Planning Bureau (WMPB), augmented by high-resolution satellite imagery sourced from platforms such as Landsat. The dataset was meticulously classified into 27 distinct land-use categories, including lakes, grasslands, built-up areas, and other structural land types, as illustrated in Figure 6a. This comprehensive dataset facilitated an in-depth analysis of the urban structure and its temporal dynamics. By leveraging such a robust and detailed dataset, the study achieved a granular understanding of the city’s evolving landscape, providing valuable insights into the intricate patterns of urban development.

2.3. Advanced Analytical Tools and Techniques: GIS, CFD, and GA

To thoroughly investigate the complex interactions within urban environments and optimize urban planning strategies, a suite of advanced analytical techniques was employed. These methodologies included geographic information systems (GIS) [36], computational fluid dynamics (CFD) [7], and genetic algorithms (GA) [37]. Each technique provided unique insights into the spatial, thermal, and dynamic aspects of urban environments, collectively contributing to a comprehensive and sophisticated analysis.

2.3.1. Geographic Information Systems (GISs)

GISs were utilized to proficiently manage, analyze, and visualize spatial data, leveraging the powerful capabilities of ArcGIS software version 10.4.1 [10,15,36]. This tool enabled the seamless integration of diverse datasets, the execution of sophisticated spatial analyses, and the creation of intricate maps that vividly illustrated land-use changes, urban morphology, and microclimatic patterns. Through this advanced GIS application, the study provides a nuanced and comprehensive portrayal of the dynamic interactions within the urban environment.

2.3.2. Computational Fluid Dynamics (CFD)

CFD simulations [7] were conducted to model airflow and thermal dynamics within urban environments. Employing the advanced capabilities of ANSYS Fluent software version 15, various urban configurations were simulated to reveal the impact of building typologies on wind flow patterns, temperature distribution, and the UHI effect. These simulations provided a sophisticated understanding of how different architectural designs and urban layouts influence microclimatic conditions, offering critical insights into optimizing urban planning for enhanced environmental sustainability and comfort.

2.3.3. Genetic Algorithms (GAs)

GAs [37] were leveraged to optimize the analysis of complex datasets, developing custom GA scripts using the Grasshopper plugin in Rhino 3D modeling software version 7. This enabled the uncovering of significant relationships between urban form, land use, and microclimatic conditions. The methodology seamlessly adopted the cutting-edge 360° RFGG Model, harnessing the power of the Fibonacci sequence driven by Python code. Figure 8c vividly depicts the Python syntax and calculation method employed in this model, showcasing the sophisticated process behind the innovative approach. This methodology allowed for the extraction of meaningful insights from extensive data pools, enabling the proposal of finely tuned strategies for urban design. By harnessing the adaptive capabilities of GAs, a sophisticated analysis was achieved that informs sustainable and resilient urban planning.

2.4. Methodological Framework

The methodological framework for this study integrates multiple analytical approaches to ensure a comprehensive understanding of urban environmental dynamics:

• Data integration and preprocessing: The data integration process meticulously collated meteorological, land-use, and observational data into a cohesive and unified database. Rigorous preprocessing, including data cleaning, normalization, and interpolation, ensuring consistency and accuracy across diverse datasets. This robust data foundation facilitated seamless amalgamation and laid the groundwork for subsequent analyses.
• Temporal and spatial analysis: Temporal analysis focused on identifying trends and patterns in meteorological data spanning from 1980 to 2016, revealing long-term climatic shifts and transformations. Spatial analysis, utilizing GIS technology, examined the distribution of urban forms and their correlations with microclimatic variations. Together, these integrated analyses were pivotal in clarifying the temporal and spatial dynamics of urbanization. The dual analyses provided deep insights into its environmental impacts and can inform the development of precisely tailored strategies for sustainable urban development.
• Simulation and modeling: CFD simulations were conducted across various scenarios, evaluating different building densities and heights to explore the microclimatic impacts of urban configurations. These simulations illuminated the influence of urban design on airflow dynamics and temperature distribution, contributing to the development of climate-adaptive urban strategies aimed at addressing contemporary environmental challenges.
• Optimization using genetic algorithms (GAs): The GA optimization process involved encoding intricate urban design parameters into a genetic representation, analogous to DNAs (genes), which were meticulously evaluated against microclimatic performance metrics. Through a series of iterative refinement operations, including selection, crossover, and mutation, the GA optimization process identified optimal design configurations adept at mitigating UHI effects and bolstering urban resilience. By utilizing the adaptive capabilities inherent in GA, the study explored potential pathways towards sustainable urban development, where design solutions are crafted to integrate with and enhance the urban fabric.

