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A hybrid deep learning model-based LSTM and modified genetic algorithm for air quality applications.

Oumaima Bouakline1, Youssef El Merabet2, Abdelhak Elidrissi3

  • 1SETIME Laboratory, Department of Physics, Faculty of Science, Ibn Tofail University, B.P 133, Kenitra, 14000, Morocco. bouaklineoumaima1@gmail.com.

Environmental Monitoring and Assessment
|November 27, 2024
PubMed
Summary

This study introduces EFS-GA-LSTM, a novel deep learning model for accurate multistep PM10 air quality forecasting. The model improves prediction accuracy for hourly particulate matter concentrations.

Keywords:
Air qualityBayesian optimization with Gaussian processGenetic algorithmLong short-term memoryParticle swarm optimizationVariable neighborhood search

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Area of Science:

  • Environmental Science
  • Data Science
  • Computer Science

Background:

  • Advancements in computing and data storage have enabled extensive data analysis in air quality monitoring.
  • Accurate pollutant prediction is crucial for public health, despite progress in air quality modeling.
  • Deep learning models show promise for air quality prediction but require optimized hyperparameters and features.

Purpose of the Study:

  • To develop and evaluate a novel hybrid deep learning model for multistep hourly PM10 forecasting.
  • To optimize the model's architecture and feature selection using advanced algorithms.
  • To compare the proposed model's performance against established hyperparameter optimization techniques.

Main Methods:

  • Construction of a long short-term memory (LSTM) based model for multistep PM10 forecasting.
  • Utilizing a modified genetic algorithm (GA) for automatic model architecture design.
  • Employing principal component analysis (PCA) and exhaustive feature selection (EFS) for optimal feature identification.
  • Introducing the hybrid Enhanced Feature Selection-Genetic Algorithm-Long Short-Term Memory (EFS-GA-LSTM) model.
  • Comparing EFS-GA-LSTM with Particle Swarm Optimization (PSO), Variable Neighborhood Search (VNS), and Bayesian Optimization (BO) using hourly PM10, meteorological, and time data.

Main Results:

  • The EFS-GA-LSTM model demonstrated improved performance in 3-h-ahead forecasting tasks.
  • Key performance metrics including Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), correlation coefficient, and coefficient of determination were enhanced.
  • The hybrid approach effectively optimized hyperparameters and selected relevant features for accurate PM10 prediction.

Conclusions:

  • The novel EFS-GA-LSTM model offers a significant advancement in multistep hourly PM10 forecasting.
  • The integration of EFS and GA provides an effective strategy for optimizing deep learning models in air quality prediction.
  • The study highlights the potential of hybrid deep learning approaches for improving air quality monitoring and public health protection.