# Dynamic Hyperbolic Tangent PSO-Optimized VMD for Pressure Signal Denoising and Prediction in Water Supply Networks

## TL;DR

This paper introduces a new denoising method for water supply pressure signals that improves forecasting accuracy using an optimized VMD approach.

## Contribution

The novel DHTPSO-VMD framework combines dynamic PSO optimization with dual-criteria IMF screening for enhanced signal denoising.

## Key findings

- DHTPSO-VMD outperformed benchmark methods in SNR, MAE, and MSE metrics on real-world pressure data.
- Preprocessing with DHTPSO-VMD improved prediction accuracy with an R2 score of 0.948924 using the Informer model.
- The dual-criteria screening strategy effectively identified and removed noise-related IMFs.

## Abstract

Urban water supply networks are prone to complex noise interference, which significantly degrades the performance of data-driven forecasting models. Conventional denoising techniques, such as standard Variational Mode Decomposition (VMD), often rely on empirical parameter selection or optimize only a subset of parameters, lacking a robust mechanism for identifying noise-dominant components post-decomposition. To address these issues, this paper proposed a novel denoising framework termed Dynamic Hyperbolic Tangent PSO-optimized VMD (DHTPSO-VMD). The DHTPSO algorithm adaptively adjusts inertia weights and cognitive/social learning factors during iteration, mitigating the local optima convergence typical of traditional PSO and enabling automated VMD parameter selection. Furthermore, a dual-criteria screening strategy based on Variance Contribution Rate (VCR) and Correlation Coefficient Metric (CCM) is employed to accurately identify and eliminate noise-related Intrinsic Mode Functions (IMFs). Validation using pressure data from District A in Zhejiang Province, China, demonstrated that the proposed DHTPSO-VMD method significantly outperforms benchmark approaches (PSO-VMD, EMD, SABO-VMD, GWO-VMD) in terms of Signal-to-Noise Ratio (SNR), Mean Absolute Error (MAE), and Mean Square Error (MSE). Subsequent forecasting experiments using an Informer model showed that signals preprocessed with DHTPSO-VMD achieved superior prediction accuracy (R2 = 0.948924), underscoring its practical utility for smart water supply management.