Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Hydrodynamic performance of floating kelp farms: Wave attenuation and coastal protection potential(Elsevier Ltd, 2025) Surakshitha; Rao, M.; Rao, S.Ecologically rich coastal zone play a crucial role in supporting both biodiversity and the economy. “Soft solutions” for coastal protection, such as vegetated breakwaters and artificial reefs, harness natural features to mitigate coastal erosion. Among these, flexible floating vegetation, such as kelp farms, presents a unique mechanism by altering flow patterns differently than bed-fixed vegetation. This study experimentally investigates the effectiveness of floating kelp farms in dissipating wave energy under monochromatic regular waves. The wave heights ranging from 0.06 m to 0.18 m and periods of 1.6 s–2.8 s is considered. The study examines the effects of two non-dimensional parameters: relative farm width (w/L, 0.1 to 2.5) and relative blade length (l/d, 0.25–1.0), representing the ratios of farm width to wavelength and blade length to water depth, respectively. Under the test conditions investigated, the highest wave dissipation coefficient (Kd ? 0.8) is observed for relative blade lengths of 0.75 and 0.5 at a water depth of 0.45 m. The optimal farm configuration occurred at a relative farm width between 0.3 and 0.4. These findings contribute to a better understanding of the role of kelp farm in wave energy dissipation and highlight its potential as a sustainable alternative for coastal protection. © 2025 Elsevier LtdItem Predicting wave reflection coefficient of vertical caisson breakwater using machine learning: A data-driven approach(Elsevier Ltd, 2025) Shankara Krishna, A.; Rao, M.; Rao, S.Coastal zones are vital for ecological balance and human development, but are increasingly threatened by wave activity, shoreline erosion, and sea-level rise. To mitigate these challenges, engineers employ coastal protection structures. Specifically, vertical caisson breakwaters are preferred in deeper waters due to their advantages. Reflection Coefficient is an important hydrodynamic performance indicator for breakwaters. Recently, machine learning (ML) has been used for predicting coastal engineering parameters, offering an efficient means to support or augment traditional physical model studies, particularly during preliminary design phases, if sufficient quality data is available. This research focuses on using ML models to estimate the reflection coefficient of vertical caisson breakwaters based on a limited set of experimental data. Four different algorithms- Artificial Neural Network (ANN), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB)- are developed and evaluated. Hyperparameters are optimised using a hybrid approach, combining Grid Search with manual refinement. Of the four models, XGB achieved the highest prediction accuracy (Test CC = 0.9631), while Random Forest exhibited the smallest generalisation gap, indicating strong consistency across datasets. The findings from the study suggest that XGB offers an efficient tool for early-stage design applications in coastal engineering. © 2025 Elsevier Ltd