Analyzing the Impact of High-Frequency Noise on Hydrological Runoff Modeling: A Frequency-Based Framework for Data Uncertainty Assessment
Year: 2026
Type: article
Abstract: The performance of deep learning-based hydrological forecasting is highly sensitive to input quality, yet existing studies lack a systematic framework to evaluate the impact of high-frequency noise based on hydrological characteristics. To address this, we propose a frequency-based framework to assess the robustness of LSTM runoff prediction models. We define three hydrologically meaningful noise types—long-term trend, short-term event, and transient interference—and employ a synthetic noise injection strategy on the CAMELS dataset. Furthermore, we introduce an adaptive exponentially weighted moving average (AEWMA) algorithm that dynamically adjusts smoothing based on local signal variability. Results from dual-domain evaluation (time and frequency) indicate that model accuracy deteriorates significantly when high-frequency noise exceeds 30% of the total signal energy. Moderate adaptive smoothing (e.g., α=0.9&0.6) effectively preserves hydrological signals while mitigating performance loss, whereas aggressive smoothing suppresses meaningful variations. This study underscores the necessity of noise-type-specific preprocessing and suggests spectral energy ratios as quantitative thresholds for adaptive data quality control in hydrological modeling workflows.
Source: Water
DOI: 10.3390/w18020195
Authors: Tianxu Liu, Wenyu Ouyang, Muhammad Adnan, Chi Zhang
Institution: Dalian University of Technology
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Award: G5021650697
OpenAlex ID: W7122765874