Prediction of Tidal Elevations at Eastern and Western Coastal Areas of Sri Lanka with Short-term Data

Abstract

Prediction of tidal heights are increasingly beneficial for multitude of ocean functions such as port development, fishing industry, and safe movement of ships. As the general harmonic technique always needed great volumes of data for predicting tidal heights, Artificial Neural Networks (ANNs) emerged as a viable alternative for addressing diverse problems in the coastal engineering sector in recent decades. However, there has been no previous research to distinguish Harmonic Analysis from ANN models for predicting tidal heights around Sri Lanka by overcoming the rampant issue of data scarcity, which is the focus of the present study. Hourly tidal heights recorded in the Western (Colombo) and Eastern (Trincomalee) coastal areas of Sri Lanka were used in modelling. As tidal elevation is periodic in nature, it was expressed as Fourier Series with its coefficients (constituents) being determined by Harmonic Analysis, while the ANN technique employed the back-propagation procedure to forecast tidal heights. Harmonic Analysis displayed lesser prediction performance even with five months of data at Colombo (MSE=0.030 and MAPE=1.875) and Trincomalee (MSE=0.019 and MAPE=1.052), in contrast to the ANN models with only 7 days of data, which has much lower MSE and MAPE at Colombo (0.006 and 0.096) and (0.003 and 0.052) at Trincomalee respectively. Thus, the ANN model outperformed the Harmonic Analysis in terms of both accuracy and flexibility. Overall, this study demonstrated the potential of ANN modeling as a reliable, economical, and efficient alternative for predicting tidal heights to circumvent the dearth of tidal data on the coastal Sri Lanka.