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syN-BEATS for robust pollutant forecasting in data-limited context.

Josef Berman¹, Ben Pinhasov², Moshe Tshuva²

¹Intelligent Systems, Afeka College of Engineering, Tel Aviv, Israel. josef.berman@outlook.com.

Environmental Monitoring and Assessment

|October 2, 2024

Summary

This study presents syN-BEATS, a new deep learning model for accurate air pollutant forecasting, even with limited data. It outperforms existing methods, aiding health alerts in under-resourced regions.

Keywords:

Deep learning Ensemble models Forecast Meteorology Pollution

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Area of Science:

Environmental Science
Artificial Intelligence
Data Science

Background:

Accurate air pollutant forecasting is crucial for public health.
Limited monitoring data poses a significant challenge for traditional forecasting models.
Developing robust models for data-scarce environments is essential for environmental management.

Purpose of the Study:

To introduce syN-BEATS, an ensemble deep learning model for pollutant forecasting with limited data.
To evaluate syN-BEATS' performance against standard models under data-constrained conditions.
To demonstrate the model's utility in supporting health alert systems in under-resourced areas.

Main Methods:

Developed syN-BEATS, an ensemble deep learning model based on the N-BEATS architecture.
Integrated diverse configurations of stacks and blocks, combining weak and strong learning.
Utilized Bayesian optimization for fine-tuning ensemble weights.
Simulated limited data environments using single meteorological and air quality monitoring stations per region.

Main Results:

syN-BEATS demonstrated superior performance compared to standard models.
The model achieved consistently low relative root mean square errors, indicating precise forecasting.
Bayesian optimization significantly enhanced ensemble weight tuning and model accuracy.
The model proved flexible and resilient across diverse climatic and air quality conditions.

Conclusions:

syN-BEATS offers effective pollutant forecasting capabilities, particularly in data-limited scenarios.
The model's performance supports the development of public health alert systems in resource-limited regions.
This research advances environmental monitoring and public health management strategies for areas with restricted infrastructure.