Seasonal Anomaly Detection in the Halda River Using a Multivariate Deep Learning Framework

MD. Minaruzzaman Shovon , H A Hossain Tamjid , Md. Tamim

Monitoring river water quality is essential to preserving
ecological integrity, especially in ecologically significant
rivers like the Halda, which is renowned for its natural
freshwater carp spawning. This study presents a deep
learning-based approach using a deep autoencoder neural
network for unsupervised anomaly detection in water quality
data. Two-year time-series data including daily measurements
of pH, turbidity, alkalinity, and chloride concentration was
utilized. The autoencoder learns compressed representations
of normal water behavior and flags anomalies based on
elevated reconstruction errors. Temporal features such as
month and climate-based seasons (Dry, Pre-Monsoon, Monsoon,
and Post-Monsoon) were added to enhance interpretability.
The model successfully detected nine anomalous days, with
78% occurring during the dry season due to low freshwater
discharge and salinity intrusion. The developed model enables
early detection of abnormal shifts in water quality and
provides actionable insights for stakeholders and
policymakers for timely environmental interventions.