Accurately predicting algal blooms remains a critical challenge due to their dynamic and non-stationary nature, compounded by high-frequency fluctuations and noise in monitoring data. Additionally, a common issue in time-series forecasting is data replication, where models tend to replicate historical patterns rather than capturing true future variations, leading to inaccurate forecasts during abrupt changes. To address these challenges, we developed a hybrid deep learning model (TAB) that integrates a Temporal Convolutional Network (TCN), an attention mechanism, and Bidirectional Long Short-Term Memory (BiLSTM) network. Furthermore, we employed a novel distortion loss function-DIstortion Loss including shApe and TimE (DILATE)-which incorporates both shape and temporal losses to enhance the model's predictive robustness. Using in situ algal bloom data from Jiangdong Reservoir, Jiulong River, China, the TAB model accurately forecasted hourly chlorophyll-a dynamics for the subsequent 24 h, achieving an R