Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy

Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy

This study aims to integrate a broad spectrum of ocean-atmospheric variables to predict sea level variation along West Peninsular Malaysia coastline using machine learning and deep learning techniques. 4 scenarios of different combinations of variables such as sea surface temperature, sea surface sa...

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Bibliographic Details
Main Authors: Abdul-Lateef Balogun, Naheem Adebisi
Format: Article
Language:English
Published: Taylor & Francis Group 2021-01-01
Series:Geomatics, Natural Hazards & Risk
Subjects:
arima
lstm neural network
ocean-atmospheric variables
sea level anomaly
support vector regression (svr)
Online Access:http://dx.doi.org/10.1080/19475705.2021.1887372

Internet

http://dx.doi.org/10.1080/19475705.2021.1887372

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