A New Uncertainty-aware Deep Neuroevolution Model for Quantifying Tidal Prediction

In this work, we propose a deep learning-based prediction interval framework in order to model the forecasting uncertainties of tidal current datasets. The proposed model develops optimum prediction intervals (PIs) focused on the deep learning-based CNN-LSTM model (CLSTM), and non-parametric approach termed as the lower upper bound estimation (LUBE) model. On the other hand, due to the high complexity raises in designing manually the deep learning architectures, as well as the enhancing the performance of the prediction intervals, we develop a novel deep neuroevolution algorithm based on the two-stage modification of the Gaining-Sharing Knowledge (GSK) optimization algorithm to optimize the architecture of the CLSTM automatically without the procedure of trial and error. We also utilize coverage width criterion (CWC) to establish an excellent correlation appropriately between both the the PI coverage probability (PICP) and PI normalized average width (PINAW). We also indicate the searching efficiency and high accuracy of our proposed framework named as MGSK-CLSTM-LUBE by examining over the practical collected tidal current datasets from the Bay of Fundy, NS, Canada.The performance of the proposed model is examined on the practical tidal current data collected from the Bay of Fundy, NS, Canada.