Reviewing the application of machine learning methods to model urban form indicators in planning decision support systems: Potential, issues and challenges

[1]  C.-C. Jay Kuo.  Understanding convolutional neural networks with a mathematical model , 2016, J. Vis. Commun. Image Represent..

[2]  Yan Song,et al.  The impact of urban growth patterns on urban vitality in newly built-up areas based on an association rules analysis using geographical ‘big data’ , 2018, Land Use Policy.

[3]  Xin Li,et al.  Data analytics of urban fabric metrics for smart cities , 2018, Future Gener. Comput. Syst..

[4]  W. T. Williams,et al.  Principles of Clustering , 1971 .

[5]  Constantine E. Kontokosta,et al.  Using machine learning and small area estimation to predict building-level municipal solid waste generation in cities , 2018, Comput. Environ. Urban Syst..

[6]  Jin-Man Kim,et al.  Study of the Relationship between Urban Expansion and PM10 Concentration Using Multi-Temporal Spatial Datasets and the Machine Learning Technique: Case Study for Daegu, South Korea , 2019, Applied Sciences.

[7]  Hamza Toulni,et al.  Improving KNN Model for Direct Marketing Prediction in Smart Cities , 2021 .

[8]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[9]  Xiaofeng Zhu,et al.  Efficient kNN Classification With Different Numbers of Nearest Neighbors , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[10]  Jiwen Dong,et al.  Simple convolutional neural network on image classification , 2017, 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA)(.

[11]  Geoffrey E. Hinton,et al.  Transforming Auto-Encoders , 2011, ICANN.

[12]  Ross Maciejewski,et al.  Urban form and composition of street canyons: A human-centric big data and deep learning approach , 2019, Landscape and Urban Planning.

[13]  William F. Lamb,et al.  Learning about urban climate solutions from case studies , 2019, Nature Climate Change.

[14]  Irina Rish,et al.  An empirical study of the naive Bayes classifier , 2001 .

[15]  Douglas A. Reynolds.  Gaussian Mixture Models , 2009, Encyclopedia of Biometrics.

[16]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[17]  Heidi Sveistrup,et al.  Research article , 2001, Journal of Inherited Metabolic Disease.

[18]  Tzung-Pei Hong,et al.  CAR-Miner: An efficient algorithm for mining class-association rules , 2013, Expert Syst. Appl..

[19]  J. Salas,et al.  A COMPARATIVE ANALYSIS OF TECHNIQUES FOR SPATIAL INTERPOLATION OF PRECIPITATION , 1985 .

[20]  Ginger Goodin,et al.  Impacts of connected vehicles in a complex, congested urban freeway setting using multi-resolution modeling methods , 2019, International Journal of Transportation Science and Technology.

[21]  David D. Lewis,et al.  Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval , 1998, ECML.

[22]  Elisabet Viladecans-Marsal,et al.  Building(s and) Cities: Delineating Urban Areas with a Machine Learning Algorithm , 2019, Journal of Urban Economics.

[23]  A. Palma,et al.  Les modéles de choix discret , 1989 .

[24]  Xiaojiang Du,et al.  A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security , 2018, IEEE Communications Surveys & Tutorials.

[25]  Tom Hanika,et al.  Probably approximately correct learning of Horn envelopes from queries , 2018, Discret. Appl. Math..

[26]  Nikola Milojevic-Dupont,et al.  Learning from urban form to predict building heights , 2020, PloS one.

[27]  Hossein Shafizadeh-Moghadam,et al.  Coupling machine learning, tree-based and statistical models with cellular automata to simulate urban growth , 2017, Comput. Environ. Urban Syst..

[28]  Jairo Alejandro Gomez,et al.  Spatiotemporal Modeling of Urban Growth Using Machine Learning , 2019, Remote. Sens..

[29]  Andrew J. Tatem,et al.  Identifying residential neighbourhood types from settlement points in a machine learning approach , 2018, Comput. Environ. Urban Syst..

[30]  Hassan Silkan,et al.  Machine Learning Aprroach for Early Detection of Glaucoma from Visual Fields , 2020, NISS.

[31]  Hannes Taubenböck,et al.  Multistrategy ensemble regression for mapping of built-up density and height with Sentinel-2 data , 2020 .

[32]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[33]  J. Chan,et al.  Detecting the nature of change in an urban environment : A comparison of machine learning algorithms , 2001 .

[34]  Yasushi Yamaguchi,et al.  Overview of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) , 1998, IEEE Trans. Geosci. Remote. Sens..

