API enabled AQI Prediction using Federated Learning
Presented by,
Anurag Singh
B200058CS
Under the Supervision of
Dr. Pankaj Kesarwani
National Institute of Technology Sikkim
Department of Computer Science
Anurag Singh
National Institute of Technology Sikkim
12 October 2023
Content
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Introduction
Literature Survey
Research Gap
Motivation & Objective
Problem Statement
Pre-requisite
Current Work
Future Work
References
12 October 2023
Introduction
12 October 2023
Importance of AQI prediction
1.Public Health: Accurate AQI prediction helps protect public health by providing early warnings
about deteriorating air quality, allowing individuals to take necessary precautions.
2.Environmental Protection: AQI prediction supports environmental conservation by identifying
sources of pollution, enabling regulatory agencies to implement measures to reduce emissions.
3.Policy Development: Reliable AQI forecasts inform the development of air quality regulations and
policies, helping governments make informed decisions to mitigate pollution.
4.Resource Allocation: Predicting AQI aids in allocating resources efficiently, as it enables
authorities to focus efforts and resources on areas with the greatest need for pollution control.
5.Quality of Life: Access to reliable AQI forecasts enhances the overall quality of life by enabling
individuals and communities to plan outdoor activities and reduce exposure to harmful pollutants.
Introduction
12 October 2023
Federated Learning
1.Privacy-Preserving Collaboration: Federated learning allows various air quality monitoring stations,
both public and private, to collaborate without sharing sensitive data. Each station trains a local model on
its data, and only model updates are shared, preserving privacy.
2.Localized Accuracy: By integrating data from multiple sources, federated learning provides more
comprehensive insights into air quality conditions in different locations. This approach helps in building
location-specific models that capture local variations effectively.
3.Efficiency and Resource Utilization: AQI prediction models benefit from federated learning's ability
to optimize model parameters across diverse datasets. This method enhances prediction accuracy, reduces
computational costs, and efficiently utilizes data resources from various monitoring stations.
12 October 2023
Literature Survey
S.no
Author
Title
Specification
Limitation
1
J. Ma, et al [1]
Air quality prediction at new stations
using spatially transferred bi-directional
long short-term memory network
Transfer Learning in Stacked BLSTM boosts air
pollution prediction accuracy for new stations with
limited data by transferring existing knowledge.
Performance relies on ample pre-training data
from existing stations, which can be limited in
some regions.
2
S.A Janabi et
al. [2]
A new method for prediction of air
pollution based on intelligent
computation
In SAQPM, PSO optimizes network parameters,
enhancing accuracy via architecture fine-tuning in
combination with RNN (LSTM).
The blend of DSN-PS and DLSTM elevates
computational complexity, causing longer
execution times, impacting real-time predictions.
A deep learning and image-based model
for air quality estimation
AQC-Net, a deep learning model using scene images,
improves air quality estimation for a holistic
understanding.
AQC-Net's accuracy improvement involves
increased model complexity, demanding more
computational resources for training and
deployment.
LSTM for PM10 and SO2 prediction outperforms MLP
and RNN models.
Focused on PM10 and SO2; didn't explore
meteorological influences on deep learning
models for broader assessment.
3
4
Q Zhang et al.
[3]
B Das et al. [4]
Prediction of air pollutants for air
quality using deep learning
methods in a metropolitan city
12 October 2023
Literature Survey
S.no
Author
Title
Specification
Limitation
5
M Krishan, et al. [5]
Air quality modelling using long
short-term memory (LSTM) over
NCT-Delhi, India
LSTM models considering meteorological
conditions, traffic data, and pollutant levels prove
efficient and effective for air pollutant prediction,
informing urban air quality research and policy.
Study focuses on Delhi's pollutants, limiting
generalizability; efficient LSTM models may
need substantial resources, posing constraints.
6
P S Mahesh, et al. [6]
A Survey Paper on Air Pollution
using IoT.
Enables real-time monitoring, cost-effectiveness,
wide applicability, and mobile potential.
Dependent on data thresholds, gas detection
limitations, and internet connectivity.
K Kumar, et al. [7]
Air pollution prediction with
machine learning: a case study of
Indian cities
Indian city air quality prediction with ML,
addressing imbalance, XGBoost excels.
Overlooks deep learning, limiting advanced
modeling exploration.
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Research Gap
12 October 2023
• Research gaps include the need to explore advanced deep learning methods in air quality
prediction, particularly in developing countries like India, and to address the limitations
of traditional methods.
• The potential benefits of integrating real-time data and IoT technologies for more robust
air quality monitoring and prediction have not been explored.
• While IoT-based air pollution monitoring systems show promise, there is limited
discussion about potential challenges, such as data security and privacy.
• Adapting LSTM models for air pollutant prediction to different locations and pollutants
with varying characteristics and sources is not addressed.
• The research could benefit from exploring hybrid approaches that combine deep learning
with traditional methods to enhance prediction accuracy.
Motivation & Objective
12 October 2023
• Enhanced Model Training: The motivation behind employing Federated Learning in
air quality prediction is to facilitate collaborative, location-specific model training,
addressing the challenge of varying pollutant characteristics and sources across regions
which are not done in previous works.
• Privacy-Preserving Data Sharing: To ensure data privacy and security while enabling
effective data sharing for air quality monitoring, thereby mitigating concerns related to
IoT technology and data security.
• Efficient Model Updates: Motivation stems from the need to efficiently update air
quality prediction models with real-time data, making it possible to harness the benefits
of real-time data integration.
Motivation & Objective
12 October 2023
• Robust and Generalizable Models: The objective is to develop robust and
generalizable air quality prediction models by aggregating knowledge from diverse
locations through Federated Learning.
