Real-time prediction of the week-ahead flood index using hybrid deep learning algorithms with synoptic climate mode indices

Abstract

This paper aims to propose a hybrid deep learning (DL) model that combines a convolutional neural network (CNN) with a bi-directional long-short term memory (BiLSTM) for week-ahead prediction of daily flood index (I_F) for Bangladesh. The neighbourhood component analysis (NCA) is assigned for significant feature selection with synoptic-scale climatic indicators. The results successfully reveal that the hybrid CNN-BiLSTM model outperforms the respective benchmark models based on forecasting capability, as supported by a minimal mean absolute error and high-efficiency metrics. With respect to I_F prediction, the hybrid CNN-BiLSTM model shows over 98% of the prediction errors were less than 0.015, resulting in a low relative error and superiority performance against the benchmark models in this study. The adaptability and potential utility of the suggested model may be helpful in subsequent flood monitoring and may also be beneficial to policymakers at the federal and state levels.