[35]  C. van Treeck,et al.  Data-driven heating and cooling load predictions for non-residential buildings based on support vector machine regression and NARX Recurrent Neural Network: A comparative study on district scale , 2018, Energy.

[36]  Zhiyuan Liu,et al.  A tailored machine learning approach for urban transport network flow estimation , 2019, Transportation Research Part C: Emerging Technologies.

[37]  Salvatore Ruggieri,et al.  Efficient C4.5 , 2002, IEEE Trans. Knowl. Data Eng..

[38]  Q. Zhan,et al.  The Diversified Impacts of Urban Morphology on Land Surface Temperature among Urban Functional Zones , 2020, International journal of environmental research and public health.

[39]  Blaz Zupan,et al.  Orange: From Experimental Machine Learning to Interactive Data Mining , 2004, PKDD.

[40]  Zifeng Guo,et al.  Data‐driven flood emulation: Speeding up urban flood predictions by deep convolutional neural networks , 2020, Journal of Flood Risk Management.

[41]  Ross Maciejewski,et al.  Sky View Factor footprints for urban climate modeling , 2018, Urban Climate.

[42]  Huimin Liu,et al.  Characterizing the spatial and temporal variation of the land surface temperature hotspots in Wuhan from a local scale , 2020, Geo spatial Inf. Sci..

[43]  Peter Mooney,et al.  Using Crowdsourced Trajectories for Automated OSM Data Entry Approach , 2016, Sensors.

[44]  Han Zhang,et al.  Self-Attention Generative Adversarial Networks , 2018, ICML.

[45]  Neeraj Kumar,et al.  Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid , 2016, IEEE Transactions on Industrial Informatics.

[46]  Jian Liu,et al.  Quantifying the Effects of Urban Form on Land Surface Temperature in Subtropical High-Density Urban Areas Using Machine Learning , 2019, Remote. Sens..

[47]  Mengjie Han,et al.  An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm , 2021, Buildings.

[48]  Krithi Ramamritham,et al.  Transfer learning approach to map urban slums using high and medium resolution satellite imagery , 2019, Habitat International.

[49]  U. Rajendra Acharya,et al.  A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images , 2017, Comput. Biol. Medicine.

[50]  Javier Del Ser,et al.  Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0 , 2019, Inf. Fusion.

[51]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[52]  N. Wong,et al.  Dependence between urban morphology and outdoor air temperature: A tropical campus study using random forests algorithm , 2020 .

[53]  Marti A. Hearst.  Trends & Controversies: Support Vector Machines , 1998, IEEE Intell. Syst..

[54]  Hari Wijayanto,et al.  Ensemble K-nearest neighbors method to predict rice price in Indonesia , 2014 .

[55]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[56]  Hassan Silkan,et al.  Prediction of Bus Waiting Time in Smart Cities Using Machine Learning Techniques and the Internet of Things , 2020 .

[57]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[58]  Lan Wang,et al.  Application of Improved Decision Tree Method based on Rough Set in Building Smart Medical Analysis CRM System , 2016 .

[59]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[60]  J. Berkson.  Application of the Logistic Function to Bio-Assay , 1944 .

[61]  Usman Qamar,et al.  An Efficient Rule-Based Classification of Diabetes Using ID3, C4.5, & CART Ensembles , 2014, 2014 12th International Conference on Frontiers of Information Technology.

[62]  Mark Stevenson,et al.  The “Paris-End” of Town? Deriving Urban Typologies Using Three Imagery Types , 2020, Urban Science.

[63]  Flora D. Salim,et al.  A Scalable Room Occupancy Prediction with Transferable Time Series Decomposition of CO2 Sensor Data , 2018, ACM Trans. Sens. Networks.

[64]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[65]  Liang-pei Zhang,et al.  Estimating Regional Ground‐Level PM2.5 Directly From Satellite Top‐Of‐Atmosphere Reflectance Using Deep Belief Networks , 2017, Journal of Geophysical Research: Atmospheres.

[66]  Xixi Lu,et al.  A global comparative analysis of urban form: Applying spatial metrics and remote sensing , 2007 .

[67]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[68]  A. Frenkel,et al.  Measuring Urban Sprawl: How Can We Deal with It? , 2008 .

[69]  A. Kafy,et al.  Cellular Automata approach in dynamic modelling of land cover changes using RapidEye images in Dhaka, Bangladesh , 2021 .

[70]  E. Gomes,et al.  Modelling urban form: A multidimensional typology of urban occupation for spatial analysis , 2019 .

