Data Anomaly Identification and Noise Reduction Application of Eco-Environment IOT Monitoring System Based on Wavelet Transform Theory

The proliferation of Internet of Things (IoT) sensors in ecological monitoring presents critical challenges related to data quality. Noise and anomalies in raw sensor data can compromise the accuracy of subsequent analyses and early-warning systems. This paper proposes and validates a two-stage methodology to address this issue. First, a Z-Score analysis is employed for statistical anomaly detection. Subsequently, a multi-scale Discrete Wavelet Transform (DWT), coupled with a soft thresholding procedure, is used for robust signal denoising before reconstruction via the Inverse DWT. The efficacy of this approach was validated using an in-situ pH monitoring dataset from Shijiu Lake. The results demonstrated a marked improvement in data quality. Compared to the original data, the denoised signal showed significant reductions in key metrics when benchmarked against ground-truth data: Standard Deviation (SD) decreased by 13.69%, Root Mean Square Error (RMSE) by 13.62%, and Mean Absolute Error (MAE) by 15.75%. These findings confirm that the proposed methodology provides an effective and practical tool for enhancing the integrity and reliability of real-time environmental monitoring data, establishing a stronger foundation for scientific analysis and decision-making.