2.5. Innovation in Analytical Models

360° Radial Fibonacci Geometric Growth (360° RFGG) Model

The innovative 360° RFGG model was introduced to dissect urban growth trajectories. This model, inspired by the organic patterns of natural growth, was adeptly applied to map urban expansion across various scales, encompassing city-wide (macro scale), neighborhood-level (meso scale), and block-level intricacies (micro scale). By leveraging the model’s geometric principles, a sophisticated understanding of urban growth dynamics and their profound environmental ramifications was attained.

2.6. Field Measurements and Validation

To corroborate the veracity of the simulation and modeling outcomes, an exhaustive endeavor of field measurements was undertaken within targeted urban zones. An array of advanced environmental sensors equipped with sophisticated technology was leveraged to meticulously capture a comprehensive dataset encompassing air temperature, relative humidity, and wind speed parameters. These empirical observations were intricately juxtaposed with simulation outputs, facilitating a rigorous validation process to refine the models and ensure their precision. By integrating advanced technologies, interdisciplinary methodologies, and rigorous scientific inquiry, this study aims to offer novel insights into urban microclimates and propose sustainable design strategies for the cities of tomorrow. The research flowchart of the study is represented in Figure 3.

3. Results and Discussions

3.1. Urban Spatiotemporal Dynamics

After conducting a comprehensive analysis of all cities and towns within Hubei Province using ArcGIS, as illustrated in Figure 4, spanning from 1980 to 2016, profound shifts in land use dynamics were unearthed, particularly spotlighting the remarkable urban expansion witnessed in Wuhan. Notably, Wuhan exhibited a conspicuous ring of major changes, emblematic of its substantial urban metamorphosis over the designated timeframe.

From 1980 to 2016, there has been a striking surge in urban land coverage within the surveyed area. The percentage escalation in urban lands from 1980 to 1990 was an impressive 134.57%, surging further to 158.58% by 2000. Subsequently, a significant ascent to 259.40% occurred by 2010, culminating in a momentous expansion to 405.11% by 2016. Moreover, there was a remarkable surge in other construction lands, marking an extraordinary escalation of 1038.32% from 1980 to 2016. This progressive amplification in urban lands and other construction lands underscores the dynamic nature of land transformation processes within the region over the specified timeframe.

Simultaneously, lakes experienced a reduction of 5.38%, a critical component of natural cooling and thermal regulation. The amalgamation of natural landscapes and green spaces—including paddy fields, swamps, woodlands, sparse woodlands, covered grasslands, and high coverage grasslands—also witnessed a substantial decline, amounting to 79.26%. These findings corroborate observations by Wu and Xie [10], underscoring that extensive urban expansion has precipitated significant environmental repercussions. The diminishing presence of natural vegetation exacerbates the UHI effect, highlighting the urgent need for sustainable urban planning practices.

The results align with the insights of Wang et al. [38], who emphasized that the integration of green infrastructure and the preservation of extant natural landscapes are crucial for mitigating these adverse effects and fostering long-term urban resilience. The strategic incorporation of vegetation is vital for urban ecosystems, influencing carbon, water, energy, and material cycles within cities. This integration must also reconcile with the rapid transition from low-rise to high-rise structures during urban growth, ensuring that the benefits of greenery are harmonized with contemporary urban development.

3.2. Urbanization Gradient in Wuhan

Following an extensive examination in Section 3.1, it was found that Wuhan’s urbanization level was significantly higher than that of all other cities in the ring of major changes in Hubei Province (with an area of 202,959.8 km²), comprising Ezhou, Huangshi, Qianjiang, Xianning, Xiantao, Xiaogan, Huanggang, Tianmen, and Wuhan itself (8573 km²), as shown in Figure 5a. Figure 5b visually corroborates this finding, highlighting Wuhan’s urbanization level as the highest among these cities, with a score between 0.80 and 1.00, marked in red to indicate peak urbanization.

