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Spatiotemporal deep learning model for citywide air pollution interpolation and prediction [article]

Van-Duc Le, Tien-Cuong Bui, Sang Kyun Cha
2019 arXiv   pre-print
In addition, recent research in air pollution has tried to build models to interpolate and predict air pollution in the city.  ...  Specially, we introduce how to transform the air pollution data into sequences of images which leverages the using of ConvLSTM model to interpolate and predict air quality for the entire city at the same  ...  SPATIOTEMPORAL DEEP LEARNING MODEL In this section, we present our proposed model for citywide air pollution Interpolation and Prediction based on Spatiotemporal Deep Learning.  ... 
arXiv:1911.12919v1 fatcat:tjp3qmm3kbfnddny4s44wa3nhq

Forecast Air Pollution in Smart City Using Deep Learning Techniques: A Review Ghufran Isam Drewil , Dr. Riyadh Jabbar Al-Bahadili

Dr. Riyadh Jabbar Al-Bahadili Ghufran Isam Drewil
2021 Zenodo  
With the rapid development of deep learning technologies and their usage in almost all aspects of life, it has become possible to predict air quality in smart cities using deep learning techniques.  ...  This paper will be considering all studies conducted recently to detect and predict the air pollution in smart cities.  ...  Spatiotemporal Deep Learning Model for Citywide Air Pollution Interpolation and Prediction [29] .  ... 
doi:10.5281/zenodo.4737745 fatcat:lerdnogfgrgh3jlzb4a3qzywwq

Deep-AIR: A Hybrid CNN-LSTM Framework for Air Quality Modeling in Metropolitan Cities [article]

Yang Han, Qi Zhang, Victor O.K. Li, Jacqueline C.K. Lam
2021 arXiv   pre-print
Our model attains an accuracy of 67.6%, 77.2%, and 66.1% in fine-grained hourly estimation, 1-hr, and 24-hr air pollution forecast for Hong Kong, and an accuracy of 65.0%, 75.3%, and 63.5% for Beijing.  ...  Using Hong Kong and Beijing as case studies, Deep-AIR achieves a higher accuracy than our baseline models.  ...  air quality, meteorology, and traffic data of Hong Kong, respectively.  ... 
arXiv:2103.14587v1 fatcat:ry2zcwif3rdspgyrazqndjeaqy

DeepCSO: Forecasting of Combined Sewer Overflow at a Citywide Level using Multi-task Deep Learning [article]

Duo Zhang, Geir Lindholm, Harsha Ratnaweera
2018 arXiv   pre-print
A comparison of the results demonstrated that the deep learning based multi-task model is superior to the traditional methods.  ...  In this paper, we proposed the DeepCSO model, which aims at forecasting CSO events from multiple CSO structures simultaneously in near real time at a citywide level.  ...  The authors would like to thank the engineers from Rosim AS for their supports.  ... 
arXiv:1811.06368v1 fatcat:g5czyq3xebhcxbnkxyb2v4xrvq

Enhanced Air Quality Inference via Multi-view Learning with Mobile Sensing Memory

Ning Liu, Xinyu Liu, Po-Ting Lin, Yue Wang, Lin Zhang
2022 IEEE Access  
Specifically, an encoder-decoder structure is applied in the region view for modeling the spatial dependencies in pollution maps.  ...  To address these challenges, we propose AQI-M 3 , a novel framework for fine-grained air quality inference via multi-view learning with mobile sensing memory.  ...  ACKNOWLEDGMENT The authors would like to thank Shenzhen Environmental Thinking Science and Technology (ETST) Company Ltd. for their assistance in system deployment and data collection.  ... 
doi:10.1109/access.2022.3164506 fatcat:bn5bakzkt5hjvngon2zjlmgauu

Urban Computing

Yu Zheng, Licia Capra, Ouri Wolfson, Hai Yang
2014 ACM Transactions on Intelligent Systems and Technology  
The tasks of urban computing include improving urban planning, easing traffic congestion, reducing energy consumption, and reducing air pollution.  ...  , taxi ridesharing, and air quality monitoring).  ...  In many cases, these systems are required to quickly answer users' instant queries (e.g., predicting traffic conditions and forecasting air pollution).  ... 
doi:10.1145/2629592 fatcat:no5gcshbmrdfphv6ewm6wdoewq

Hybrid Time-series Framework for Daily-based PM2.5 Forecasting

Pei-Wen Chiang, Shi-Jinn Horng
2021 IEEE Access  
[18] proposed a deep air learning (DAL) model to deal with feature analysis, interpolation and forecasting. In [34] , Zhang et al.  ...  In addition, many studies show that deep learning models have been used for air quality prediction.  ... 
doi:10.1109/access.2021.3099111 fatcat:chllqhsdkjh55dfvgidr67euu4

2020 Index IEEE Transactions on Intelligent Transportation Systems Vol. 21

2020 IEEE transactions on intelligent transportation systems (Print)  
., +, TITS April 2020 1503-1513 Air pollution Estimating Carbon Dioxide Emissions of Freeway Traffic: A Spatiotemporal Cell-Based Model.  ...  ., +, TITS Feb. 2020 803-814 An Improved Bayesian Combination Model for Short-Term Traffic Prediction With Deep Learning.  ... 
doi:10.1109/tits.2020.3048827 fatcat:ab6he3jkfjboxg7wa6pagbggs4

