ArcUHI: A GIS add-in for automated modelling of the Urban Heat Island effect through machine learning

Alexander, 2020, Normalised difference spectral indices and urban land cover as indicators of land surface temperature (LST), Int. J. Appl. Earth Obs. Geoinf., 86 Alshemali, 2020, Improving the reliability of deep neural networks in NLP: a review, Knowl.-Based Syst., 191, 10.1016/j.knosys.2019.105210 Au, 2018, Random forests, decision trees, and categorical predictors: the “absent levels” problem, J. Mach. Learn. Res., 19, 1 Awad, 2015, Support Vector Regression, 67 Beck, 2022 Beck, 2018, Present and future Köppen-Geiger climate classification maps at 1-km resolution, Sci. Data, 5, 10.1038/sdata.2018.214 Behzad, 2009, Generalization performance of support vector machines and neural networks in runoff modeling, Expert Syst. Appl., 36, 7624, 10.1016/j.eswa.2008.09.053 Bindajam, 2020, Impacts of vegetation and topography on land surface temperature variability over the semi-Arid Mountain cities of Saudi Arabia, Atmosphere, 11, 762, 10.3390/atmos11070762 Bottyán, 2003, A multiple linear statistical model for estimating the mean maximum urban heat island, Theor. Appl. Climatol., 75, 233, 10.1007/s00704-003-0735-7 Breiman, 1996, Bagging predictors, Mach. Learn., 24, 123, 10.1007/BF00058655 Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Breiman, 1984 Buttstädt, 2014, Thermal load in a medium-sized European city using the example of Aachen, Germany, Erdkunde, 68, 71, 10.3112/erdkunde.2014.02.01 Campisi, 2021, The development of the smart cities in the connected and autonomous vehicles (CAVs) era: from mobility patterns to scaling in cities, Infrastructures, 6, 100, 10.3390/infrastructures6070100 Chicco, 2021, The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation, PeerJ. Comput. Sci., 7, 10.7717/peerj-cs.623 Chuanyan, 2005, Methods for modelling of temporal and spatial distribution of air temperature at landscape scale in the southern Qilian mountains, China, Ecol. Model., 189, 209, 10.1016/j.ecolmodel.2005.03.016 Ciardini, 2019, Interconnections of the urban heat island with the spatial and temporal micrometeorological variability in Rome, Urban Clim., 29, 10.1016/j.uclim.2019.100493 Cichosz, 2014, Imitation learning of car driving skills with decision trees and random forests, Int. J. Appl. Math. Comput. Sci., 24, 579, 10.2478/amcs-2014-0042 CNIG, 2018 Cortez, 2020 Cortez, 2013, Using sensitivity analysis and visualization techniques to open black box data mining models, Inf. Sci., 225, 1, 10.1016/j.ins.2012.10.039 De Gregorio, 2018, Operational river discharge forecasting with support vector regression technique applied to alpine catchments: results, advantages, limits and lesson learned, Water Resour. Manag., 32, 229, 10.1007/s11269-017-1806-3 Dewan, 2021, Surface urban heat island intensity in five major cities of Bangladesh: patterns, drivers and trends, Sustain. Cities Soc., 71, 10.1016/j.scs.2021.102926 Dirksen, 2019, Sky view factor calculations and its application in urban heat island studies, Urban Clim., 30, 10.1016/j.uclim.2019.100498 Dozier, 1990, Rapid calculation of terrain parameters for radiation modeling from digital elevation data, IEEE Trans. Geosci. Remote Sens., 28, 963, 10.1109/36.58986 El Kenawy, 2020, Nocturnal surface urban Heat Island over greater cairo: spatial morphology, temporal trends and links to land-atmosphere influences, Remote Sens., 12, 3889, 10.3390/rs12233889 Equere, 2021, Integration of topological aspect of city terrains to predict the spatial distribution of urban heat island using GIS and