Deep learning for air quality forecasts: a review
Q Liao, M Zhu, L Wu, X Pan, X Tang, Z Wang - Current Pollution Reports, 2020 - Springer
Air pollution is one of major environmental issues in the twenty-first century due to global
industrialization and urbanization. Its mitigation necessitates accurate air quality forecasts …
industrialization and urbanization. Its mitigation necessitates accurate air quality forecasts …
Urban anomaly analytics: Description, detection, and prediction
Urban anomalies may result in loss of life or property if not handled properly. Automatically
alerting anomalies in their early stage or even predicting anomalies before happening is of …
alerting anomalies in their early stage or even predicting anomalies before happening is of …
Urban flow prediction from spatiotemporal data using machine learning: A survey
Urban spatiotemporal flow prediction is of great importance to traffic management, land use,
public safety. This prediction task is affected by several complex and dynamic factors, such …
public safety. This prediction task is affected by several complex and dynamic factors, such …
Federated learning in the sky: Aerial-ground air quality sensing framework with UAV swarms
Due to air quality significantly affects human health, it is becoming increasingly important to
accurately and timely predict the air quality index (AQI). To this end, this article proposes a …
accurately and timely predict the air quality index (AQI). To this end, this article proposes a …
Intelligent calibration and virtual sensing for integrated low-cost air quality sensors
This paper presents the development of air quality low-cost sensors (LCS) with improved
accuracy features. The LCS features integrate machine learning based calibration models …
accuracy features. The LCS features integrate machine learning based calibration models …
Imbalanced regression and extreme value prediction
RP Ribeiro, N Moniz - Machine Learning, 2020 - Springer
Research in imbalanced domain learning has almost exclusively focused on solving
classification tasks for accurate prediction of cases labelled with a rare class. Approaches for …
classification tasks for accurate prediction of cases labelled with a rare class. Approaches for …
Urban PM2.5 Concentration Prediction via Attention-Based CNN–LSTM
Urban particulate matter forecasting is regarded as an essential issue for early warning and
control management of air pollution, especially fine particulate matter (PM2. 5). However …
control management of air pollution, especially fine particulate matter (PM2. 5). However …
Sparse mobile crowdsensing with differential and distortion location privacy
Sparse Mobile Crowdsensing (MCS) has become a compelling approach to acquire and
infer urban-scale sensing data. However, participants risk their location privacy when …
infer urban-scale sensing data. However, participants risk their location privacy when …
Towards personalized privacy-preserving incentive for truth discovery in mobile crowdsensing systems
Incentive mechanisms are essential for stimulating adequate worker participation to achieve
good truth discovery performance in mobile crowdsensing (MCS) systems. However, most of …
good truth discovery performance in mobile crowdsensing (MCS) systems. However, most of …
Understanding sensor cities: Insights from technology giant company driven smart urbanism practices
The data-driven approach to sustainable urban development is becoming increasingly
popular among the cities across the world. This is due to cities' attention in supporting smart …
popular among the cities across the world. This is due to cities' attention in supporting smart …