Article detail · 2017
A deep learning model for air quality prediction in smart cities
- Year
- 2017
- Type
- conference-paper
Abstract
OpenAlex · English
In recent years, Internet of Things (IoT) concept has become a promising research topic in many areas including industry, commerce and education. Smart cities employ IoT based services and applications to create a sustainable urban life. By using information and communication technologies, IoT enables smart cities to make city stakeholders more aware, interactive and efficient. With the increase in number of IoT based smart city applications, the amount of data produced by these applications is increased tremendously. Governments and city stakeholders take early precautions to process these data and predict future effects to ensure sustainable development. In prediction context, deep learning techniques have been used for several forecasting problems in big data. This inspires us to use deep learning methods for prediction of IoT data. Hence, in this paper, a novel deep learning model is proposed for analyzing IoT smart city data. We propose a novel model based on Long Short Term Memory (LSTM) networks to predict future values of air quality in a smart city. The evaluation results of the proposed model are found to be promising and they show that the model can be used in other smart city prediction problems as well.
Topics
Citations
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217 citations
OpenAlex cited_by_count (cache / database)
20 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).
- The Impact of Dust and Sandstorms in Increasing Drought Areas in Nineveh Province, North-western Iraq 2019
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- Air Pollution Modelling with Deep Learning: A Review 2018
- ANFIS and Deep Learning based missing sensor data prediction in IoT 2020
- A deep learning-based CEP rule extraction framework for IoT data 2021
- Short-term prediction of PM2.5 pollution with deep learning methods 2020