Transport-domain applications of widely used data sources in the smart transportation: A survey [article]

Sina Dabiri, Kevin Heaslip
2018 arXiv   pre-print
introduced to fuse the knowledge learned from a set of heterogeneous but complementary data sources.  ...  The rapid growth of population and the permanent increase in the number of vehicles engender several issues in transportation systems, which in turn call for an intelligent and cost-effective approach  ...  Behavior analysis of vehicles A robust vehicle detection and tracking system deliver rich information on spatiotemporal features that are used to learn, model, and predict the behavior of vehicles on the  ... 
arXiv:1803.10902v3 fatcat:tc67qy4x4vbtjb76qi6mbwrqy4

A study of multimodalization of spatio-temporal neural network for prediction

Yutaro MISHIMA, Shinya WADA
These days, many novel neural networks for modeling spatio-temporal relationship are proposed as many kinds of spatio-temporal datasets like location dataset or traffic dataset are published and utilized  ...  handle multimodal data and comparing prediction capability with the original network.  ...  ] Van-Duc Le, Tien-Cuong Bui, Sang- 1. *1 80 23 Kyun Cha: Spatiotemporal Deep Learning Model for Citywide Air Pollution Interpolation and Prediction, DMVST-Net Bigcomp, 2020.  ... 
doi:10.11517/pjsai.jsai2021.0_1j2gs10d03 fatcat:s5kqmasidzbapihlu4kzvgknbe

High-Resolution Urban Air Quality Mapping for Multiple Pollutants Based on Dense Monitoring Data and Machine Learning

Rong Guo, Ying Qi, Bu Zhao, Ziyu Pei, Fei Wen, Shun Wu, Qiang Zhang
Here, we proposed a method for urban air quality mapping at high-resolution for multiple pollutants.  ...  By using the dense air quality monitoring data from 448 micro stations in Lanzhou city, we developed a decision tree model to infer the distribution of citywide air quality at a 500 m × 500 m × 1 h resolution  ...  Acknowledgments: We sincerely thank the reviewers for their helpful comments and suggestions about our manuscript. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/ijerph19138005 pmid:35805664 pmcid:PMC9265361 fatcat:5zm2xyzkibf7tpyrslzbknxd2m

Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer [article]

Bin Lu, Xiaoying Gan, Weinan Zhang, Huaxiu Yao, Luoyi Fu, Xinbing Wang
2022 pre-print
Spatio-temporal graph learning is a key method for urban computing tasks, such as traffic flow, taxi demand and air quality forecasting.  ...  Therefore, we propose a model-agnostic few-shot learning framework for spatio-temporal graph called ST-GFSL.  ...  [11] employ IoT devices on vehicles to sense city air quality and estimated unknown air pollutants by variational graph autoencoders. In particular, Pan et al.  ... 
doi:10.1145/3534678.3539281 arXiv:2205.13947v1 fatcat:4eydhzsak5bdnlykvuo5ssfewi

Deep learning based prediction and visual analytics for temporal environmental data [article]

Shubhi Harbola, Universität Stuttgart
the past, present, and future behaviour with Machine Learning models' aid.  ...  The objective of this thesis is to focus on developing Machine Learning methods and their visualisation for environmental data.  ...  The prediction can be achieved using Machine Learning models comprising deep learning algorithms. These Machine Learning techniques including deep learning are subsets of Artificial Intelligence.  ... 
doi:10.18419/opus-12173 fatcat:antxn64qtnfuhcumwvfsmj6orq

High resolution remote sensing for reducing uncertainties in urban forest carbon offset life cycle assessments

Jan Tigges, Tobia Lakes
2017 Carbon Balance and Management  
The authors are grateful to Michael Strohbach (Institute of Geoecology, Technische Universität Braunschweig) for sharing and providing field data on tree growth.  ...  Acknowledgements This study was supported by the German Research Foundation (DFG) as part of the Research Training Group 780 3 and 4 on "Perspectives on Urban Ecology" (project numbers: 32108303 and 32108304  ...  Biophytomass, species-specific leaf structures and seasonal differences among tree species affect the filtering of air pollutants, as well as the cooling and retention of rainwater (Shashua-Bar et al.  ... 
doi:10.1186/s13021-017-0085-x pmid:28980218 pmcid:PMC5628095 fatcat:rhtun3osx5a57km5r2mst4uhf4

Adaptive Cycle Analysis of Urban Fragments

Mehmet Hakan Aksözen
For the analysis of the adaptive cycles of urban fragments, a new framework combines methods and instruments, incorporating concepts from ecology and sustainability.  ...  The social, cultural, economic and natural assets urban fragments possess are used to analyse and assess the future a fragment will face.  ...  research results for the integration of spatial data models into spatiotemporal data models but there is no commercial GIS software.  ... 
doi:10.5445/ir/1000035077 fatcat:inihad4gfzhitpbspq6ougcgni
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