A Systematic Review on Fuzzy Decision Support Systems and Multi-Criteria Analysis in Urban Heat Island Management
Abstract
:1. Introduction
1.1. Basic Concepts of UHIs
1.2. Decision Support Systems with Fuzzy Theory and Multi-Criteria Decision-Making
1.3. Research Focus
- What are the research trends in using DSSs and MCA for UHI decision-making?
- How is fuzzy theory used?
- Does the theme develop over time?
- What are the most relevant authors and sources in the research field?
2. Literature Review
2.1. Remote Sensing and Mitigation of UHIs
2.2. Multi-Criteria Decision-Making and Fuzzy Theory Application in UHI Management
2.3. Decision Support System and Fuzzy Logic in UHIs
3. Materials and Methods
4. Results
4.1. Descriptive Analysis
4.2. Sources and Authors Analysis
4.3. Topic Development Analysis
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Nomenclature
AHP | Analytical Hierarchy Process | MACBETH | Measuring Attractiveness by a Categorical-Based Evaluation Technique |
AUHI | Atmospheric Urban Heat Island | MCA | Multi-criteria analysis |
DSS | Decision support system | MCDM | Multi-criteria decision-making |
EFDM | Enhanced Fuzzy Delphi method | SUHI | Surface Urban Heat Island |
GIS | Geographic information system | TOPSIS | The Order of Preference by Similarity to Ideal Solution |
GR | Green roof | UHI | Urban Heat Island |
LCZ | Local Climate Zones | UHII | Urban Heat Island intensity |
LST | Land Surface Temperature | WOS | Web of Science |
LULC | Land use/Land cover | WSM | Weighted Scoring method |
Appendix A
Author(s) | Title | Year | Study Location | Methodology | Findings |
---|---|---|---|---|---|
Nuruzzaman, M. [20] | Urban heat island: causes, effects and mitigation measures-a review | 2015 | Not specified | Review of various measures to encounter UHI effect. | The most effective measures are green vegetation, high albedo materials, and pervious pavements. |
Duplančić Leder, T. et al. [31] | Split Metropolitan area surface temperature assessment with remote sensing method | 2016 | Split, Croatia | Landsat thermal channels have been used to determine the LST. | Microclimate changes and severe changes in LST and UHI effects. |
Amani-Beni, M. et al. [46] | Impact of urban park’s tree, grass and waterbody on microclimate in hot summer days: A case study of Olympic Park in Beijing, China | 2018 | Beijing, China | Observation of the greenery impact on the park during summer days. | The park was 0.48–1.12 °C cooler during the day; increased air humidity was observed at 2.39–3.74%; a reduced human comfort index was used to generate a more comfortable thermal environment. |
Nwakaire, C. M. et al. [61] | Urban Heat Island Studies with emphasis on urban pavements: A review | 2020 | Not specified | Literature review of UHIs, with emphasis on urban pavements. | One of the main findings included using creative designs that can provide cooling without compromising the structural integrity of the pavement, which is a key component of effective UHI mitigation techniques for highway pavements. |
Kim, S. W. et al. [65] | Urban heat island (UHI) intensity and magnitude estimations: A systematic literature review | 2021 | Not specified | Literature review of 51 study dealing with UHI intensity and magnitude estimation. | The current UHI energy models for calculating UHIs must be updated to take into account the city’s three-dimensional physical layout. The literature review demonstrates that UHI research requires the development of an optimal analysis method. |
Almeida, C. R.d. et al. [66] | Study of the Urban Heat Island (UHI) Using Remote Sensing Data/Techniques: A Systematic Review | 2021 | Not specified | Literature review of publications in Scopus and Web of Science on UHI analysis using RS data/techniques and LST, from 2000 to 2020. | The Northern Hemisphere concentrates the majority of studied areas, particularly in Asia (69.94%), so Cfa climate areas are the most represented. Landsat products were most frequently used to estimate LST (68.39%) and LULC (55.96%); ArcGIS (30.74%) was the most frequently used software for data treatment; and correlation (38.69%) was the most frequently applied statistical technique. |
Deilami, K. et al. [67] | Urban heat island effect: A systematic review of spatio-temporal factors, data, methods, and mitigation measures | 2018 | Not specified | Literature review of different spatial and temporal factors affecting the UHI effect. | Ordinary least square regression is the most widely used method (68%) to investigate the relationship between different spatio-temporal factors and the UHI effect, followed by comparative analysis (33%). The most common factors affecting the UHI effect, as reported in the reviewed studies, are vegetation cover (44%), season (33%), built-up area (28%), day/night (25%), population density (14%), and water body (12%), among others. In total, 54% of the studies used Landsat TM images for modeling the UHI effect, followed by Landsat ETM (34%) and MODIS (28%). |
Nimac, I. et al. [68] | The contribution of urbanisation and climate conditions to increased urban heat load in Zagreb (Croatia) since the 1960s | 2022 | Zagreb, Croatia | Two land use/land cover (LULC) scenarios are examined, together with the appropriate climate conditions, using modeling techniques. | The findings show that, with an average increase in summer days of 35 days, climate change has a major impact on the change in total heat load (88%). Changes in LULC have less of an effect (12%), but they have a significant impact on the spatial variability of the heat load. |
Chen, Y. et al. [60] | Numerical simulation of local climate zone cooling achieved through modification of trees, albedo and green roofs—a case study of Changsha | 2020 | Changsha, China | Through the integration of in situ measurements and ENVI-met numerical simulations, it constructed and modeled 39 scenarios and analyzed the cooling impacts of different cooling factor combinations. | The findings demonstrate that, in three LCZs, an increased albedo and more trees are more effective than green roofs at lowering summer potential temperatures at street level (2 m high); the effects of cooling factors differ depending on the LCZ class, with an increase of 60% in trees resulting in lower outdoor temperatures; the application of combined cooling methods can cause an increase in air temperature (up to 0.96 ◦C). |
Duplančić Leder, T. et al. [69] | Land Surface Temperature Determination in the Town of Mostar Area | 2018 | Mostar, Bosnia and Hercegovina | The atmospheric corrections and LST were calculated using accessible meteorological data and Landsat 5, 7, and 8 satellite images. | Mostar is a town where the maximum land surface temperatures can be expected, since it has LST values of more than 50 °C, which are documented in this study. |
