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Watershed Planning within a Quantitative Scenario Analysis Framework

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Published on: July 24, 2016

Enhancing urban air quality prediction using time-based-spatial forecasting framework.

Shrikar Jayaraman¹, Nathezhtha T², Abirami S¹

¹Vellore Institute of Technology Chennai, Chennai, India.

Scientific Reports

|February 3, 2025

Summary

This study introduces a Time-Based-Spatial (TBS) framework for accurate Air Quality Index (AQI) forecasting. By integrating Convolutional Neural Networks (CNNs) and Auto-Regressive Integrated Moving Average (ARIMA) models, it enhances environmental management and public health strategies.

Keywords:

AQI ARIMA CNN Forecasting Spatial characteristics TBS Temporal dependencies

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Area of Science:

Environmental Science
Data Science
Urban Planning

Background:

Accurate air quality forecasting is crucial for environmental management, public health, and urban planning.
Existing methods often struggle to integrate complex spatial and temporal data effectively.

Purpose of the Study:

To develop and validate a novel Time-Based-Spatial (TBS) framework for Air Quality Index (AQI) forecasting.
To leverage machine learning to integrate geographical and temporal pollutant data for improved prediction accuracy.

Main Methods:

The TBS framework combines Convolutional Neural Networks (CNNs) for spatial dependency analysis using normalized coordinates.
Auto-Regressive Integrated Moving Average (ARIMA) models capture temporal dependencies from pollutant concentration time series.
Data preprocessing involved cleaning, normalization, and splitting into training and testing sets for model development.

Main Results:

The TBS model demonstrated significant accuracy in predicting Air Quality Index (AQI) values.
The integration of CNNs and ARIMA models provided a deeper understanding of factors influencing air quality variations.
A 6-hour AQI forecast was successfully generated for test instances.

Conclusions:

The TBS framework offers a robust and accurate approach to air quality forecasting.
Combining spatial and temporal machine learning models enhances the prediction of AQI.
This research contributes to better environmental monitoring and informed public health decisions.