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

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TLDR
In this article, a broad spectrum of ocean-atmospheric variables were integrated to predict sea level variation along West Peninsular Malaysia coastline using machine learning and deep learning technologies.
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 techniq...

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Citations
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Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting

TL;DR: In this paper, the authors compared the performance of four data-driven techniques of Linear Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) network in daily streamflow forecasting.
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Convolutional Neural Network and Optical Flow for the Assessment of Wave and Tide Parameters from Video Analysis (LEUCOTEA): An Innovative Tool for Coastal Monitoring

TL;DR: In this paper , an innovative system composed of a combined approach between Geophysical surveys, Convolutional Neural Network (CNN), and Optical Flow techniques were used to assess tide and storm parameters by a video record.
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Ocean Remote Sensing Techniques and Applications: A Review (Part I)

TL;DR: In this paper , 15 applications of Remote Sensing (RS) in the ocean using different RS techniques and systems are comprehensively reviewed and discussed, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD).
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Assessing the Impacts of Rising Sea Level on Coastal Morpho-Dynamics with Automated High-Frequency Shoreline Mapping Using Multi-Sensor Optical Satellites

TL;DR: In this paper, a Google Earth Engine (GEE)-enabled Python toolkit was used to perform high-frequency data sampling to analyze the impact of sea-level rise on the Malaysian coastline between 1993 and 2019.
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A deep-learning model for national scale modelling and mapping of sea level rise in Malaysia: the past, present, and future

TL;DR: In this paper, the authors conducted a holistic evaluation of current and future trend in coastal sea level at the 21 stations along Malaysia's coastline for sea level prediction, univariate and 3 scenario.
References
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Time series analysis, forecasting and control

TL;DR: Time series analysis san francisco state university, 6 4 introduction to time series analysis, box and jenkins time seriesAnalysis forecasting and, th15 weeks citation classic eugene garfield, proc arima references 9 3 sas support, time series Analysis forecasting and control pambudi, timeseries analysis forecasting and Control george e.
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Another look at measures of forecast accuracy

TL;DR: In this paper, the mean absolute scaled error (MESEME) was proposed as the standard measure for comparing forecast accuracy across multiple time series across different time series types, and was used in the M-competition as well as the M3competition.
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Sea-Level Rise and Its Impact on Coastal Zones

TL;DR: Although the impacts of sea-level rise are potentially large, the application and success of adaptation are large uncertainties that require more assessment and consideration.
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