Duplančić Leder, T. and Bačić, S. [70] | The influence of local climate zones on the thermal characteristics of the city of Split | 2021 | Split, Croatia | Local climatic zoning (LCZ) and its occurrence with the zones of the highest urban temperatures are applied. | Water surfaces and greenery reduce the intensity of UHIs. |
Estacio, I. et al. [71] | GIS-based mapping of local climate zones using fuzzy logic and cellular automata | 2019 | Quezon City, Philippines | Automated GIS-based methodology for determining LCZs and Landsat 8 were used. The membership percentage of 100 m cells to an LCZ type was determined using fuzzy logic. To extract the LCZ map from the fuzzy layers, cellular automata were used. | The findings indicate that five out of seven land cover LCZs and seven out of ten built-up LCZs were detected. Every LCZ type had temperatures that matched those reported in the literature, according to LST data obtained from Landsat 8. |
Bokaie, M. et al. [72] | Hosseini, A. Assessment of urban heat island based on the relationship between land surface temperature and land use/land cover in Tehran | 2016 | Tehran, Iran | The relationship between LST and LULC was studied using Landsat TM satellite images. Vegetation and greenery in different areas were studied using the normalized vegetation index (NDVI), using remotely sensed data. | The findings demonstrated that the causal agent of the UHIs produced in Tehran is distinct. This discrepancy illustrates the strong correlation between land surface temperature and land cover and is mostly caused by the state of LULC in the area. |
Jiménez-Muñoz, J. C. and Sobrino, J. A. [73] | A generalized single-channel method for retrieving land surface temperature from remote sensing data | 2003 | Not specified | A single-channel algorithm is developed that can be applied to different sensors onboard a satellite to estimate LST. | The thermal channels with effective wavelengths close to 11 μm yield the greatest findings; for AVHRR-4, ATSR2-11, and TM-6, root mean square deviation values of 1.6 K, 2 K, and 1.3 K have been found. For AVHRR-5 and ASTR2-12, thermal channels with an effective wavelength close to 12 μm yield an inferior performance, with root mean square deviation values greater than 2 K. |
Feizizadeh, B. and Blaschke, T. [74] | Examining urban heat island relations to land use and air pollution: Multiple endmember spectral mixture analysis for thermal remote sensing | 2013 | Tabriz, Iran | Spectral Mixture Analysis and Endmember Remote Sensing Indices are combined to calculate LST, detect UHIs, and look into the connections between UHI, LULC, and air pollution. | The findings show that LULC has a significant impact on LST and that LST and LULC are intimately related to UHIs. |
Zhu, W. et al. [75] | How to measure the urban park cooling island? A perspective of absolute and relative indicators using remote sensing and buffer analysis | 2021 | Jinan, China | The land cover of parks was determined using high-spatial-resolution GF-2 pictures, and the thermal environment was examined using buffer analysis on Landsat 8 TIR photos. To investigate the connections between the park cooling island and park attributes, linear statistical models were created. | The average park cooling area was roughly 120.68 ha and the average land surface temperature (LST) of urban parks was found to be around 3.6 °C lower than that of the study area. The biggest temperature differential, of 7.84 °C, occurred during summer daylight. |
Firozjaei, M.K. et al. [76] | Land Surface Ecological Status Composition Index (LSESCI): A novel remote sensing-based technique for modeling land surface ecological status | 2021 | 14 test sites | The Landsat multi-temporal imagery, National Land Cover Database (NLCD), and Imperviousness and High Resolution Layer Imperviousness (HRLI) datasets were used. Improved Ridd’s conceptual Vegetation–Impervious–Soil triangle model was used. Combination of the Biophysical Composition Index (BCI) information and LST was applied. | Over the past 20 years, the mean values of the Remote Sensing-based Ecological Index (RSEI) and Land Surface Ecological Status Composition Index (LSESCI) have grown, which is indicative of the detrimental effects of Anthropogenic Destructive Activities (ADAs) on the ecosystem. Nevertheless, there was a considerable difference in the modeling findings of LSES derived from the RSEI and LSESCI. The LSESCI showed a stronger association with the moisture, dryness, heat, and greenness indices than with the RSEI. |
Zhang, Z. et al. [77] | Evaluating Natural Ecological Land Change in Function-Oriented Planning Regions Using the National Land Use Survey Data from 2009 to 2018 in China | 2021 | China | Natural ecological land types were classified into forest, grassland, wetland, and bare land using land use data from the National Land Use Survey from 2009 to 2018. The changes in natural ecological land types in the major function-oriented zones between 2009 and 2018 were then discussed. | Between 2009 and 2018, there was a general downward tendency in the amount of natural ecological lands. However, this trend was reversed after 2015, when regulations for environmental conservation and ecological projects were put into place. |
Zhang, T. et al. [78] | Assessing the Urban Eco-Environmental Quality by the Remote-Sensing Ecological Index: Application to Tianjin, North China | 2021 | Tianjin, China | Methodology for the new RSEI is proposed. | The case study’s findings demonstrate that seasonal variability affects both the contributions of RSEI indicators to eco-environment quality and RSEI values. To account for this seasonal variability, differentiating the normalization of indicator measures and utilizing more representative remote-sensing images should be used. |
Jiang, Y. and Lin, W. [79] | A Comparative Analysis of Retrieval Algorithms of Land Surface Temperature from Landsat-8 Data: A Case Study of Shanghai, China | 2021 | Shanghai, China | A variety of algorithms, including the Radiative Transfer Equation (RTE), Mono-Window Algorithm (MWA), Split-Window Algorithm (SWA), and Single-Channel Algorithm (SCA), have been developed for the purpose of extracting LST from satellite imagery. | The outcomes demonstrated that all four algorithms were capable of recovering LST with reasonable success and that the LST retrieval outcomes were largely constant throughout a given spatial scale. The SWA is better suited for recovering LST in Shanghai in the summer, when the city experiences extremely high humidity and temperatures. |