• Preservation of Data Privacy: The primary objective is to preserve the privacy of air
quality data while enabling collaborative model training, ensuring that sensitive
information remains secure.
• Hybrid Approaches: The objective is to explore the synergies of combining deep
learning with traditional methods in air quality prediction while leveraging Federated
Learning to enhance accuracy, adaptability, and data privacy.
Problem Statement
12 October 2023
The study focuses on advancing Air Quality Index (AQI) prediction by harnessing
Federated Learning and Application Programming Interfaces (APIs). Challenges in
air quality forecasting, such as location-specific adaptability, privacy concerns, and realtime data integration, necessitate innovative solutions. The research employs Federated
Learning, a privacy-preserving method, to create an API-enabled AQI prediction
system. This system enhances accuracy, maintains data privacy, and explores hybrid
modeling, addressing the complexities of air quality prediction, especially in locations
with varying pollutant characteristics and sources. The work contributes to more effective
air quality monitoring and management.
Pre-requisite
12 October 2023
In addition to the previous attributes mentioned, the following factors may also be considered
for AQI prediction:
• Historical AQI data: This can help the model to learn the patterns of air pollution
over time.
• Chemical data: This can include data on the chemical composition of the air
pollution.
• Human activity data: This can include data on traffic patterns, industrial activity, and
energy consumption.
Pre-requisite
12 October 2023
The following attributes are required for AQI prediction:
• Air pollutant concentrations: This includes the concentrations of particulate matter
(PM2.5 and PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and
carbon monoxide (CO).
• Meteorological data: This includes temperature, humidity, wind speed and direction,
and precipitation.
• Topography: This includes the elevation and land cover of the area.
• Emission sources: This includes the location and type of emission sources, such as
vehicles, industry, and power plants.
Current Work
Field Descriptors of AQI attributes fetched using APIs:
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lat: Latitude (Degrees).
lon: Longitude (Degrees).
timezone: Local IANA Timezone.
city_name: City name.
country_code: Country abbreviation.
state_code: State abbreviation/code.
aqi: Air Quality Index [US - EPA standard 0 - +500]
o3: Concentration of surface O3 (µg/m³)
so2: Concentration of surface SO2 (µg/m³)
no2: Concentration of surface NO2 (µg/m³)
co: Concentration of carbon monoxide (µg/m³)
pm25: Concentration of particulate matter < 2.5 microns (µg/m³)
pm10: Concentration of particulate matter < 10 microns (µg/m³)
12 October 2023
Current Work
Sample attributes of AQI fetched using API:
12 October 2023
Current Work
Field Descriptors of Meteorological attributes fetched using APIs:
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Latitude: Latitude in degrees.
Longitude: Longitude in degrees.
Time: Timestamp or observation moment.
Temperature at 2m: Air temperature at 2 meters.
Relative Humidity at 2m: Moisture percentage at 2 meters.
Dew Point at 2m: Saturation temperature at 2 meters.
Apparent Temperature: "Feels-like" temperature.
Precipitation: Total moisture (rain, snow).
Rain: Liquid precipitation amount.
Snowfall: Snow depth or amount.
12 October 2023
Current Work
Field Descriptors of Meteorological attributes fetched using APIs:
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Pressure at MSL: Sea-level atmospheric pressure.
Surface Pressure: Surface atmospheric pressure.
Cloud Cover: Sky coverage percentage.
Cloud Cover (Low, Mid, High): Coverage at different levels.
ET0 (FAO Evapotranspiration): Evaporation/transpiration rate.
Wind Speed at 10m: 10m wind speed.
Wind Speed at 100m: 100m wind speed.
Wind Direction at 10m: 10m wind direction.
Wind Direction at 100m: 100m wind direction.
Wind Gusts at 10m: Sudden wind speed increases.
12 October 2023
Current Work
Sample attributes of Meteorological data fetched using API:
12 October 2023
Future Work
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Federated Learning Infrastructure
Collaborative Model Design
Communication and Aggregation
Location-Specific Adaptability
Real-Time Data Integration
Model Evaluation
API Integration
Testing and Validation
12 October 2023
References
12 October 2023
[1] Ma, Jun, Zheng Li, Jack CP Cheng, Yuexiong Ding, Changqing Lin, and Zherui Xu. "Air quality prediction at new stations using spatially
transferred bi-directional long short-term memory network." Science of The Total Environment 705 (2020): 135771.
[2] Al-Janabi, Samaher, Mustafa Mohammad, and Ali Al-Sultan. "A new method for prediction of air pollution based on intelligent computation." Soft
Computing 24, no. 1 (2020): 661-680.
[3] Zhang, Qiang, Fengchen Fu, and Ran Tian. "A deep learning and image-based model for air quality estimation." Science of The Total Environment
724 (2020): 138178.
[4] Das, Bihter, Ömer Osman Dursun, and Suat Toraman. "Prediction of air pollutants for air quality using deep learning methods in a metropolitan
city." Urban Climate 46 (2022): 101291.
[5] Krishan, Mrigank, Srinidhi Jha, Jew Das, Avantika Singh, Manish Kumar Goyal, and Chandrra Sekar. "Air quality modelling using long short-term
memory (LSTM) over NCT-Delhi, India." Air Quality, Atmosphere & Health 12 (2019): 899-908.
[6] Mahesh, Pattar Sunil, Patil Bhushan Rajendra, Bodke Akshay Dnyaneshwar, and Mr Ulhās V. Patil. "A survey paper on air pollution monitoring
using IOT." (2018).
[7] Kumar, K., and B. P. Pande. "Air pollution prediction with machine learning: a case study of Indian cities." International Journal of Environmental
Science and Technology 20, no. 5 (2023): 5333-5348.
Thank You