The seminar at MWC Shanghai 2018 highlighted the importance of air quality forecasting in Taiwan, where air pollution significantly affects health and quality of life. AI and IoT technologies were discussed for monitoring and predicting pollution levels, with an emphasis on leveraging big data for effective forecasting. Key goals include localizing models for Taiwan and integrating industrial impacts to enhance forecast accuracy.

1. 1
GSMA Internet of Things Seminar:
MWC Shanghai 2018
Dr. Hau Chen Mike Lee
Far Eastone, Taiwan
AI+IoT Case Study –
Air Quality Forecast Model
and
beyond

2. 2
Air Pollution, an important living quality
issue in Taiwan
• 7 of top 10 causes of death in Taiwan, are
closely related to air pollution.
• Government took many measures but
difficult to measure effectiveness
PM2.5 Taiwan Status
2

3. Universtiy of Texas. Environ. Sci. Technol. 2015, 49, 8057−8066
Harvard School of Public Health. Environ Health Perspect 2016
CVD & COPD
Polluted Air Can Lead to Cardiovascular and
Respiratory Diseases
Increase 2.1ug/m3 of PM2.5,
eGFR will decrease like the
picture shown below accordingly.

4. 4
Air Quality IoT devices
collected big data
connected devices
to monitor Air
Quality
IoT Real Values to Customers is Information
Information
Sensor Qualities
and Quantities
(many similar competitors)
Qualities of data sets
How often do we have data?
How accurate?
Can we use big data to
forecast air quality?
For how long?
How good?
(Competition on value)
DATA
IoT
Big Data
AI/
Machine Learning
4
Forecast
air quality

5. 5
PoC to Validate Machine Learning

6. 6
Learning from PoC
NO2
Reading
Air Quality
Station Data
Predict Taiwan N02 Value
NO2 _PPB Description NO2 _PPB Index Predicted NO2 Reading
Tempera-
ture
Wind
Direction
Barom-
etric
Pressure
Wind
Speed
Weather
Station Data
Pollution Source
Power Plant data
FET mobile
network data
FET Population
Aggregation
Gradient Boosting
Extra Trees
Random Forest
Data Processing & Model Tuning
Parameter Tuning
Learning Rate,
Max Features,
Max_Depth
Subsamples
GSMA
Model
Result
Grid Search
Filter outlier
Linear
interpolation
Data ProcessingOriginal
NO2 _ Predicted PPB

7. 7
Summary of Model Verification
Best Performance Model
Base 9% 10% 13% 15%
R2 Best Performance
Increase Rate
Predicted Taiwan NO 𝟐
GSMA Machine Learning Model
Weather Data
Interpolation
FET Population
Aggregation
Power Plant
Data
Model Parameter Tuning
(Grid Search)
NO2 Reading = 0
Filtering NO2
FET Carrier
Data
► Best Performance of Gradient Boost R2 : GSMA ML Model + Data
Interpolation + FET Population + Power Plant Data + Model Tuning

8. Objectives of “Air Quality Forecast Project”
8
PM 2.5 NO2
Build Forecast pipeline for London and Taiwan –
models for urbanization effects
Localize Forecast models for Taiwan by adding
industrialization impact
Expand to PM2.5
Commercialize with local government first

9. How we Build it
Acquire Taiwan air-
quality & weather
data
Compare London air-
quality model
learning performance
with Taiwanese
models
Adding weather
forecast information
in London and
Taiwan to build air
quality forecast
London/TW AQ &
Weather
Data Acquisition
1
London ML Model
Comparison with
Taiwan Data
2 3
Adding Weather
Forecast Info to build
Air Quality Forecast
9
Localization Taiwanese
Models by adding
Industrial Impacts
Adding industrial Impacts
to Taiwanese Models
4

10. 10
► Higher forecast resolution than area forecast
► Leverage both civilian and government sensors
We can provide reliable Air Quality Forecast
on a per station level
Taipei

11. Forecast Examples
11
London Taiwan

12. What we learned
1. Air quality is a key issues in Smart City, Health Care, and Industrial Anti-
pollution
2. Working forecast model need to integrate
► AI machine learning
► Carrier big data + IoT big data platform + weather forecast
► Air Quality IoT Sensors
3. Benchmark is important
4. New tech/new tools are amazing
AQ IoT sensor
open data
FET carrier data
& IoT big data
Health care
Smart City
Industrial
Anti-pollution
AQ forecast
12

13. Expanding Analytics to other services
With AI + IoT
From smallest tracker
to smartest services
13

14. PARTNERS CONNECTIVITY AREA

15. 15
Thank you
15
FET is willing to share our development
experiences with carriers