Peng, T. et al. [80] | Temporal and Spatial Variation of Anthropogenic Heat in the Central Urban Area: A Case Study of Guangzhou, China | 2021 | Guangzhou, China | Using Landsat data and the surface energy balance equation, the urban anthropogenic heat flux (AHF) in the central Guangzhou urban area in 2004, 2009, 2014, and 2020 was retrieved. The temporal and spatial characteristics of various anthropogenic heat types were investigated by combining the transfer matrix and the migration of the gravity center. | The findings show that, while different types of anthropogenic heat had different characteristics in terms of area expansion and spatial changes, the overall change trend of anthropogenic heat in Guangzhou’s central urban area was enhanced, with the degree of enhancement being related to the type of urban functional land. |
You, M. et al. [81] | Quantitative analysis of a spatial distribution and driving factors of the urban heat island effect: a case study of Fuzhou central area, China | 2021 | Fuzhou, China | The application of Moran’s I and hot-spot analysis helped to clarify the geographical pattern of UHI in the center area of Fuzhou, China. | According to the findings, the LST displayed a gradient layer structure with low temperatures in the northwest and high temperatures in the southeast that was significantly correlated with industrial zones. Moreover, soil moisture (impact = 0.792) > NDBI (influence = 0.732) > MNDWI (influence = 0.618) > NDVI (influence = 0.604) were the four parameters that had the biggest influence (q-Value) on LST. |
Despini, F. et al. [82] | Urban surfaces analysis with remote sensing data for the evaluation of UHI mitigation scenarios | 2021 | Modena, Italy | The Worldview3 sensor (WV3) collected satellite photos, which were then processed to identify the various types of urban surfaces and to determine each surface’s albedo value. | The five scenarios that comprised the mitigation interventions assumed the replacement or retrofitting of specific urban surfaces, such as parking lots, rooftops, and roadways, with materials that reflect solar radiation. From the standpoint of public administration, scenarios 4 and 5 were shown to be the most favorable, with advantages for the general welfare of the populace. |
Santos, L. G. et al. [83] | Climate-informed decision-making for urban design: Assessing the impact of urban morphology on urban heat island | 2021 | Singapore | A novel framework for urban design that takes climate change into account is carried out. With the help of this framework, various options can be impartially examined, in order to assess various scenarios. It offers a foundation for doing so. The Data Processing Unit (DPU), the Climate Model, and the Analysis are the three primary components of the framework. | The outcome is a matrix of scores for each scenario that shows which design performs greatest on predetermined measures. The scenarios that include the tallest buildings (81 m) and either a medium (40%) or low (30%) density provide a good compromise between the external trade-offs associated with the environmental effect and the number of people assigned to each site area, as well as the energy costs associated with running the district. |
Amani-Beni, M. et al. [84] | Impacts of urban green landscape patterns on land surface temperature: Evidence from the adjacent area of Olympic Forest Park of Beijing, China | 2019 | Beijing, China | This study investigates the connection between the surrounding areas’ cooling effect and urban greening trends. | According to the findings, waterbodies and forests might decrease temperatures by 12.82% and 6.51%, respectively, below those of the impermeable surface. |
Pritipadmaja, Garg, R. D. and Sharma, A. K. [85] | Assessing the cooling effect of blue-green spaces: implications for Urban Heat Island mitigation | 2023 | Bhubaneswar, India | Google Earth Engine (GEE) was used to interpret satellite photos and generate LST data for the blue–green spaces. The presence and features of these blue–green regions were measured using the Normalized Difference Vegetation Index (NDVI) and the Modified Normalized Difference Water Index (MNDWI). | Significant geographical variations in the LST were found, with lower temperatures in the blue–green spaces and higher temperatures in built-up and bare land areas. Furthermore, a correlation study demonstrated the built-up index’s (NDBI) significant influence on the LST, highlighting the effect of urbanization on regional climate dynamics. |
Dong, J. et al. [86] | Quantitative study on the cooling effect of green roofs in a high-density urban Area—A case study of Xiamen, China | 2020 | Xiamen Island, China | Using Landsat 8 remote sensing images from the summers of 2014 and 2017, the relative difference between the average land surface temperature (LST) of Xiamen Island and the green roofs was estimated in geographic information systems (GISs), to illustrate the cooling effect of the green roof project. | Results showed that the average LST difference between green roofs and Xiamen Island decreased by 0.91 °C, indicating that green roofs could effectively alleviate UHI effects in high-density urban areas; the cooling effect was significant up to 100 m from the green roof installation in Xiamen Island, known as the characteristic cooling buffer zone; regression analysis revealed that for every 1000 m2 increase in green roof area, the average LST of the roof and its characteristic cooling buffer zone decreased by 0.4 °C. |
Imran, H. M. et al. [87] | Effectiveness of green and cool roofs in mitigating urban heat island effects during a heatwave event in the city of Melbourne in southeast Australia | 2018 | Melbourne, Australia | The non-hydrostatic regional climate models (RCMs) model employed in this study is called WRFv3.8.1 and it has been used extensively in studies on urban meteorology. | As a result of raising the green roof fractions from 30% to 90% and the cool roof albedo from 0.50 to 0.85, the maximum roof surface UHI is lowered by 1 °C to 3.8 °C and 2.2 °C to 5.2 °C during the day, according to the results. Cool roofs can reduce UHIs by up to 1.4 °C, making them more effective than green roofs, in this regard. |
Sanchez, L. and Reames, T. G. [88] | Cooling Detroit: A socio-spatial analysis of equity in green roofs as an urban heat island mitigation strategy | 2019 | Detroit, USA | The most vulnerable places for the ecosystem services provided by green roofs, as well as the demographic makeup of the populations that make up these regions, were identified using spatial data on land surface temperature, income, and race. | The majority of low-income residents are within walking distance of cooling centers, but are not included in the Detroit Future City Urban Green Neighborhoods, according to an analysis of the study’s spatial data; in contrast, green roofs were found in the wealthy, primarily white area of Detroit’s urban core. |
Gagliano, A. et al. [89] | A multi-criteria methodology for comparing the energy and environmental behavior of cool, green and traditional roofs | 2015 | Mediterranean | Three types of roofs are compared numerically in terms of their energy and environmental performance—standard roofs (SRs), cool roofs (CRs), and green roofs (GRs). | Consequently, it is discovered that cool and green roofs offer greater environmental and energy savings than typical, heavily insulated roofs. For instance, under the usual Mediterranean climate, green roofs with minimal insulation performed the best in terms of mitigating UHIs. |
