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

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 sufBcient 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 DELFT3DWAVE. 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.

Automated summary

The study demonstrates that numerical wave transformation models and Artificial Neural Network (ANN) models can effectively estimate the nearshore wave climate of Kerala's coast, reducing computational costs and showing good predictive ability.