Floods are among the most devastating natural hazards, intensified by climate change and rapid urbanization. This study introduces a novel deep learning framework, Transfer Learning-Enhanced FEDformer (TLE-FEDformer), designed for accurate and temporally consistent flood inundation mapping. The framework integrates pre-trained Xception backbones for robust multi-sensor feature extraction from Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery, a cross-modal fusion module to align heterogeneous modalities, and the Frequency Enhanced Decomposed Transformer (FEDformer) for efficient frequency-domain temporal modeling. This architecture effectively captures long-range dependencies and flood dynamics including onset, peak, duration, and recession, while addressing challenges such as cloud contamination, speckle noise, and limited labeled data. Comprehensive experiments demonstrate superior performance, achieving an overall accuracy of 98.12%, an F1-score of 98.55%, and an Intersection over Union (IoU) of 97.38%, outperforming baselines including Convolutional Neural Networks, Capsule Networks, and transfer learning alone. Ablation studies validate the contributions of each component, while sensitivity analyses confirm robustness across hyperparameters. Uncertainty quantification via Monte Carlo dropout highlights high confidence in core flooded regions. Preliminary generalization tests on independent events yield IoU > 94%, indicating strong transferability. TLE-FEDformer advances operational flood monitoring by providing reliable, scalable, and temporally consistent mapping from multi-sensor remote sensing data. This approach offers significant potential for real-time disaster response, early warning systems, and damage assessment in flood-prone regions worldwide.

Floating breakwaters are commonly utilized in certain marine areas where conventional breakwaters are not viable, such as locations with temporary requirements for installation (i.e., seasonal ports), environmental considerations, and significant tidal ranges, etc. The operation of floating breakwaters in deep waters with short-crested waves is generally diagnosed correctly. The transmission coefficient, which measures the fraction of radiation transferred by a floating breakwater, is typically used to determine the efficiency of these structures. This key parameter may be calculated using physical and/or numerical models. This study investigated thirteen different geometries in two groups of the same volume and draft on a laboratory scale using the FLOW-3D software. Then, the impact of the wave number, wave steepness, the breakwater’s weight, and draft were investigated using FLOW-3D. The numerical results for a model were first verified against experimental data from a pontoon breakwater, and having established the model’s accuracy, a systematic parametric study was carried out to compare wave‐attenuation performance across a range of geometries. Subsequently, several triangular cogs were formed at the bottom of the floating structures. The numerical results show that creating these variations can improve the efficiency of these structures by up to 45.32% compared to simple flat-bottomed. In terms of the transmission coefficients, the results of the first group (same volume) rendered the breakwater more effective than those of the second group (same draft). Furthermore, the findings of this study will provide insights into the engineering principles underlying the design of a floating breakwater for spectral (random) waves.