A Whale Optimization Assisted Deep Metaheuristic Framework for IoT-Enabled Pipeline Leakage Detection and Failure Prediction

Leakage in oil, gas, and water pipelines poses severe environmental, financial, and operational challenges, especially when traditional monitoring systems fail to detect early or small leaks due to noisy sensor data and limited modeling intelligence. To address these limitations, this study introduces a Whale Optimization Assisted Deep Metaheuristic Learning (WOA-DML) framework designed for smart, IoT-enabled pipeline leakage detection and predictive failure analytics. The proposed system integrates heterogeneous pressure, acoustic, vibration, and flow-rate sensors with a hybrid CNN-BiLSTM model to effectively capture spatial-temporal signal patterns related to pipeline anomalies. The Whale Optimization Algorithm is employed to optimize hyperparameters and feature-selection weights, enabling faster convergence, improved anomaly sensitivity, and stable learning under uncertain operating conditions. Experimental evaluation using benchmark datasets demonstrates that the WOA-DML framework achieves a superior leakage detection accuracy of 98.46 %, with corresponding precision, recall, and F 1 -score values of$97.92 \%, 98.63 \%$, and 98.27 %, respectively. Comparative analysis shows statistically significant improvements ($\mathbf{p}