highlights the efficacy of semi-supervised classification, which retains only normal values, in identifying abrupt changes and minor spikes in water quality. By contrast, supervised classification, which considers both normal and anomalous data, is more suitable in identifying long-term anomalies linked to gradual changes. Notably, the LSTM-based approach surpasses regressionbased autoregressive integrated moving average (ARIMA) in detecting these long-term anomalies.