Lecture: A Deep Learning Approach to Jointly Exploit Spatial and Temporal Patterns for Accurate Air Quality Forecasting

Speaker: Yao-Yi Chiang, Associate Professor , University of Southern California

Time: 10:00 a.m., 17th, Dec. , 2018

Venue: Room208, Tongji Building A

Abstract:

Predicting and forecasting air quality at a fine spatiotemporal scale is not only essential for studying the impact of air pollutant on health conditions but also critical for making informed decisions. For example, accurately forecasting the air quality at a fine spatial resolution in a city can help school officials make their advanced prevention plan based on their locations. (School on the east side of the town might need to cancel the afternoon physical education classes due to the poor air quality but not other schools.) Existing work on air quality modeling typically relies on area-specific, expert-selected data features and fail to model the complex spatial and temporal relationships between the air quality data generated from a sensor network. In this talk, I will present our latest approach for forecasting the short-term (next 24 hours) PM 2.5 concentrations using a deep learning model. The model learns the spatial relationships between air quality sensors by first mining publicly available geographic data to determine how the built environment affects air quality and then performing a diffusion convolutional process on the sensor network. Next, the model learns the temporal dependencies of the air quality readings by leveraging the sequence-to-sequence encoder-decoder architecture. We have evaluated our model on two real-world air quality datasets (Beijing and Los Angeles) and showed consistent improvement over the state-of-the-art deep learning approaches.

Bio:

Yao–Yi Chiang, Ph.D.

Spatial Sciences Institute

Dana and David Dornsife College of Letters, Arts and Sciences

University of Southern California

3616 Trousdale Parkway, AHF B55C

Los Angeles, CA 90089-0374

Phone: (213) 740-5910

E-mail: yaoyic@usc.edu

Personal Website: https://yaoyichi.github.io

Spatial Computing Lab Website: https://spatial–computing.github.io/

Current Appointments

University of Southern California

2017 – Associate Professor (Research) of Spatial Sciences, Spatial Sciences Institute

2017 – Associate Director, Integrated Media System Center

2013 – Director, Spatial Computing Lab, Spatial Sciences Institute

2013 – Visiting Computer Scientist, Information Sciences Institute

GeoInformatica (An International Journal on Advances of Computer Science for Geographic Information

Systems, Springer)

2017 – Action Editor

Education

2007 – 2010 Ph.D., Computer Science, University of Southern California, USA

Dissertation Title: Harvesting Geographic Features from Heterogeneous Raster

Maps

2003 – 2004 M.S., Computer Science, University of Southern California, USA

1996 – 2000 B.B.A. in Information Management, National Taiwan University, Taiwan

Research Focus

My research focus lies at the intersection of computer science and spatial sciences. I build artificial intelligence algorithms and applications (in particular, with technologies in machine learning and data mining) for discovering, collecting, fusing, and analyzing spatial data from heterogeneous sources, ranging from streaming data and time series data (e.g., traffic and air monitoring sensors) to images

(e.g., scanned maps and satellite imagery).