Gunawardena, K. R. et al. [90] | Utilising green and bluespace to mitigate urban heat island intensity | 2017 | London, UK | This research offers a meta-analysis of the main influences that green space and blueprint have on the temperatures of the urban canopy and boundary layer, as viewed from the angles of urban climatology, city planning, and climate science. | This research indicates that tree-dominated greenspace provides the greatest heat stress alleviation when it is most needed and that the evapotranspiration-based cooling influence of both green and blue space is primarily significant for urban canopy layer circumstances. |
Guo, G. et al. [91] | Complex mechanisms linking land surface temperature to greenspace spatial patterns: Evidence from four southeastern Chinese cities | 2019 | Guangzhou, Foshan, Dongguan, and Shenzhen, China | Greenspace data were taken from 0.5 m resolution photos and LST was estimated using Landsat 5/8 summer and winter images. | The findings showed that LST is systematically impacted by the spatial composition and layout of greenspace. The size and importance of these associations, however, differed greatly. Particularly during the summer, the combined contributions of the greenspace landscape measures were more important in determining LST than their individual contributions. |
He, X. et al. [92] | Observational and modeling study of interactions between urban heat island and heatwave in Beijing | 2020 | Beijing, China | The Advanced Research version WRF (ARW) model is used in conjunction with a multilayer urban canopy model (Building Environment Parameterization, or BEP) and a sub-model for indoor–outdoor heat exchange (Building Energy Model, or BEM) to simulate the spatiotemporal variations of UHIs over the course of a heatwave period. | The findings indicated that higher surface evapotranspiration differences between urban and rural areas are the primary source of heatwave-related urban heat island augmentation during the day; whereas, increased anthropogenic heat and improved warm advection are the primary causes of this enhancement at night. |
Semenzato, P. and Bortolini, L. [93] | Urban Heat Island Mitigation and Urban Green Spaces: Testing a Model in the City of Padova (Italy) | 2023 | Padova, Italy | At a height of two meters above the ground, temporal trends, variations in air temperature, and their spatial distribution were all simulated using the i-Tree Cool Air model. The model is a component of the USDA Forest Service’s i-Tree Hydro+ software suite, which consists of process-based environmental models. | The findings indicate that there are temperature variations between green spaces shaded by trees and urban open places with impermeable cover (streets and squares) of up to nearly 10 °C. In metropolitan areas with sealed surfaces, the simulated night time air temperature is 4.4 °C higher than the reference station readings, although it is only marginally colder in areas with tree cover. |
Zheng, T. et al. [94] | A Novel Urban Heat Island Mitigation Strategies-Engaged City Scale Building Energy Consumption Prediction Workflow: Case Study and Validation | 2022 | Osaka, Japan | The urban weather generating workflow, 3D urban model, data-driven modeling, urban energy consumption modeling, and simulation outcome comprise the five steps of this workflow. | According to the findings, the cool pavement approach is the best one, since it minimizes the rise in heating energy use (4.86%), maximizes the reduction in total urban operational energy use (0.29%), and is insensitive to the embodied energy and CO2 emissions of buildings. It also maximizes the reduction in cooling energy use (2.57%). Therefore, increasing the pavement’s albedo, or “cool pavement”, is the best way to reduce urban heat island effects in Osaka city. |
Sangkakool, T. et al. [95] | Prospects of green roofs in urban Thailand–a multi-criteria decision analysis | 2018 | Thailand | This paper deals with identifying and quantifying the key factors that influence green roof adoption in Thailand, which was achieved through a combination of SWOT analysis and AHP. | The key factors were quantified and it was concluded that builders, architects, and planners should be further educated and encouraged to utilize green roofs, since specific technical knowledge is not yet widespread. |
Qi, J. et al. [96] | A decision-making framework to support urban heat mitigation by local governments | 2022 | Not specified | A combination of the best UHI mitigation strategies for the urban context is selected using ontology-based knowledge, sensitivity analysis, and genetic algorithm-based multi-objective optimization. | The paper presents a novel framework that enables automated decision-making for choosing optimal combination of UHI mitigation strategies, using AI. |
Sangiorgio, V. et al. [97] | Comparative Analysis and Mitigation Strategy for the Urban Heat Island Intensity in Bari (Italy) and in Other Six European Cities | 2022 | Bari (Italy) and in another six European cities | Using the recently developed index-based approach, Iuhii, this paper assesses the absolute maximum UHI intensity in Bari and suggests various mitigation strategies. Secondly, comparative evaluation of seven European cities is given. | The Bari districts most vulnerable to high intensity UHI levels were identified. Good examples of reducing the phenomenon in other cities are found through comparative analyses. For a city in Puglia, practical mitigation scenarios are put forth for the first time. |
Temizkan, S. and Kayili, M. T. [98] | Investigation of proper material selection for rainwater harvesting in squares having higher urban heat island effect potential: KBU Social Life Center example | 2021 | Turkey (Demir Çelik Campus) | Finding the best cover designs that encourage green spaces and reduce heat island effects was the aim of the project. The PROMETHEE method was utilized to identify a material that is significantly optimal for use in the cover. | This study suggested a top cover to mitigate the effects of the heat island that was observed in the KBU Social Life Center Square, which can be described as a large heat island on campus, and to collect rainwater over a large area of the square. |
Qureshi, A. M. and Rachid, A. [99] | Comparative Analysis of Multi-Criteria Decision-Making Techniques for Outdoor Heat Stress Mitigation | 2022 | Not specified | Eight MCDM approaches were compared in order to determine which measurements would be best for mitigating urban heat under particular conditions. The models were used with weighting criteria that came from the direct weighting method and the AHP. | The outcomes demonstrate that for every normalization technique, WSM and PROMETHEE produced dependable and consistent results. The frequency of consistent ranking was enhanced by the combination of AHP and applied MCDMs, with the exception of ELECTRE-NS. |
