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Watershed Planning within a Quantitative Scenario Analysis Framework
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A hybrid model for monthly runoff forecasting based on mixed signal processing and machine learning.

Shu Chen1,2, Wei Sun3,4, Miaomiao Ren5

  • 1Carbon-Water Research Station in Karst Regions of Northern Guangdong, School of Geography and Planning, Sun Yat-Sen University, Guangzhou, 510006, China.

Environmental Science and Pollution Research International
|November 28, 2024
PubMed
Summary

This study introduces a novel mixed signal processing model for monthly runoff forecasting, improving accuracy by using different machine learning algorithms for decomposed runoff components. The hybrid approach significantly enhances forecasting performance compared to traditional methods.

Keywords:
Deep learningMixed signal processing methodMonthly run off forecastingVariational mode decomposition

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Area of Science:

  • Hydrology and Water Resources Management
  • Machine Learning and Signal Processing
  • Environmental Science

Background:

  • Accurate monthly runoff forecasting is crucial for water resource planning and management.
  • Existing methods often use homogeneous models for decomposed runoff components, limiting individual component accuracy and overall performance.
  • Signal decomposition techniques are widely used but can be suboptimal when applied with uniform forecasting models.

Purpose of the Study:

  • To develop and evaluate a mixed signal processing model for monthly runoff forecasting.
  • To investigate the efficacy of employing heterogeneous machine learning models (SVM and LSTM) for different decomposed runoff components.
  • To compare the proposed hybrid model against traditional and homogeneous signal processing approaches.

Main Methods:

  • Variational Mode Decomposition (VMD) was used to decompose monthly runoff into components.
  • Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) models were applied heterogeneously to forecast original runoff and decomposed components.
  • Performance was evaluated by comparing the hybrid model with standalone SVM, LSTM, VMD-SVM, and VMD-LSTM models using metrics like R_avg and RMSE_avg.

Main Results:

  • The optimal hybrid model demonstrated superior forecasting accuracy, with validation R_avg values increasing by up to 3.5% and RMSE_avg values decreasing by up to 4.7% compared to other models.
  • Key input variables for the optimal model included sea surface temperature and 500 hPa geopotential height, indicating their significant influence on runoff in the study basin.
  • The study confirmed that heterogeneous forecasting models tailored to component characteristics improve overall monthly runoff prediction.

Conclusions:

  • A mixed signal processing approach using heterogeneous machine learning models offers significant improvements in monthly runoff forecasting accuracy.
  • Tailoring forecasting models to the specific characteristics of decomposed runoff components is essential for enhancing predictive performance.
  • While teleconnection factors are important, they may not be solely sufficient for highly accurate monthly runoff prediction.