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A novel Encoder-Decoder model based on read-first LSTM for air pollutant prediction.

Bo Zhang¹, Guojian Zou², Dongming Qin³

¹College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, PR China.

The Science of the Total Environment

|January 8, 2021

Summary

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This summary is machine-generated.

This study introduces a novel Read-first LSTM (RLSTM) for improved air pollutant prediction. The new model enhances time series analysis, outperforming existing methods for accurate environmental pollution forecasting.

Area of Science:

Environmental Science and Computer Science
Artificial Intelligence
Time Series Analysis

Background:

Accurate air pollutant prediction is crucial for environmental management and preventing pollution incidents.
Existing air pollutant prediction models often use interdisciplinary approaches, formulating the problem as time series prediction.
Recurrent Neural Network (RNN) based Encoder-Decoder models, like Long Short-Term Memory (LSTM), show great potential but often ignore correlations between gate units.

Purpose of the Study:

To propose an improved LSTM, termed Read-first LSTM (RLSTM), as a more powerful temporal feature extractor.
To address the limitation of ignored gate unit correlations in existing LSTM models.
To develop and evaluate an Encoder-Decoder model using RLSTM for enhanced air pollutant prediction.

Keywords:

Air pollutant prediction Deep learning Encoder-Decoder model Long short term memory Numerical analysis Recurrent neural networks

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Main Methods:

Development of a novel Read-first LSTM (RLSTM) architecture.
RLSTM enhances temporal feature extraction by considering correlations between gate units and improving memory capabilities.
An Encoder-Decoder model (EDSModel) was constructed using RLSTM as the Encoder and standard LSTM as the Decoder for pollutant prediction.

Main Results:

The proposed EDSModel demonstrated strong performance in air pollutant prediction.
Experimental results showed effectiveness for predictions ranging from 1 to 24 hours.
The model achieved a root mean square error of 30.218, indicating high accuracy.

Conclusions:

The Read-first LSTM (RLSTM) is a superior temporal feature extractor compared to RNN, LSTM, and GRU.
The RLSTM-based Encoder-Decoder model (EDSModel) significantly improves air pollutant prediction accuracy.
The study highlights the effectiveness and superiority of the proposed RLSTM and EDSModel for environmental time series forecasting.