Journal of JSCE
Online ISSN : 2187-5103
ISSN-L : 2187-5103
Paper
TIDAL-LEVEL FORECASTING USING ARTIFICIAL NEURAL NETWORKS ALONG THE WEST COAST OF INDIA
Shetty RAKSHITHG. S. DWARAKISHUsha NATESAN
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2014 年 2 巻 1 号 p. 176-187

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 Knowledge of tide level is essential for safe navigation of ships in harbor, disposal and movement of sediments, environmental observations, explorations, and many more coastal and ocean engineering applications. Traditional methods such as harmonic analysis, least mean squares method, and hydrodynamic models have disadvantages in that they require excessive data, are time consuming, and are tedious to carry out. Artificial Neural Network (ANN) has been widely applied in the coastal engineering field in the last two decades for solving various problems related to time series forecasting of waves and tides; predicting sea-bed liquefaction and scour depth; and estimating design parameters of coastal engineering structures. Its ability to learn highly complex interrelationship based on provided data sets with the help of a learning algorithm, along with built-in error tolerance and less amount of data requirement, makes it a powerful modeling tool in the research community. In the present study, an attempt was made to predict tides at Karwar, located at the west coast of India, using a type of network called Non-linear Auto Regressive eXogenous input (NARX). It has an advantage in that the generated output is fed back to the network creating a loop. Conceptually, it differs in the fact that it uses the target given to it also as an input. Predictions were carried out for various durations using the weekly and monthly data sets. It was found that at Karwar, one year's prediction can be successfully carried out using one month data as an input with correlation coefficient (‘r’) greater than 0.97. The developed model was further applied to predict tides at New Mangalore Port Trust, Panambore, along the west coast of India, which is 260 kms south of Karwar. Results obtained were encouraging with ‘r’ value greater than 0.96.

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© 2014 Japan Society of Civil Engineers
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