Predicting water demand for spraying operations in dry bulk ports: A hybrid approach based on data decomposition and deep learning

Abstract

Dust pollution from materials in dry bulk ports (DBPs) significantly impacts air quality and public health in coastal cities. Spraying operations are the primary dust control measures in ports and accurately predicting water demand for these operations helps optimize water scheduling and conserve resources. However, challenges remain in addressing non-stationary time series and improving prediction accuracy. Additionally, existing studies rarely consider the impacts of port operations on water demand for spraying. Therefore, this study proposes a hybrid approach based on data decomposition and deep learning to predict water demand for spraying operations in DBPs. Port operational data is specifically integrated into the input features. An optimal variational mode decomposition (OVMD) method is introduced to reduce data non-stationarity. Compared to other methods, OVMD adaptively selects the optimal modes and effectively mitigates mode mixing issues. The 1-D Convolutional Neural Network integrated with an Attention BILSTM model, combined with OVMD, an Artificial Neural Network, and Error Correction, is employed to capture long-term temporal dependencies. Moreover, the relationship between material surface moisture content and water consumption for spraying operations is uniquely incorporated into the prediction process. This approach is compared with benchmark models using a dataset from a DBP in northern China. The results demonstrate that the proposed method achieves superior predictive performance, with a MAE of 0.47, a RMSE of 0.71, and an R2 of 0.95. The proposed approach enables port operators to accurately determine water consumption for spraying operations, thereby promoting the intelligent and sustainable development of dust control in DBPs.