Teixeira, D. C. F. and Amorim, M. C. D. C. T. [100] | Multicriteria Spatial Modeling: Methodological Contribution to the Analysis of Atmospheric and Surface Heat Islands in Presidente Prudente, Brazil | 2022 | Presidente Prudente, Brazil | In order to analyze AUHIs, researchers combine spatial data from a multi-criteria model, based on multiple linear regression, with air temperature data, including relief and land use. The surface temperature (Landsat) is compared to the estimated and measured air temperatures. | The study demonstrates that vegetation lowers atmospheric temperatures, but urban surface materials are the primary energy sources influencing heat transfer. Precise mappings such as the one proposed is important for decision-making and planning measures. |
Turhan, C. et al. [101] | An Integrated Decision-Making Framework for Mitigating the Impact of Urban Heat Islands on Energy Consumption and Thermal Comfort of Residential Buildings | 2023 | Vilayetler Evi Zone (VEZ) in Izmir/Turkey | Research proposes an integrated framework that combines hybrid microclimates and building energy performance simulations (in order to investigate four different strategies for UHI mitigation) with MCDM. | The findings demonstrated that, of all the mitigation strategies, the application of vegetation—such as green roofs and replacing existing trees with ones with a high leaf area density—ranks highest. |
Tabatabaee, S. et al. [102] | An assessment model of benefits, opportunities, costs, and risks of green roof installation: A multi criteria decision making approach | 2019 | Malaysia | This paper proposed a framework for evaluating the key benefits, opportunities, costs, and risks of GR installation and their mutual dependence, using EFDM and the fuzzy-DEMATEL method. | Among the identified benefits, opportunities, costs, and risks, storm water management, contribution to mitigation of urban heat islands, additional structural support, and irregular maintenance were identified as being the most influential. It was also demonstrated that, in order to fully benefit from GR installation, the most influential benefits and opportunities should receive the majority of attention rather than the most important. |
Sturiale, L. and Scuderi, A. [103] | The role of green infrastructures in urban planning for climate change adaptation | 2019 | Catanya, Italy | This framework integrates participatory planning with social multi-criteria evaluation framework for assessing how the public views urban green spaces from a social perspective. | This study demonstrates an intriguing possibility for the broader application of Multi-Critical Social Assessment in green space governance, due to its capacity to incorporate ecological, social, and economic values. |
Rosasco, P. and Perini, K. [104] | Selection of (green) roof systems: A sustainability-based multi-criteria analysis | 2019 | Not specified | This paper uses a sustainability-based multi-criteria analysis to compare a traditional solution with a greening system, in order to investigate the factors that influence choice of the building roof systems. | When choosing between a green roof and a traditional roof, performance, thermal insulation qualities, roof protection, and system weight are the most important factors to consider. |
Rosenzweig, C. et al. [106] | Mitigating New York City’s Heat Island with Urban Forestry, Living Roofs, and Light Surfaces | 2006 | New York, USA | This study compares observed meteorological, satellite, and GIS data with a regional climate model (MM5) to assess how various mitigation strategies affect surface and near-surface air temperatures in New York. | The study’s findings indicate that there is a significant amount of variation in the mitigation strategies’ effects amongst scenarios, case study locations, and heatwave days and that the application of these strategies needs to be adapted to the local conditions. |
Rabbani, G. et al. [107] | Multi-criteria modeling for land suitability evaluation of the urban greenbelts in Iran | 2021 | Iran | This study proposes multi-criteria spatial modeling for a land suitability evaluation of the urban greenbelt in 25 major cities of Iran. | The rise in the environmental change index values over time suggests that new, appropriate urban green belt plans with updated regulations and best practices are required to stop the degradation of urban land use. |
Mushtaha, E. et al. [108] | A study of the impact of major Urban Heat Island factors in a hot climate courtyard: The case of the University of Sharjah, UAE | 2021 | University of Sharjah, UAE | The Analytical Hierarchy Process (AHP) was used to rank the UHI factors. ENVI-met (4.4.2.) simulation software was used to test and evaluate each factor. | Air movement is the most effective element in the environmental category, according to the data, with a value of 0.283, while urban greenery is the most effective factor in the urban category, with a recorded value of 0.317. |
Moradi, B. et al. [109] | A scenario-based spatial multi-criteria decision-making system for urban environment quality assessment: case study of Tehran | 2023 | Tehran, Iran | The AHP method was used to determine the weight of the effective criteria. The approach known as ordered weighted averaging (OWA) was then applied. | The study’s findings demonstrated that a sizable portion of the local populace lives under unacceptable UEQ settings, highlighting the need to improve the situation. |
Meerow, S. and Newell, J. P. [110] | Spatial planning for multifunctional green infrastructure: Growing resilience in Detroit | 2017 | Detroit, USA | The Green Infrastructure Spatial Planning (GISP) model is presented, which is a multi-criteria, GIS-based method that includes the following benefits: air quality, storm water management, green space, urban heat island mitigation, and landscape connectivity. | The analysis offers preliminary evidence that green infrastructure is not being placed in storm water abatement high-priority locations, much less to mitigate the effects of urban heat islands, enhance habitat connectivity, or improve air quality. But, as the Detroit GISP model shows, it might be built in areas that reduce air pollution, urban heat islands, and storm water all at the same time. |
Mahmoudzade, H. et al. [111] | Evaluating urban environmental quality using multi criteria decision making | 2024 | Tabriz, Iran | This study used a multi-criteria decision-making (MCDM) approach that used CRITIC to assess the quality of the urban environment using spatial analysis. | Air pollution, green space per capita, recreational space, and health care per capita do not seem to differ significantly, on average. In two urban areas, the creation of environmental quality maps highlights the significance of relevant neighborhood-level variables. |
