MSFA: Multi-Strategy Fusion Algorithm for Data Cleaning and Its Application in Offshore Marine Environmental Monitoring
Marine monitoring records collected from buoys and nearshore sensors are often affected by missing values, abrupt spikes, and short-term fluctuations. These errors are difficult to remove with a single detection or interpolation rule, especially when local anomalies and global outliers occur in the same sequence. This study develops a Multi-Strategy Fusion Architecture (MSFA) for cleaning marine environmental time-series data. In MSFA, DBSCAN is not applied directly to the raw observations; instead, the time index and measurement value are first normalized into a common feature space, where local density anomalies can be detected more consistently. IQR screening is then used to identify global extreme values. After abnormal positions are marked, the repair result is estimated from two complementary sources: linear interpolation, which follows local temporal change, and a moving average based only on neighboring valid observations, which reduces random noise. Their contributions are adjusted according to local reliability rather than fixed manually. Because initial repair may still leave small residual errors, we further use a Combined Residual Metric (CRM) with a median/MAD-based threshold to recheck the repaired sequence and update the abnormal-position set when necessary. Experiments on the 2020 Dongying offshore buoy dataset and a self-collected nearshore dataset show that MSFA achieves AUROC/AUPRC/NRMSE values of 0.896/0.855/0.066 and 0.986/0.915/0.0653, respectively. Compared with DBSCAN+LOF, DBSCAN+Transformer, and IQR+Sigmoid, MSFA improves AUROC and AUPRC by about 12–25% on average and reduces NRMSE by more than 40%. These results indicate that the proposed method can improve the usability of noisy and incomplete marine monitoring data while keeping the cleaning process interpretable.