Analyzing the data and land use classifications (Figure 6a), which were converted into grid codes (Figure 6b) and quantified in Figure 6c, unveiled Wuhan’s rapid urbanization through a five-step framework: (1) initial development, (2) moderate expansion, (3) significant growth, (4) advanced urbanization, and (5) metropolitan development. Figure 6d succinctly depicts these stages. This progression, from nascent urbanization to its apex, vividly illustrates the urbanization gradient and provides a clear and detailed narrative of Wuhan’s dynamic transformation.

3.3. 360° Radial Fibonacci Geometric Growth: Unveiling Nature’s Secret Blueprint

Employing a sophisticated mesh-based system (Figure 7b) with a resolution of 1 m² (Figure 7d) within a 360° radial Fibonacci geometric growth (360° RFGG) framework, the myriad changes in urban landscapes were meticulously captured and analyzed. This high-resolution grid system, encompassing the entirety of the city map, enabled us to evaluate land cover shifts with remarkable precision. The grid analysis, conducted and shown in Figure 7e, vividly illustrates this capability.

By quantifying urbanization impacts down to the square meter, this approach provides an unparalleled level of accuracy in identifying and characterizing the dominant land-use and land-cover transformations (LULCTs). The 360° RFGG framework proved instrumental in delineating the intricate patterns and directional trends of urbanization, offering an invaluable tool for urban planners and researchers to comprehend and manage the complexities of urban growth.

The exploration delved deeper into urban growth trajectories using the innovative 360° RFGG, unveiling nature’s hidden blueprint, as meticulously illustrated in Figure 8b. This pioneering framework, spanning 16 primary directions from 0 to 15 and encompassing the entirety of city growth, allowed the analysis to pinpoint urban growth trajectories in four distinct directions, as depicted in Figure 8a,b.

Notably, the investigation revealed pivotal urban growth trajectories spanning key districts, including Huangpi, Xinzhou, Caidian, and Jiangxia. These trajectories vividly showcase the burgeoning urban hotspots in the most rapidly growing areas, as depicted in Figure 8a, providing captivating insights into the city’s dynamic evolution. By harnessing the power of the 360° RFGG system, the nuanced interplay between natural and anthropogenic factors driving urban expansion was illuminated. This comprehensive analysis not only underscores the accelerating urbanization in these districts but also highlights the intricate mechanisms behind this growth, offering a profound understanding of the forces shaping the city’s landscape.

This innovative approach, rooted in the principles of the Fibonacci sequence, underscores the fractal nature of urban growth and offers a profound understanding of how cities evolve in harmony with underlying geometrical patterns. The calculation formula and generating process are detailed in Figure 8b,c. The 360° RFGG framework emerges as a critical instrument for urban planners, enabling them to craft more sustainable and resilient urban environments that align with the inherent order of natural growth patterns. The detailed delineation of these trajectories not only highlights the transformative impact of urbanization on the landscape but also provides a scientifically robust foundation for future urban development strategies. This aligns with Ku’s emphasis on the importance of an integrated modeling framework for evaluating planning strategies [39].

3.4. Long-Term Meteorological Analysis: Insights into Wuhan’s Climate Patterns

The detailed examination of Wuhan’s climatic patterns has unveiled the precision of urban growth trajectories delineated through the 360° radial Fibonacci geometric growth (360° RFGG) framework. This validation becomes particularly conspicuous when scrutinizing the weighted emphasis on Jiangxia, as delineated in Figure 7c. The 360° RFGG system’s adeptness in capturing the nuanced dynamics of urban expansion underscores its resilience as a prognostic tool in urban planning and environmental management. As depicted in Figure 9, a comprehensive meteorological analysis spanning from 1980 to 2016 has been executed, validating pivotal urban growth trajectories across pivotal districts. This analysis has incorporated data gleaned from four strategically positioned meteorological stations: Caidian (Station 1), Huangpi (Station 2), Xinzhou (Station 3), and Jiangxia (Station 4). Furthermore, this assessment has corroborated that the preponderance of urban growth gravitated towards the Jiangxia District (Station 4), distinguished by the highest recorded air temperature and the lowest relative humidity, and the dominant wind speed from the north–northeast. Specifically, the air temperature (in Jiangxia is approximately 2.03 °C higher than in Xinzhou, the area with the lowest recorded temperature, while the relative humidity difference is 5.59%. This exemplifies and substantiates that the most pronounced degradation of natural landscapes has ensued concomitantly with urban expansion, with compelling evidence that the majority of land-use and land-cover transformations (LULCTs) have transpired in Jiangxia, covering an extensive area of 2009.14 km², as vividly illustrated in Figure 7c.

