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Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-grained Air Quality
- Publication Year :
- 2017
-
Abstract
- The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing. The solutions to these topics can provide extremely useful information to support air pollution control, and consequently generate great societal and technical impacts. Most of the existing work solves the three problems separately by different models. In this paper, we propose a general and effective approach to solve the three problems in one model called the Deep Air Learning (DAL). The main idea of DAL lies in embedding feature selection and semi-supervised learning in different layers of the deep learning network. The proposed approach utilizes the information pertaining to the unlabeled spatio-temporal data to improve the performance of the interpolation and the prediction, and performs feature selection and association analysis to reveal the main relevant features to the variation of the air quality. We evaluate our approach with extensive experiments based on real data sources obtained in Beijing, China. Experiments show that DAL is superior to the peer models from the recent literature when solving the topics of interpolation, prediction, and feature analysis of fine-gained air quality.
- Subjects :
- FOS: Computer and information sciences
Computer science
Feature extraction
Air pollution
Feature selection
02 engineering and technology
Atmospheric model
Variation (game tree)
010501 environmental sciences
medicine.disease_cause
computer.software_genre
01 natural sciences
Machine Learning (cs.LG)
0202 electrical engineering, electronic engineering, information engineering
medicine
Air quality index
0105 earth and related environmental sciences
business.industry
Deep learning
Computer Science Applications
Computer Science - Learning
Computational Theory and Mathematics
Embedding
020201 artificial intelligence & image processing
Artificial intelligence
Data mining
business
computer
Information Systems
Interpolation
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....b7574434e7696c5864c5ece0060af9fc