ANN, Sustain. Cities Soc., 69, 10.1016/j.scs.2021.102825 Esri Esri, 2019 European Union, Copernicus Land Monitoring Service European Union, Copernicus Land Monitoring Service Fisher, 1992, Statistical methods for research workers, 66 Fisher, 2019, All models are wrong, but many are useful: learning a variable’s importance by studying an entire class of prediction models simultaneously, J. Mach. Learn. Res., 20 Fox, 2021 Gaffin, 2008, Variations in New York city’s urban heat island strength over time and space, Theor. Appl. Climatol., 94, 1, 10.1007/s00704-007-0368-3 Gardes, 2020, Statistical prediction of the nocturnal urban heat island intensity based on urban morphology and geographical factors - an investigation based on numerical model results for a large ensemble of French cities, Sci. Total Environ., 737, 10.1016/j.scitotenv.2020.139253 Gevrey, 2003, Review and comparison of methods to study the contribution of variables in artificial neural network models, Ecol. Model., 160, 249, 10.1016/S0304-3800(02)00257-0 Graf, 2003, Classification in a normalized feature space using support vector machines, IEEE Trans. Neural Netw., 14, 597, 10.1109/TNN.2003.811708 Han, 2014, Influence of aspect on land surface temperature in mountainous city: a case study in central area of Chongqing city, Shengtai Xuebao Acta Ecol. Sin., 34, 4017 Hart, 2009, Quantifying the influence of land-use and surface characteristics on spatial variability in the urban heat island, Theor. Appl. Climatol., 95, 397, 10.1007/s00704-008-0017-5 Herath, 2018, Modeling a tropical urban context with green walls and green roofs as an Urban Heat Island adaptation strategy, Proc. Eng., 212, 691, 10.1016/j.proeng.2018.01.089 Hidayat, 2021, Development of temperature monitoring and prediction system for urban heat island (UHI) based on the internet of things, J. Phys.: Conf. Ser., 1816, 012054 Hjort, 2011, Spatial prediction of urban–rural temperatures using statistical methods, Theor. Appl. Climatol., 106, 139, 10.1007/s00704-011-0425-9 Huynh-Thu, 2019, Unsupervised gene network inference with decision trees and random forests, 195 INE James, 2013 Jato-Espino, 2019, Spatiotemporal statistical analysis of the urban Heat Island effect in a Mediterranean region, Sustain. Cities Soc., 46, 10.1016/j.scs.2019.101427 Kassambara, 2019 Khalil, 2021, Time series analysis of land surface temperature and drivers of urban heat island effect based on remotely sensed data to develop a prediction model, Appl. Artif. Intell., 35, 1, 10.1080/08839514.2021.1993633 Khan, 2020, City-scale Modeling of Urban Heat Islands for Kolkata, 89 Kim, 2019, Multicollinearity and misleading statistical results, Korean J. Anesthesiol., 72, 558, 10.4097/kja.19087 2008, 1090 Kotharkar, 2018, Evaluating urban heat island in the critical local climate zones of an Indian city, Landsc. Urban Plan., 169, 92, 10.1016/j.landurbplan.2017.08.009 Kotthaus, 2014, Energy exchange in a dense urban environment – part II: impact of spatial heterogeneity of the surface, Urban Clim., 10, 281, 10.1016/j.uclim.2013.10.001 Kuhn, 2021 Kyriakodis, 2018, Using reflective pavements to mitigate urban heat island in warm climates - results from a large scale urban mitigation project, Urban Clim., 24, 326, 10.1016/j.uclim.2017.02.002 Lek, 1996, Application of neural networks to modelling nonlinear relationships in ecology, Ecol. Model., 90, 39, 10.1016/0304-3800(95)00142-5 Li, 2012, An empirical study of the impact of human activity on long-term temperature change in China: a perspective from energy consumption, J. Geophys. Res.