3.5. Urban Morphological Analysis: Deciphering Dynamic Landscapes

In this section, a detailed analysis of urban morphological considerations was conducted, concentrating specifically on the Jiangxia District, where the most profound land-use and land-cover transformations (LULCTs) have prominently manifested (Figure 7c). The analysis extended to targeted locations within these regions, where field observations were meticulously undertaken (refer to Figure 7f). By scrutinizing morphosectors, encompassing low-rise, mid-rise, and high-rise buildings, the underlying dynamics of urban development were aimed at being unraveled. Furthermore, the investigation extended beyond built structures to include the integration of sizable green spaces within the urban fabric, in line with the insights of Wang et al. [38]. The study rigorously scrutinized four distinct morphosectors: (I) low-rise buildings, (II) mid-rise buildings, (III) high-rise buildings, and (IV) a sizable green space. The outcomes of this exhaustive mobile assessment of the first three morphosectors (I, II, and III) and the fixed assessment of the green space (IV) are depicted in Figure 10. Additionally, these are delineated as case studies in Figure 7f (and Figure 11a), predominantly delving into temperature and relative humidity across different times of the day—early morning, midday, and nighttime—spanning a seven-day period. The instrumentation utilized in this comprehensive investigation, as detailed in the methodology section and illustrated in Figure 2, facilitated the rigorous collection and analysis of environmental data, ensuring the robustness and precision of the findings.

The results conclusively indicate that urban morphology significantly influences microclimates within all analyzed morphosectors (I, II, III, and IV). Observations from different residential zones (cases I, II, and III) revealed distinct air temperature variations. Notably, in the early morning, case IV (a sizable green space) exhibited temperatures 5.4 °C lower than those in built-up environments (Figure 10). At night, air temperature distribution differed according to block morphology, with higher values in high-density areas (cases I). The study reveals that the greenery ratio and building density are crucial for thermal comfort. For instance, in case II (mid-rise), with a 25% greenery ratio, air temperatures were lower than in cases I (low-rise) and III (high-rise). Additionally, the plot ratio in high-rise buildings significantly impacts heat balance. High-building-density areas with a low greenery ratio and plot ratio show higher temperatures, emphasizing the need for more greenery and open spaces to reduce UHI intensity. These findings underscore the critical role of green spaces in mitigating UHI effects and enhancing urban microclimate resilience.

3.6. CFD Simulation Results

In Figure 12, a concise representation of computational fluid dynamics (CFD) results succinctly illustrates the influence of building diversity and morphology on urban microclimates. The air change rates for low-rise (I), mid-rise (II), and high-rise (III) buildings were 1.2 m/s, 2.4 m/s, and 3 m/s, respectively. High-rise cases enhance air movement, aiding ventilation, while dense, low-rise areas trap heat, increasing air temperature and UHI intensity. For comprehensive documentation of these results, including the detailed setup, boundary conditions, and validation of the CFD models, please refer to the previous works conducted by the authors as referenced [7,22].

The simulations underscore the importance of building density and plot ratio in mitigating UHI intensity. Specifically, they highlight how open spaces and strategic urban design can improve air movement and reduce thermal loads, thereby enhancing urban microclimates and energy efficiency. Integrating green spaces within urban morphosectors is crucial for optimizing these benefits, as it allows for precise enhancement of air quality and temperature regulation. Aligned with the research outcomes, empirical evidence robustly validates the multifaceted advantages of green spaces as a pivotal component of urban landscapes [13]. Urban green spaces offer a wide range of indispensable ecosystem services, such as air purification and temperature regulation [40,41,42]. They also provide recreational amenities that promote physical activity and community engagement [43,44,45]. Moreover, these spaces contribute to aesthetic enrichment by enhancing the visual appeal of urban landscapes [46,47,48]. Importantly, urban green spaces have demonstrated the capacity to enhance both physical and mental health by mitigating stress and fostering overall well-being [41,49,50,51]. They play a critical role in carbon sequestration, helping to mitigate climate change by absorbing and storing carbon dioxide [36,52,53]. Finally, these areas are vital for biodiversity conservation, offering habitats for various species and supporting ecological networks [54,55,56].

