eprintid: 15840
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/58/40
datestamp: 2023-11-10 03:30:28
lastmod: 2023-11-10 03:30:28
status_changed: 2023-11-10 02:00:32
type: article
metadata_visibility: show
creators_name: Balogun, A.-L.
creators_name: Adebisi, N.
title: Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models� accuracy
ispublished: pub
keywords: Atmospheric pressure; Autoregressive moving average model; Deep learning; Learning systems; Precipitation (meteorology); Predictive analytics; Sea level; Surface waters; Weather forecasting; Wind, Atmospheric variables; Comparison of models; Learning techniques; Neural network model; Sea level variability; Sea level variations; Sea surface salinity; Sea surface temperature (SST), Long short-term memory
note: cited By 31
abstract: 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 salinity, sea surface density, surface atmospheric pressure, wind speed, total cloud cover, precipitation and sea level data were used to train ARIMA, SVR and LSTM neural network models. Results show that atmospheric processes have more influence on prediction accuracy than ocean processes. Combining ocean and atmospheric variables improves the model prediction at all stations by 1- 9 for both SVR and LSTM. The means of R accuracy of optimal performing LSTM, SVR and ARIMA models at all stations are 0.853, 0.748 and 0.710, respectively. Comparison of model performance shows that the LSTM model trained with ocean and atmospheric variables is optimal for predicting sea level variation at all stations except Pulua Langkawi where ARIMA model trained without ocean-atmospheric variables performed best due to the dominating tide influence. This suggests that performance and suitability of prediction models vary across regions and selecting an optimal prediction model depends on the dominant physical processes governing sea level variability in the area of investigation. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
date: 2021
publisher: Taylor and Francis Ltd.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102176138&doi=10.1080%2f19475705.2021.1887372&partnerID=40&md5=72d33ca31edf06f61497dea572dcf8b3
id_number: 10.1080/19475705.2021.1887372
full_text_status: none
publication: Geomatics, Natural Hazards and Risk
volume: 12
number: 1
pagerange: 653-674
refereed: TRUE
issn: 19475705
citation:   Balogun, A.-L. and Adebisi, N.  (2021) Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models� accuracy.  Geomatics, Natural Hazards and Risk, 12 (1).  pp. 653-674.  ISSN 19475705