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Optimization algorithms as training approach with hybrid deep learning methods to develop an ultraviolet index

A A Masrur Ahmed1, Mohammad Hafez Ahmed2, Sanjoy Kanti Saha3

  • 1School of Mathematics Physics and Computing, University of Southern Queensland, Springfield, QLD 4300 Australia.

Stochastic Environmental Research and Risk Assessment : Research Journal
|March 1, 2022
PubMed
Summary

Forecasting daily solar ultraviolet index (UVI) using a hybrid deep learning model (CEEMDAN-CLSTM) with genetic algorithms (GA) significantly improved accuracy. This advance aids public health by providing better UV exposure advice.

Keywords:
Deep learningHybrid modelOptimization algorithmsPublic healthSolar ultraviolet index

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Area of Science:

  • Environmental Science
  • Public Health
  • Artificial Intelligence

Background:

  • The solar ultraviolet index (UVI) is crucial for public health, helping to prevent UV-exposure-related diseases.
  • Accurate UVI forecasting is essential for effective public health advisories and disease mitigation strategies.

Purpose of the Study:

  • To develop and compare hybrid deep learning models, specifically Convolutional Neural Network-Long Short-Term Memory (CLSTM), for daily UVI forecasting.
  • To integrate Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and feature selection algorithms for enhanced UVI prediction.

Main Methods:

  • Hybridization of CEEMDAN with CLSTM architecture for UVI time-series forecasting.
  • Application of four feature selection algorithms: Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Differential Evolution (DEV).
  • Utilized diverse datasets including satellite data, ground-based SILO data, and synoptic mode climate indices.

Main Results:

  • The CEEMDAN-CLSTM model combined with GA demonstrated superior performance in accurately forecasting daily UVI.
  • The proposed hybrid model exhibited excellent forecasting capabilities, characterized by low error and high efficiency.
  • The model effectively captured the complex, non-linear relationships between predictor variables and daily UVI.

Conclusions:

  • The hybrid CEEMDAN-CLSTM-GA model represents an accurate and efficient system for daily UVI forecasting.
  • Findings can significantly enhance real-time UV exposure advice, aiding public health initiatives.
  • This approach helps mitigate risks associated with solar UV-exposure-related diseases, such as melanoma.