3.7. Spatial Optimization Outcomes: Genetic Algorithm-Based Location Analytics

Building upon the preceding findings and discussions, the intricate interplay between morphological configurations and green spaces has been unveiled, accentuating their indispensable role in mitigating the impacts of UHI effects. This correlation closely resonates with the insights of Line et al. [57], particularly concerning the influence of green space morphology on UHI effects. Hence, genetic algorithms (GA) were employed for optimization. Illustrated in Figure 11a, critical elements of urban form, previously identified as morphosectors (I, II, III, and IV), underwent meticulous analysis using a comprehensive 360° RFGG for microscale exploration.

The rigorous examination of the foundational factors influencing urban morphology, outlined in preceding sections (I, II, III, and IV), utilizing a comprehensive 360° RFGG, facilitated thorough analyses spanning both macro and micro scales (Figure 11a). This approach served as the foundation of the study, aiding in the precise delineation of the microscale study area for exploration. Furthermore, as depicted in Figure 11b, this analytical framework played a pivotal role as a critical pre-optimization processing step before the application of GA optimization, a process intricately delineated in Figure 11c. Subsequent to location selection and prior to optimization, the analysis revealed that the green space (IV) was situated at distances of 1133.02 m, 1702.27 m, and 855.07 m from locations I, II, and III, respectively. Subsequently, utilizing GA, the optimal pathways between these locations were identified, thereby demonstrating the robustness and precision of the analytical approach.

The aim was to strategically and intelligently position green spaces to optimize the spatial arrangement of Case IV amidst low-rise, mid-rise, and high-rise buildings to mitigate ambient air temperature. Employing location optimization, the study aimed to counteract urban heat effects originating from areas with elevated ambient air temperatures, such as Case 1, identified by the experimental results as having the highest air temperature (refer to Figure 10). Utilizing green space from Case IV, which exhibited a significantly lower air temperature (reduced by 5.4 °C), it was strategically deployed to effectively counterbalance the heat generated by hotter urban locations. The GA optimization (GAO) process, employing heuristic search algorithms from modeling to optimization and selection, along with the intertwined paths of the optimization process and graph analysis, are thoroughly unveiled in Figure 11c.

As illustrated in Figure 11b, the optimization of pathfinding between study locations through the application of GA entails a rigorous scientific process rooted in evolutionary principles. Central to this methodology are the concepts of genes, mutation, and crossover, which collectively orchestrate the iterative refinement of potential solutions, as detailed in Figure 11d. Genes serve as the elemental units embodying candidate paths between study locations, with each gene representing a sequence of waypoints or nodes. Mutation, a pivotal genetic operator, injects essential diversity into the population of potential solutions by stochastically altering gene sequences. This stochastic perturbation facilitates the exploration of novel paths, thereby enriching the solution space and fostering adaptive evolution. Conversely, crossover, another fundamental genetic operator, amalgamates genetic material from two parent solutions, generating offspring solutions that inherit traits from both parents. This process mirrors the genetic recombination observed in natural selection, stimulating the emergence of increasingly refined solutions. By iteratively applying these genetic operators within the GA framework, the optimization process converges towards an optimal pathfinding solution between study locations, underscored by its robustness, efficiency, and adaptability to complex spatial environments.

The findings depicted in Figure 11d provide a comprehensive overview of the optimization process from Gen.0 (the initiation) to Gen.n (its completion). It commences with a randomly generated population of potential solutions, each meticulously evaluated using a fitness function to ascertain its efficacy in addressing the problem at hand. The algorithm judiciously selects the most promising solutions for reproduction, facilitating the creation of a new generation through genetic recombination and occasional mutations. This iterative process perpetuates, with successive generations progressively refining the solutions until the algorithm converges on an optimal or satisfactory outcome (Figure 11b–d). The efficacy of evolutionary strategies in navigating complex design spaces is thus compellingly demonstrated. Figure 11d further underscores the strategic positioning of green space (Case IV) among various morphosectors (I, II, and III), achieving optimized spatial distances of 909.77 m, 761.50 m, and 850.47 m among low-rise, mid-rise, and high-rise morphosectors, respectively. This highlights the algorithm’s capability to optimize spatial configurations across diverse urban forms.

The GA-optimized site selection and the finalized results, pinpointing the optimal site through the intricate optimization process, are depicted in Figure 12. This figure illustrates the crucial elements that shape urban forms and highlights the efficacy of the GA in determining the most advantageous locations.

