Weekly Prediction of Tides Using Neural Networks. (2015)

• Record Type: Journal Article
• Authors: Salim, Akhil Muhammad; Dwarakish, G.S.; K.V., Liju; Thomas, Justin; Devi, Gayathri; R., Rajeesh
• Is Part Of: Procedia engineering. Volume 116(2015)
• Page Start: 678
• Page End: 682
• Publication Date: 2015
• Subjects: ANN -- Forecasting -- Tides -- Feed Forward Back Propagation -- Nonlinear Auto Regression.; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Conference proceedings; Periodicals; 620.005
• DOI: 10.1016/j.proeng.2015.08.351
• Languages: English
• ISSNs: 1877-7058

Abstract

Abstract: Knowledge of tide level is essential for explorations, safe navigation of ships in harbour, disposal of sediments and its movements, environmental observations and in many more coastal engineering applications. Artificial Neural Network (ANN) is being widely applied in coastal engineering field for solving various problems. Its ability to learn highly complex interrelationships based on the provided data sets, along with less amount of data requirement, makes it a powerful modelling tool. The present work is related to predicting the hourly tide levels at Mangalore, Karnataka, using a week's hourly tidal levels as input. The data has been obtained from NMPT, Mangalore and is made use of in predicting tide level using Feed Forward Back Propagation (FFBP) and Non-linear Auto Regressive with eXogenous input (NARX) network. FFBP network yielded correlation coefficient value of 0.564 and NARX network yielded very high correlation coefficient of the order 0.915 for predictions of yearlong hourly tide levels. The study proves that ANN technique can be successfully utilized for the prediction of tides at Mangalore.