-Atmos., 117, 1, 10.1029/2012JD018132 Li, 2021, Context sensitivity of surface urban heat island at the local and regional scales, Sustain. Cities Soc., 74, 10.1016/j.scs.2021.103146 Liaw, 2018 Liu, 2019, Spatially disaggregating satellite land surface temperature with a nonlinear model across agricultural areas, J. Geophys. Res. Biogeosci., 124, 3232, 10.1029/2019JG005227 Liu, 2021, Predicting the surface urban heat island intensity of future urban green space development using a multi-scenario simulation, Sustain. Cities Soc., 66, 10.1016/j.scs.2020.102698 Madrid City Council Madrid City Council Madrid'’s Directorate of Public Health, 2021 Makido, 2016, Daytime variation of urban Heat Islands: the case study of Doha, Qatar. Clim., 4, 32 Marando, 2022, Urban heat island mitigation by green infrastructure in European functional urban areas, Sustain. Cities Soc., 77, 10.1016/j.scs.2021.103564 Marcot, 2021, What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?, Comput. Stat., 36, 2009, 10.1007/s00180-020-00999-9 Mathew, 2019, Prediction of land surface temperatures for surface urban heat island assessment over Chandigarh city using support vector regression model, Sol. Energy, 186, 404, 10.1016/j.solener.2019.04.001 Meng, 2021, Prediction of urban Heat Island effect over Jinan City using the Markov-cellular automata model combined with urban biophysical descriptors, J. Indian Soc. Remote Sens., 49, 997, 10.1007/s12524-020-01274-6 Menze, 2009, A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data, BMC Bioinform., 10, 10.1186/1471-2105-10-213 Meyer, 2021 Montavon, 2018, Methods for interpreting and understanding deep neural networks, Digit. Signal Proc., 73, 1, 10.1016/j.dsp.2017.10.011 Mutiibwa, 2015, Land surface temperature and surface air temperature in complex terrain, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 8, 4762, 10.1109/JSTARS.2015.2468594 Nguyen, 2021, Comparison of multivariate linear regression and a machine learning algorithm developed for prediction of precision warfarin dosing in a Korean population, J. Thromb. Haemost., 19, 1676, 10.1111/jth.15318 Noro, 2015, Urban heat island in Padua, Italy: simulation analysis and mitigation strategies, Urban Clim., 14, 187, 10.1016/j.uclim.2015.04.004 Oh, 2020, Using deep-learning to forecast the magnitude and characteristics of urban heat island in Seoul Korea, Sci. Rep., 10 Oke, 1982, The energetic basis of the urban heat island, Q. J. R. Meteorol. Soc., 108, 1 ORNL DAAC, 2017 Osborne, 2003, Four assumptions of multiple regression that researchers should always test, Pract. Assess. Res. Eval., 8 Peng, 2012, Surface urban Heat Island across 419 global big cities, Environ. Sci. Technol., 46, 696, 10.1021/es2030438 Pigliautile, 2020, Inter-building assessment of urban heat island mitigation strategies: field tests and numerical modelling in a simplified-geometry experimental set-up, Renew. Energy, 147, 1663, 10.1016/j.renene.2019.09.082 R Core Team, 2020 Rahimi, 2009, Random features for large-scale kernel machines Rhee, 2014, Relationship between land cover patterns and surface temperature in urban areas, GISci. Remote Sens., 51, 521, 10.1080/15481603.2014.964455 Salmerón, 2018, Variance inflation factor and condition number in multiple linear regression, J. Stat. Comput. Simul., 88, 2365, 10.1080/00949655.2018.1463376 Sánchez-Guevara Sánchez, 2017, Urban Heat Island and vulnerable population. The case of Madrid, 3 Schwaab, 2021, The role of urban trees in reducing land surface temperatures in European cities, Nat. Commun., 12, 6763, 10.1038/s41467-021-26768-w SEC Shatnawi, 2019, Mapping