Figure 12 illustrates the GA-optimized site selection, underscoring the importance of strategically positioning green spaces to bolster environmental benefits. By determining the optimal location for green spaces, environmental performance is significantly enhanced. The optimized paths between locations facilitate ambient heat mitigation, strategically selecting positions that maximize the cooling effects across all cases. Consequently, relocating the former green site to exert the highest impact on the other examined morphosectors, as shown in Figure 12, is imperative.

The findings, as illustrated, underscore the pivotal role of strategically positioned green spaces in mitigating UHI effects. This observation corroborates Mashagiro et al.’s insights [40] into the profound impact of vegetation on primary ecosystem services within urban environments, thereby augmenting essential ecosystem functions. Furthermore, Benati et al.’s study [41] demonstrates that green spaces considerably enhance human well-being by fostering environmental equality, improving physical and mental health, boosting cognitive development, and strengthening social cohesion, while simultaneously alleviating stress and thermal comfort. Consequently, strategic urban green space planning is indispensable for ensuring equitable access to these multifaceted benefits, thereby promoting environmental justice and holistic public health for all urban inhabitants.

By leveraging the computational capabilities, intricate relationships within the extensive dataset have been deciphered. This innovative interdisciplinary approach not only sheds light on the complex dynamics governing urban ecosystems but also suggests that the findings could significantly influence the future design of cities. These insights pave the way for creating urban environments that are smarter, more adaptable, and resilient, poised to meet the multifaceted challenges of the 21st century.

4. Conclusions

This study presents a comprehensive spatiotemporal analysis of urban expansion and morphological changes in Hubei Province, with a particular focus on Wuhan, spanning from 1980 to 2016. The findings reveal an alarming 405.11% increase in urban land coverage and a staggering 1038.32% rise in other construction areas, juxtaposed with an astounding 79.26% decrease in natural landscapes and green spaces. These significant transformations underscore an urgent need for adopting innovative and sustainable urban planning practices to mitigate UHI effects and preserve essential ecosystem services.

To advance this research, the integration of advanced computational techniques—specifically the 360° RFGG framework and GA—was pivotal. The application of these methodologies has proven instrumental in decoding the intricate patterns and trajectories of urbanization. The 360° RFGG framework, which incorporates natural geometry and fractal growth principles, offered nuanced insights into spatial and temporal dynamics. This approach revealed critical directional trends and growth hotspots essential for effective urban planning. Consequently, these advanced techniques have provided profound insights, enabling the development of effective strategies for sustainable urban growth.

Meteorological and morphological analyses further highlighted the critical role of green spaces in regulating urban microclimates and mitigating UHI effects. Empirical data, corroborated by CFD simulations, demonstrated that green spaces within urban fabrics could reduce temperatures by up to 5.4 °C and increase relative humidity compared to built-up environments. This underscores the critical role of strategic green space planning in enhancing thermal comfort and environmental quality. The findings highlight how urban morphology significantly influences microclimates, with green spaces showing notable improvements in temperature moderation and humidity levels. These insights emphasize the necessity of optimizing urban spatial arrangements through strategic green space integration to effectively mitigate UHI effects.

Nevertheless, the geographic specificity of this study may constrain the generalizability of the findings to other urban contexts. Future research should undertake extensive environmental impact assessments across diverse geographical locations to enhance applicability. Additionally, while the GA utilized demonstrates strong optimization capabilities, its effectiveness is contingent on the complexity and dimensionality of the search space. This challenge is particularly pronounced in intricate and high-dimensional scenarios, necessitating precise parameter tuning to balance exploration and exploitation. Advancements in GA techniques or exploration of alternative methodologies could substantially improve robustness and generalizability. Long-term predictive modeling is also crucial for assessing the sustainability of current urban development trajectories and formulating adaptive strategies for climate change resilience.

In summary, the integration of advanced computational techniques with empirical data, through an interdisciplinary approach, offers profound insights into urban dynamics and sustainability challenges. Strategic integration of green spaces, informed by these insights, is essential for developing resilient urban environments that promote environmental health, well-being, and equitable access to urban amenities and services. The application of this approach, culminating in GA optimization for spatial planning, substantiates its validity through the analysis of empirical data and advanced computational simulations, thereby establishing a scientifically sound basis for future urban development strategies.