Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy-Reference-Cited by-同舟云学术

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

Published:2021-01-01 Issue:1 Volume:12 Page:653-674
ISSN:1947-5705
Container-title:Geomatics, Natural Hazards and Risk
language:en
Short-container-title:Geomatics, Natural Hazards and Risk

Author:

Balogun Abdul-Lateef¹^ORCID,Adebisi Naheem¹^ORCID

Affiliation:

1. Geospatial Analysis and Modelling (GAM) Research group, Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS (UTP), Perak, Malaysia

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,General Environmental Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/19475705.2021.1887372

Reference54 articles.

1. Satellite Altimetry-Based Sea Level at Global and Regional Scales

2. An Empirical Comparison of Machine Learning Models for Time Series Forecasting

3. Nonlinear sea level trends from European tide gauge records

4. Contemporary Sea Level Rise

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