[71]  Weiwei Chen,et al.  Analyzing driving factors of land values in urban scale based on big data and non-linear machine learning techniques , 2020 .

[72]  Hannu Toivonen,et al.  Sampling Large Databases for Association Rules , 1996, VLDB.

[73]  J. Milder.  Sustainable Urban Form , 2012 .

[74]  Christopher E. Ndehedehe,et al.  Integrating machine learning with Markov chain and cellular automata models for modelling urban land use change , 2020, Remote Sensing Applications: Society and Environment.

[75]  Yoav Freund,et al.  A Short Introduction to Boosting , 1999 .

[76]  Yuan Meng,et al.  Measuring urban landscapes for urban function classification using spatial metrics , 2020 .

[77]  Song Han,et al.  Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding , 2015, ICLR.

[78]  El Arbi Abdellaoui Alaoui,et al.  Intelligent management of bike sharing in smart cities using machine learning and Internet of Things , 2021 .

[79]  Moira L. Zellner,et al.  Exploring the Influence of Urban Form on Work Travel Behavior with Agent-Based Modeling , 2008 .

[80]  Uwe Stilla,et al.  Machine Learning Comparison between WorldView-2 and QuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification , 2011, Remote. Sens..

[81]  Razvan Pascanu,et al.  On the difficulty of training recurrent neural networks , 2012, ICML.

[82]  Ujjwal Bhattacharya,et al.  CNN based common approach to handwritten character recognition of multiple scripts , 2015, 2015 13th International Conference on Document Analysis and Recognition (ICDAR).

[83]  Leo Newland,et al.  Mapping micro-urban heat islands using LANDSAT TM and a GIS , 1995 .

[84]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[85]  Dewen Seng,et al.  A combined method for short-term traffic flow prediction based on recurrent neural network , 2020 .

[86]  Dennis Luxen,et al.  Real-time routing with OpenStreetMap data , 2011, GIS.

[87]  Yanru Zhang,et al.  A gradient boosting method to improve travel time prediction , 2015 .

[88]  F. Pla,et al.  Characterizing the relationship between land use land cover change and land surface temperature , 2017 .

[89]  Hendrik Herold,et al.  Automatic Derivation of Urban Structure Types from Topographic Maps by Means of Image Analysis and Machine Learning , 2013 .

[90]  Walid Cherif,et al.  Optimization of K-NN algorithm by clustering and reliability coefficients: application to breast-cancer diagnosis , 2018 .

[91]  Chuan Wang,et al.  Forecasting urban household water demand with statistical and machine learning methods using large space-time data: A Comparative study , 2018, Environ. Model. Softw..

[92]  Nina Schwarz.  Urban form revisited—Selecting indicators for characterising European cities , 2010 .

[93]  Hassan Silkan,et al.  Improvement in automated diagnosis of soft tissues tumors using machine learning , 2021, Big Data Min. Anal..

[94]  Simon Fong,et al.  DBSCAN: Past, present and future , 2014, The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014).

[95]  Michael I. Jordan,et al.  Machine learning: Trends, perspectives, and prospects , 2015, Science.

[96]  G. McCormack,et al.  The associations between objectively-determined and self-reported urban form characteristics and neighborhood-based walking in adults , 2014, International Journal of Behavioral Nutrition and Physical Activity.

[97]  Elisabete A. Silva,et al.  Crowdsourced Data Mining for Urban Activity: Review of Data Sources, Applications, and Methods , 2020, Journal of Urban Planning and Development.

[98]  Xu Bing,et al.  AdaBoost typical Algorithm and its application research , 2017 .

[99]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[100]  Yves Rybarczyk,et al.  Modeling PM2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters , 2017, J. Electr. Comput. Eng..

[101]  Changyeon Lee,et al.  Impacts of urban form on air quality: Emissions on the road and concentrations in the US metropolitan areas. , 2019, Journal of environmental management.

[102]  Jieping Ye,et al.  Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting , 2019, AAAI.

[103]  N. Wong,et al.  Combining CityGML files and data-driven models for microclimate simulations in a tropical city , 2020, Building and Environment.

[104]  Yanxia Sun,et al.  Improved sparse autoencoder based artificial neural network approach for prediction of heart disease , 2020 .

[105]  T. Moon.  The expectation-maximization algorithm , 1996, IEEE Signal Process. Mag..

[106]  Martin Mozina,et al.  Orange: data mining toolbox in python , 2013, J. Mach. Learn. Res..

[107]  D. Opitz,et al.  Popular Ensemble Methods: An Empirical Study , 1999, J. Artif. Intell. Res..

