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Modeling of Particulate Pollutants Using a Memory-Based Recurrent Neural Network Implemented on an FPGA
Julio Alberto Ramírez-Montañez1, Jose de Jesús Rangel-Magdaleno2, Marco Antonio Aceves-Fernández1
1Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico.
Micromachines
|September 28, 2023
Summary
This study developed an LSTM neural network for predicting air pollutants like nitrogen dioxide and particulate matter. The FPGA implementation achieved an 11% improvement, maintaining accuracy for 24- and 72-hour forecasts.
Area of Science:
- Environmental Science
- Computer Engineering
- Artificial Intelligence
Background:
- Accurate air quality assessment is vital for protecting ecosystems and human health.
- Criteria air pollutants include nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter (PM10, PM2.5).
- Overexposure to these pollutants poses significant risks.
Purpose of the Study:
- To train and implement a Long Short-Term Memory (LSTM) neural network on an FPGA board for air pollutant prediction.
- To evaluate the performance of a modified LSTM architecture on resource-constrained systems.
- To assess the feasibility of integrating predictive models into embedded systems for real-time air quality monitoring.
Main Methods:
- Development and training of an LSTM neural network model.
- Implementation of the trained model on a Field-Programmable Gate Array (FPGA) board.
- Comparative analysis of the FPGA-implemented model against the original LSTM network.
Main Results:
- The FPGA-implemented LSTM network demonstrated an 11% performance improvement over the original model.
- The modified architecture maintained prediction accuracy despite a reduction in neurons.
- Accurate predictions were achieved for both 24-hour and 72-hour time frames.
- Feasibility of integrating prediction networks into limited systems like FPGAs was confirmed.
Conclusions:
- The proposed LSTM network on an FPGA board offers an efficient solution for air pollutant modeling and prediction.
- This approach enables the deployment of advanced AI models in embedded systems without compromising accuracy.
- The findings highlight potential for enhanced air quality monitoring systems and further model optimization.
