Definitional Label Leakage in Aquaculture Water Quality Anomaly Detection

Semi-supervised anomaly detection is widely pro-posed for Internet of Things water quality monitoring in aquaculture. However, reported high scores often result from definitional label leakage. This occurs when an observation is labelled anomalous based on a physicochemical threshold, and that same variable is used as an input feature. The benchmark then measures threshold recovery rather than true anomaly detection. To quantify this artefact, we introduce two trivial reference detectors: an all-positive predictor and a per-channel range rule. Using a rigorous leakage-aware protocol, we evaluated Isolation Forest, one-class SVM, a Liquid State Machine, a convolutional autoencoder, and an LSTM autoencoder on two corpora: the three ponds of a public aquaponics dataset that survive a sensor-integrity screen fixed before modelling, eight of eleven ponds having been rejected, and a six-pond dataset from East Java in which all positive test windows originate from two ponds. One-class SVM proved the strongest detector on both corpora, achieving a Matthews correlation coefficient up to 0.77, although with two corpora the shared detector ordering is indicative rather than established. Crucially, on the public corpus, both autoencoders and the trivial range rule performed identically poorly, with recall capped near twenty-one percent. This collapse stems from normal-class contamination caused by duration-based labelling, which inflates the percentile threshold for reconstruction-based models. Furthermore, we demonstrate that the F1 score of a zero-information all-positive predictor exceeds several trained detectors, rendering F1 unsafe as a primary metric at these prevalences. Because the labels are derived from husbandry thresholds rather than expert annotation, and because our multi-seed statistics quantify reproducibility on fixed partitions rather than generalisation across farms or seasons, these results bound the interpretation of the benchmark and not field performance. We conclude by proposing a strict reporting protocol for aquaculture anomaly detection studies, with guidance for applying it retroactively to already-published corpora.

@article{Nahari2026,
  title     = {Definitional Label Leakage in Aquaculture Water Quality Anomaly Detection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {8},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Rosida Vivin Nahari and Teguh Prasetyo and Riza Alfita},
  doi       = {10.14569/IJACSA.2026.0170873},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170873}
}