Lassandro, P. and Di Turi, S. [112] | Multi-criteria and multiscale assessment of building envelope response-ability to rising heat waves | 2019 | Bari, Italy | The selected approaches are examined in three distinct cities with rising summer temperatures, using an integrated, multilevel analysis with EnergyPlus and ENVI_met. Applying a multi-criteria analysis based on the discovered indicators, the final comparative analysis is conducted. | The results demonstrate the value of employing a multiple criterion analysis and a double-level approach when assessing how well-suited various building envelope retrofitting methods are to handle the escalating frequency of heat waves. Green solutions with high albedo values were the most responsive. |
Kotharkar, R. et al. [113] | A systematic approach for urban heat island mitigation strategies in critical local climate zones of an Indian city | 2020 | Nagpur, India | Determination of the LCZ criticality using The Order of Preference by Similarity to Ideal Solution (TOPSIS) technique. | In the older, unplanned settlement with dense urban agglomeration (LCZ 3), the application of cool roof shows significant reductions in air temperature; however, increasing the green area ration is found to be a more effective strategy for the sparsely built (LCZ 9) and planned settlement with open spaces (LCZ 3F). |
Putra, M. I. J. et al. [114] | Spatial Multi-Criteria Analysis for Urban Sustainable Built Up Area Based on Urban Heat Island in Serang City | 2019 | Serang, Indonesia | A simulation model was developed with a sustainable principle, carried out by performing equal weighting on each factor so that the obtained value and spatial distribution from the appropriate area was obtained and was suitable for the development of the built-up area in Serang City. | The region’s suitability area of 3.313 Ha was determined using the results of SMCE modeling. This area was distributed at various locations in Serang City and was expanded linearly along the road network to cover 3.538 Ha. In the meantime, the majority of Serang City’s central, north, and west regions—which together account for 19.553 Ha—are unsuitable for a built-up area. |
Yan, Y. et al. [115] | A multi-criteria evaluation of the urban ecological environment in Shanghai based on remote sensing | 2021 | Shanghai, China | Development of an ecological environment evaluation method, based on remote sensing and a projection pursuit model. | In order to assess the urban ecological environment in a more convenient and objective manner, remote sensing-based detection has been used. Next, a multi-criteria assessment approach has been suggested for the analysis of the ecological environment, based on a projection pursuit model. Finally, a thorough assessment of Shanghai City’s ecological environment changes over the last five years was conducted. |
Teotónio, I. et al. [116] | Decision support system for green roofs investments in residential buildings | 2020 | Lisbon, Portugal | The proposed methodology is based on the MACBETH method and determines the green roof option with the best trade-off between costs and benefits, in agreement with the preferences of the users/investors. | With a score of 69.43 out of 100, the methodology application selects the intensive green roof as the optimal alternative. The results of the sensitivity and robustness analyses supported the best option conclusions. This strategy maximizes building retrofitting, while assisting in the decision-making process regarding green roofs, as well as enabling sound and well-informed urban planning decisions. |
Sangiorgio, V. et al. [117] | Development of a holistic urban heat island evaluation methodology | 2020 | European cities | Development of a new index aimed at quantifying the hazard of the absolute maximum UHI intensity in urban districts during the summer season, by taking all the parameters influencing the phenomenon into account. | The results are obtained by utilizing three interrelated techniques, as follows: an optimization process, using a Jackknife resampling approach to calibrate the index by utilizing the effective UHI intensity measured in a total of 41 urban districts and 35 European cities; the Analytic Hierarchy Processes to analyze the parameters involved in the UHI phenomenon; and a state-of-the-art technique to acquire a large set of data. |
Philipps, N. et al. [118] | Urban Heat Island Mapping Based on a Local Climate Zone Classification: a Case Study in Strasbourg City, France | 2022 | Strasbourg, France | Establishment of an LCZ classification for the city of Strasbourg by using a vector-based method that relies on a large vector database composed of land cover and cadastral parcels data. | The obtained final LCZ map demonstrates that the devised vector-based approach enables the acquisition of pertinent LCZ classification. The values of the LCZ parameters are then utilzsed to calculate a multiple linear regression (MLR), with the goal of obtaining an UHI intensity for every LCZ polygon. The great regional heterogeneity of the phenomena is accurately illustrated in the resulting UHI map of the Eurométropole de Strasbourg (EMS). |
Mostafa, E. et al. [119] | Urbanization Trends Analysis Using Hybrid Modeling of Fuzzy Analytical Hierarchical Process-Cellular Automata-Markov Chain and Investigating Its Impact on Land Surface Temperature over Gharbia City, Egypt | 2023 | Gharbia, Egypt | The development of two comparable models for the simulation of spatiotemporal dynamics of land use in the study area—CA–Markov chain and FAHP–CA–Markov chain hybrid models. | The two comparable models were validated using the Jaccard coefficient, which confirmed that both models are valid and can be utilized for future predictions. In contrast to the hybrid FAHP–CA–Markov chain model, which can predict urban sprawl-prone sites in a methodical manner, while taking the dynamics driving urban growth into account, the results of the classic CA–Markov chain model were more haphazard. |
Kazak, Jan K. [25] | The use of a decision support system for sustainable urbanization and thermal comfort in adaptation to climate change actions—The case of the Wrocław larger urban zone (Poland) | 2018 | Wrocław, Poland | The development of a decision support system that can be used for the assessment of areas, in relation to their potential exposure to the UHI effect. | The scenario that is least susceptible to UHI impacts is revealed by the computations. The study gives local government officials recommendations on where to concentrate their efforts to build more environmentally friendly urban structures and improve their ability to withstand extreme weather conditions and climate change. |
