• Development of hybrid wave transformation methodology and its application on Kerala Coast, India

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    • Keywords


      Wave transformation; wave climate; DELFT3D-WAVE; ANN; Kerala coast.

    • Abstract


      A major portion of the coastline of Kerala is under erosion, primarily due to the action of wind-generated waves. Accurate assessment of the nearshore wave climate is essential for detailed apprehension of the sediment processes that lead to coastal erosion. Numerical wave transformation models set up incorporating high-resolution nearshore bathymetry and nearshore wind data, prove to be sufficient for the purpose. But, running these models for decadal time scales incur huge computational cost. Thus, a Feed Forward Back Propagation ANN is developed to estimate the wave parameters nearshore with training datasets obtained from minimal set of numerical simulations of wave transformation using DELFT3D-WAVE. The numerical model results are validated using Wave Rider Buoy data available for the location. This hybrid methodology is utilized to hindcast nearshore wave climate of a location in north Kerala for a period of 40 years with the ANN model trained with 1-yr data. The model shows good generalization ability when compared to the results of numerical simulation for a period of 10 years. This paper illustrates the data and methodology adopted for the development of the numerical model and the proposed ANN model along with the statistical comparisons of the results obtained.


      $\bullet$ A hybrid methodology, combining numerical modelling and soft computation using ANNs, is developed to obtain long-term nearshore wave hindcast. One years’ numerical model simulation is utilised to train the ANN models.

      $\bullet$ The optimised ANN$_{H}$ , ANN$_{T}$ , ANN$_{ θmx }$ and ANN$_{θmx}$ models, with 15, 25, 25 and 30 neurons respectively in their single hidden layer, show good generalization ability when compared to the results of numerical simulation for a period of 10 years. The coefficient of correlation between the numerical model results and the ANN$_{H}$ model is 0.99. Results of ANN$_{T}$ model and the combined result of ANN$_{θmx}$ , ANN$_{θmy}$ models show a coefficient of correlation of 0.97 with the corresponding numerical model results. The new methodology allows for faster reconstruction of long-term time series of nearshore wave parameters.

      $\bullet$ The trained models are used for simulating nearshore wave parameters at a location in North Kerala coast for 40 years. The maximum H$_{s}$ at the nearshore location from 40 years’ ANN simulation is 3.39 m. H$_{s}$ exceeds 3 m only for 0.04% of the time. During monsoon, waves feature a narrow range of T$_{p}$ as well as mean wave direction as opposed to the non-monsoon period.

    • Author Affiliations



      1. National Institute of Technology Calicut, Kozhikode, Kerala, India
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