urban land surface temperature using remote sensing techniques and artificial neural network modelling, Int. J. Remote Sens., 40, 3968, 10.1080/01431161.2018.1557792 Sheu, 2020, Illuminating the black box: interpreting deep neural network models for psychiatric research, Front. Psychiatry, 11, 10.3389/fpsyt.2020.551299 Solecki, 2005, Mitigation of the heat island effect in urban New Jersey, Environ. Hazards, 6, 39, 10.1016/j.hazards.2004.12.002 Straub, 2019, Statistical modelling of spatial patterns of the urban heat island intensity in the urban environment of Augsburg, Germany, Urban Clim., 29, 10.1016/j.uclim.2019.100491 Sun, 2019, Quantifying the effects of urban form on land surface temperature in subtropical high-density urban areas using machine learning, Remote Sens., 11, 959, 10.3390/rs11080959 Sussman, 2021, The controlling factors of urban heat in Bengaluru, India, Urban Clim., 38, 10.1016/j.uclim.2021.100881 Szymanowski, 2012, Local regression models for spatial interpolation of urban heat island—an example from Wrocław, SW Poland, Theor. Appl. Climatol., 108, 53, 10.1007/s00704-011-0517-6 Tan, 2021, The urban heat island mitigation potential of vegetation depends on local surface type and shade, Urban For. Urban Green., 62, 10.1016/j.ufug.2021.127128 Tepanosyan, 2021, Studying spatial-temporal changes and relationship of land cover and surface Urban Heat Island derived through remote sensing in Yerevan, Armenia, Build. Environ., 187, 10.1016/j.buildenv.2020.107390 Unger, 2004, Intra-urban relationship between surface geometry and urban heat island: review and new approach, Clim. Res., 27, 253, 10.3354/cr027253 Üstün, 2007, Visualisation and interpretation of support vector regression models, Anal. Chim. Acta, 595, 299, 10.1016/j.aca.2007.03.023 Van Doninck, 2016 Voogt, 2003, Thermal remote sensing of urban climates, Remote Sens. Environ., 86, 370, 10.1016/S0034-4257(03)00079-8 Wang, 2020, Downscale MODIS land surface temperature based on three different models to analyze surface urban heat island: a case study of Hangzhou, Remote Sens., 12 Wang, 2021, Cool pavements for urban heat island mitigation: a synthetic review, Renew. Sust. Energ. Rev., 146, 10.1016/j.rser.2021.111171 Xiao, 2018, Responses of urban land surface temperature on land cover: a comparative study of Vienna and Madrid, Sustainability, 10, 260, 10.3390/su10020260 Yagüe, 1991, Statistical analysis of the Madrid urban heat island, Atmos. Environ. Part B Urban Atmos., 25, 327, 10.1016/0957-1272(91)90004-X Yoo, 2018, Investigating important urban characteristics in the formation of urban heat islands: a machine learning approach, J. Big Data, 5, 10.1186/s40537-018-0113-z Yuan, 2011, Mitigating urban heat island effects in high-density cities based on sky view factor and urban morphological understanding: a study of Hong Kong, Archit. Sci. Rev., 54, 305, 10.1080/00038628.2011.613644 Yuan, 2020, Mitigating intensity of urban heat island by better understanding on urban morphology and anthropogenic heat dispersion, Build. Environ., 176, 10.1016/j.buildenv.2020.106876 Zhang, 2021, Predicting surface Urban Heat Island in Meihekou City, China: a combination method of Monte Carlo and Random Forest, Chin. Geogr. Sci., 31, 659, 10.1007/s11769-021-1215-7 Zhou, 2018, Satellite remote sensing of surface Urban Heat Islands: Progress, challenges, and perspectives, Remote Sens., 11, 48, 10.3390/rs11010048 Žuvela-Aloise, 2016, Modelling the potential of green and blue infrastructure to reduce urban heat load in the city of Vienna, Clim. Chang., 135, 425, 10.1007/s10584-016-1596-2