Mostofi, N. et al. [120] | Developing an SDSS for optimal sustainable roof covering planning based on UHI variation at neighborhood scale | 2021 | Tehran, Iran | The development of a spatial decision support system (SDSS) to investigate the effect of parcels’ roof covering type on surface heat island (SHI) values and their variation, at the neighborhood scale. | The two primary components of the present research’s innovation, the SDSS, are determining the study area’s UHI value and selecting the best set of parcels to replace their roof coverings with flagstone, high-albedo material, or three different types of vegetation. The findings show that, because flagstone and high-albedo materials have local impacts on reducing the UHI effects, it is important to inhibit UHI effects at the study area boundary with plant roof covering, in order to control UHI in the center of the region. |
Bathaei, B. [121] | Decision Support System to Select the Most Effective Strategies for Mitigating the Urban Heat Island Effect Using Sustainability and Resilience Performance Measures | 2021 | Not specified | The development of a decision support system (DSS) to assist decision-makers in reducing the effects of UHIs, by allowing them to choose the most viable mitigation method/technique based on resiliency and sustainability concerns. | The study’s contribution is the creation of a DSS that resembles a knowledge-sharing platform, to assist decision-makers, policymakers, urban planners, and architects in extracting UHIMSs. A more resilient and sustainable design is the anticipated outcome in a broad sense. The groundwork for developing a dynamic computer-based decision support system (DSS) to choose the most effective UHIMSs is laid by this study. |
Tuczek, M. et al. [123] | Toward a decision support system for mitigating urban heat | 2019 | Not specified | The development of a DSS with an optimization model at its core, which optimizes the corresponding urban areas with respect to the highest possible revenue, while maintaining an optimal building/vegetation balance. | To address the identified research gap and to address the research question posed, the best solution would likely be an optimization model that provides a thorough discounted cash flow calculation in the future and takes into account a range of economic factors, including site-specific land prices, electricity costs for energy demand, and air conditioning, as well as a variety of potential alternatives for building and vegetation types. |
Qureshi, A. M. and Rachid, A. [124] | Review and Comparative Study of Decision Support Tools for the Mitigation of Urban Heat Stress | 2021 | Not specified | Review of different decision support tools for mitigating UHI. | This review demonstrates how MCA can be applied to any research area and how criteria can be modified to fit the specific area. A comparative analysis leads to the conclusion that the DSS tool meets many requirements for mitigating UHIs. |
Mahdiyar, A. et al. [125] | A prototype decision support system for green roof type selection: A cybernetic fuzzy ANP method | 2019 | Kuala Lumpur, Malaysia | The development of a decision support system (DSS) for selecting the optimum type of GR for residential buildings in Kuala Lumpur. | During the project’s design phase, a number of interrelated selection criteria for GR types were taken into account. This study’s unique contribution, from the perspective of MCDM, is the prototype DSS that was created by employing a CFANP technique to automate the complete GR type selection decision-making process. |
Werbin, Z. et al. [62] | A tree-planting decision support tool for urban heat island mitigation | 2020 | Boston, USA | The development of a Boston-specific Heat Vulnerability Index (HVI). | Researchers from Boston University examined the City of Boston’s strategy for expanding the urban canopy, because the city has encountered challenges in establishing a long-lasting growth. They determined what barriers there were to applying scientific information to tree-planting decisions and collaborated with the city to create a tool that would help with the process. The tool itself is replicable or adaptable to other cities and the process of development offers a model of a fruitful collaboration between academia and the public sector. |
Acosta, M. P. et al. [63] | How to bring UHI to the urban planning table? A data-driven modeling approach | 2021 | Montreal, Canada | The development of a decision support tool for the modeling of UHIs at the street level. | The methodology for creating a decision assistance tool for street-level UHI modeling was described in this research study. Urban planners can utilize this simple-to-use tool to look at how their design decisions will affect street-level traffic. There are five levels of UHI potential, ranging from low to high, represented by the UHI evaluation matrix. |
Qi, J. et al. [64] | Planning for cooler cities: A framework to support the selection of urban heat mitigation techniques | 2020 | Sydney, Australia | The development of a decision-making framework, related to UHI mitigation | One of the study’s contributions is the creation of a method that can determine the best combinations of UHMTs and planning and design elements with the greatest potential for cooling in a particular urban setting. The framework can support high-performance production and sustainable development, in addition to giving decision-makers the best UHMTs for creating a workable policy for mitigating urban heat islands. |
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Author(s) | Title | Year | Study Location | Methodology | Findings |
---|---|---|---|---|---|
Nuruzzaman, M. [20] | Urban heat island: causes, effects and mitigation measures-a review | 2015 | Not specified | Review of various measures used to encounter UHI effects. | The most effective measures are green vegetation, high albedo materials, and pervious pavements. |
Duplančić Leder, T. et al. [31] | Split Metropolitan area surface temperature assessment with remote sensing method | 2016 | Split, Croatia | Landsat thermal channels have been used to determine the LST. | Microclimate changes and severe changes in LST and UHI effects. |
Amani-Beni, M. et al. [46] | Impact of urban park’s tree, grass and waterbody on microclimate in hot summer days: A case study of Olympic Park in Beijing, China | 2018 | Beijing, China | Observation of the greenery impact on the park during summer days. | The park was 0.48–1.12 °C cooler during the day; increased air humidity was observed at 2.39–3.74%; and a reduced human comfort index was used to generate a more comfortable thermal environment. |
Nwakaire, C. M. et al. [61] | Urban Heat Island Studies with emphasis on urban pavements: A review | 2020 | Not specified | Literature review of UHIs, with emphasis on urban pavements. | One of the main findings included using creative designs that can provide cooling without compromising the structural integrity of the pavement, which is a key component of effective UHI mitigation techniques for highway pavements. |