[108]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[109]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[110]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[111]  Helena Titheridge,et al.  predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning , 2018, Comput. Environ. Urban Syst..

[112]  HwaMin Lee,et al.  Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models , 2020 .

[113]  Satu Elisa Schaeffer,et al.  Graph Clustering , 2017, Encyclopedia of Machine Learning and Data Mining.

[114]  George D. C. Cavalcanti,et al.  A study on combining dynamic selection and data preprocessing for imbalance learning , 2018, Neurocomputing.

[115]  Gotthard Meinel,et al.  Automatic identification of building types based on topographic databases – a comparison of different data sources , 2015 .

[116]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[117]  Phil Hubbard,et al.  Understanding urban gentrification through machine learning , 2018, Urban Studies.

[118]  James H. Faghmous,et al.  Spatio-temporal Data Mining for Climate Data: Advances, Challenges, and Opportunities , 2014 .

[119]  H. B. Barlow,et al.  Unsupervised Learning , 1989, Neural Computation.

[120]  Matthijs Douze,et al.  Deep Clustering for Unsupervised Learning of Visual Features , 2018, ECCV.

[121]  William H. Press,et al.  Numerical recipes , 1990 .

[122]  Hassan Silkan,et al.  Improving parking availability prediction in smart cities with IoT and ensemble-based model , 2020, J. King Saud Univ. Comput. Inf. Sci..

[123]  Daniel Castro-Lacouture,et al.  Generative design and performance modeling for relationships between urban built forms, sky opening, solar radiation and energy , 2019, Energy Procedia.

[124]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[125]  Masakazu Matsugu,et al.  Subject independent facial expression recognition with robust face detection using a convolutional neural network , 2003, Neural Networks.

[126]  R. Janarthanan,et al.  A deep learning approach for prediction of air quality index in a metropolitan city , 2021 .

[127]  S. Lindley,et al.  Evidence of urban heat island impacts on the vegetation growing season length in a tropical city , 2021, Landscape and Urban Planning.

[128]  Debahuti Mishra,et al.  Adaptive boosting of weak regressors for forecasting of crop production considering climatic variability: An empirical assessment , 2017, J. King Saud Univ. Comput. Inf. Sci..

[129]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[130]  J. Berkson.  Why I Prefer Logits to Probits , 1951 .

[131]  Shenghui Cui,et al.  Assessing the adaptive capacity of urban form to climate stress: a case study on an urban heat island , 2019, Environmental Research Letters.

[132]  Vincent Loonis.  Les déterminants de la réélection des députés français de 1871 à 2002 , 2006 .

[133]  S. Lee,et al.  Ubiquitous urban infrastructure: Infrastructure planning and development in Korea , 2008 .

[134]  Verónica Bolón-Canedo,et al.  Ensembles for feature selection: A review and future trends , 2019, Inf. Fusion.

[135]  Yong-qing Wei,et al.  An improved Apriori algorithm for association rules of mining , 2009, 2009 IEEE International Symposium on IT in Medicine & Education.

[136]  M. Hart,et al.  Quantifying the influence of land-use and surface characteristics on spatial variability in the urban heat island , 2009 .

[137]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

[138]  Xia Li,et al.  Simulating Urban Form and Energy Consumption in the Pearl River Delta Under Different Development Strategies , 2013 .

[139]  Jun-Hyun Kim,et al.  Predicting stream water quality under different urban development pattern scenarios with an interpretable machine learning approach. , 2020, The Science of the total environment.

[140]  Nikolaos Doulamis,et al.  Deep Learning for Computer Vision: A Brief Review , 2018, Comput. Intell. Neurosci..

[141]  Li Li,et al.  Using LSTM and GRU neural network methods for traffic flow prediction , 2016, 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC).

[142]  K. Krishna,et al.  Genetic K-means algorithm. , 1999, IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society.

[143]  Raymond J. Mooney,et al.  Mapping and Revising Markov Logic Networks for Transfer Learning , 2007, AAAI.

[144]  Bart Custers,et al.  Drone Technology: Types, Payloads, Applications, Frequency Spectrum Issues and Future Developments , 2016 .

[145]  Shipeng Xie,et al.  Alcoholism Identification Based on an AlexNet Transfer Learning Model , 2019, Front. Psychiatry.

[146]  Hui Wang,et al.  A machine learning-based method for the large-scale evaluation of the qualities of the urban environment , 2017, Comput. Environ. Urban Syst..

[147]  Chao Yang,et al.  A Survey on Deep Transfer Learning , 2018, ICANN.