Drought prediction based on an improved VMD-OS-QR-ELM model
Yang Liu1, Li Hu Wang1, Li Bo Yang1
1School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, Henan, China.
Plos One
|January 6, 2022
Summary
This study introduces an improved extreme learning machine (ELM) model for enhanced drought prediction accuracy and reliability. The novel VMD-OS-QR-ELM method significantly boosts prediction performance and computational efficiency.
Area of Science:
- Environmental Science
- Data Science
- Machine Learning
Background:
- Current drought prediction models suffer from low accuracy, poor reliability, and delays.
- Existing methods require significant improvements in predictive performance and efficiency.
Purpose of the Study:
- To develop a novel and highly accurate drought prediction model.
- To enhance the reliability and computational efficiency of drought forecasting.
Main Methods:
- Proposed a new extreme learning machine (ELM) model incorporating variational mode decomposition (VMD) and an online sequence learning mechanism (OS).
- Introduced orthogonal triangular matrix decomposition (QR) to redefine ELM hidden layer outputs (QR-ELM) and integrated online sequence learning (OS-QR-ELM).
- Utilized mutual information extension to improve VMD end effects, creating the VMD-OS-QR-ELM model.
Main Results:
- The VMD-OS-QR-ELM model demonstrated significant improvements in reliability (86.19%) and accuracy (93.20%) compared to support vector regression with empirical mode decomposition.
- Computational efficiency of the OS-QR-ELM model increased by 88.65% over ELM and 85.32% over QR-ELM.
- The integrated VMD-OS-QR-ELM approach offers superior drought prediction capabilities.
Conclusions:
- The VMD-OS-QR-ELM model represents a substantial advancement in drought prediction technology.
- This novel approach effectively addresses the limitations of existing drought forecasting methods.
- The enhanced model provides a more reliable and efficient tool for environmental monitoring and management.
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