… | |||||
Werbin, Z. et al. [62] | A tree-planting decision support tool for urban heat island mitigation | 2020 | Boston, USA | Development a Boston-specific Heat Vulnerability Index (HVI). | Researchers from Boston University examined the City of Boston’s strategy for expanding the urban canopy because the City has encountered challenges in establishing a long-lasting growth. They determined what barriers there were to applying scientific information to tree-planting decisions and collaborated with the city to create a tool that would help with the process. The tool itself is replicable or adaptable to other cities, and the process of development offers a model of a fruitful collaboration between academia and the public sector. |
Acosta, M. P. et al. [63] | How to bring UHI to the urban planning table? A data-driven modeling approach | 2021 | Montreal, Canada | Development of the decision support tool for the modeling of UHI at the street-level. | The methodology for creating a decision assistance tool for street-level UHI modeling was described in this research study. Urban planners can utilize this simple-to-use tool to look at how their design decisions will affect street-level traffic. There are five levels of UHI potential, ranging from low to high, represented by the UHI evaluation matrix. |
Qi, J. et al. [64] | Planning for cooler cities: A framework to support the selection of urban heat mitigation techniques | 2020 | Sidney, Australia | Development decision-making framework to UHI mitigation | One of the study’s contributions is the creation of a method that can determine the best combinations of UHMTs and planning and design elements with the greatest potential for cooling in a particular urban setting. The framework can support high-performance production and sustainable development in addition to giving decision-makers the best UHMTs for creating a workable policy for mitigating urban heat. |
Authors | Title | Year | Source Title | Cited by | Author’s Keywords | Ref. |
---|---|---|---|---|---|---|
Lissner T.K.; Holsten A.; Walther C.; Kropp J.P. | Towards sectoral and standardised vulnerability assessments: The example of heatwave impacts on human health | 2012 | Climatic Change | 33 | / | [131] |
Borri D.; Camarda D.; Pluchinotta I. | Planning urban microclimate through multiagent modelling: A cognitive mapping approach | 2013 | Lecture Notes in Computer Science | 11 | Behavioural knowledge; Decision support system; Fuzzy cognitive mapping; Multiple agents; Urban microclimate planning | [132] |
Tabatabaee S.; Mahdiyar A.; Durdyev S.; Mohandes S.R.; Ismail S. | An assessment model of benefits, opportunities, costs, and risks of green roof installation: A multi criteria decision making approach | 2019 | Journal of Cleaner Production | 55 | Cleaner production; Fuzzy DEMATEL; Green roof; Multi criteria decision making (MCDM); Sustainability | [102] |
Mostafa E.; Li X.; Sadek M. | Urbanization Trends Analysis Using Hybrid Modeling of Fuzzy Analytical Hierarchical Process-Cellular Automata-Markov Chain and Investigating Its Impact on Land Surface Temperature over Gharbia City, Egypt | 2023 | Remote Sensing | 8 | CA-Markov chain; fuzzy AHP; Gharbia governorate; hybrid models; LULCC dynamics; UHI | [119] |
Description | Results |
---|---|
MAIN INFORMATION ABOUT DATA | |
Timespan | 1996:2024 |
Sources (Journals, Books, etc.) | 144 |
Documents | 248 |
Document Average Age | 4.4 |
Average citations per doc | 21.88 |
References | 12,584 |
DOCUMENT CONTENTS | |
Keywords Plus (ID) | 1797 |
Author’s Keywords (DE) | 797 |
AUTHORS | |
Authors | 902 |
Authors of single-authored docs | 17 |
AUTHORS COLLABORATION | |
Single-authored docs | 18 |
Co-Authors per Doc | 4.03 |
International co-authorships % | 25 |
DOCUMENT TYPES | |
Article | 183 |
Book | 1 |
Book chapter | 17 |
Conference paper | 47 |
Sources | Articles |
---|---|
Science of the Total Environment | 11 |
Sustainable Cities and Society | 11 |
Sustainability (Switzerland) | 9 |
Remote Sensing | 8 |
Urban Climate | 8 |
Urban Forestry and Urban Greening | 7 |
Energy and Buildings | 6 |
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences—Isprs Archives | 6 |
Building and Environment | 5 |
Journal of Cleaner Production | 5 |
Sources | Number of Local Citations |
---|---|
Remote Sensing Applications: Society and Environment | 207 |
Building and Environment | 186 |
Energy and Buildings | 177 |
Landscape and Urban Planning | 129 |
Remote Sensing | 117 |
Science of the Total Environment | 115 |
Energy and Buildings | 109 |
Remote Sensing of Environment | 106 |
Landscape and Urban Planning | 104 |
Sustainable Cities and Society | 100 |
Author | h-Index | g-Index | m-Index | Total Citations | Number of Publications | First Publication Year |
---|---|---|---|---|---|---|
Chen, L. | 4 | 4 | 0.308 | 405 | 4 | 2011 |
Zhang, H. | 5 | 6 | 0.455 | 306 | 6 | 2013 |
Sun, R. | 2 | 2 | 0.167 | 271 | 2 | 2012 |
Cai, Y-B | 1 | 1 | 0.091 | 263 | 1 | 2013 |
Chen, M-N | 1 | 1 | 0.091 | 263 | 1 | 2013 |
Ma, W-C | 1 | 1 | 0.091 | 263 | 1 | 2013 |
Qi, Z-F | 1 | 1 | 0.091 | 263 | 1 | 2013 |
Ye, X-Y | 1 | 1 | 0.091 | 263 | 1 | 2013 |
Maderspacher, J. | 1 | 1 | 0.125 | 231 | 1 | 2016 |
Pauleit, S. | 1 | 1 | 0.125 | 231 | 1 | 2016 |
Wamsler, C. | 1 | 1 | 0.125 | 231 | 1 | 2016 |
Zölch, T. | 1 | 1 | 0.125 | 231 | 1 | 2016 |
Yu, Z. | 3 | 3 | 0.6 | 187 | 3 | 2019 |
Corburn, J. | 1 | 1 | 0.067 | 184 | 1 | 2009 |
Wang, X. | 2 | 2 | 0.4 | 169 | 2 | 2019 |
Qi, J. | 2 | 2 | 0.286 | 162 | 2 | 2017 |
Vejre, H. | 2 | 2 | 0.4 | 154 | 2 | 2019 |
Yang, G. | 2 | 2 | 0.4 | 154 | 2 | 2019 |
Ben-Dor, E. | 1 | 1 | 0.05 | 140 | 1 | 2004 |
Chudnovsky, A. | 1 | 1 | 0.05 | 140 | 1 | 2004 |
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Ćesić, M.; Rogulj, K.; Kilić Pamuković, J.; Krtalić, A. A Systematic Review on Fuzzy Decision Support Systems and Multi-Criteria Analysis in Urban Heat Island Management. Energies 2024, 17, 2013. https://doi.org/10.3390/en17092013
Ćesić M, Rogulj K, Kilić Pamuković J, Krtalić A. A Systematic Review on Fuzzy Decision Support Systems and Multi-Criteria Analysis in Urban Heat Island Management. Energies. 2024; 17(9):2013. https://doi.org/10.3390/en17092013
Chicago/Turabian StyleĆesić, Majda, Katarina Rogulj, Jelena Kilić Pamuković, and Andrija Krtalić. 2024. "A Systematic Review on Fuzzy Decision Support Systems and Multi-Criteria Analysis in Urban Heat Island Management" Energies 17, no. 9: 2013. https://doi.org/10.3390/en17092013
APA StyleĆesić, M., Rogulj, K., Kilić Pamuković, J., & Krtalić, A. (2024). A Systematic Review on Fuzzy Decision Support Systems and Multi-Criteria Analysis in Urban Heat Island Management. Energies, 17(9), 2013. https://doi.org/10.3390/en17092013