Leena Elneel's research while affiliated with University of Dubai and other places

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Publications (3)


Methodology of comparative analysis of employing autoregressive models in forecasting sea level variations.
Results of applying ARIMA model on seasonal GMSL data, where (a) shows the prediction results against the actual data, and (b) shows the long-term forecasting results of the model. (a) Actual vs. predicted seasonal GMSL. (b) Long-term Forecasting of seasonal GMSL.
Results of applying ARIMA model on non-seasonal GMSL data, where (a) shows the prediction results against the actual data, and (b) shows the long-term forecasting results of the model applying. (a) Prediction of non-seasonal GMSL data using ARIMA. (b) Long-term forecasting of non-seasonal data using ARIMA model.
Results of applying Prophet model on GMSl data, where (a) shows the prediction results against the actual data, and (b) shows the long-term forecasting results of the model applying. (a) Long-term forecasting of seasonal data. (b) Long-term forecasting of non-seasonal data.
Comparison of the GMSL forecasting results in accordance to historical observations (purple), and under the influence of CO2 emissions (blue), CH4 (orange), Ocean Heat (green), and Global Temperature Change (red).

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Examining sea levels forecasting using autoregressive and prophet models
  • Article
  • Full-text available

June 2024

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22 Reads

Scientific Reports

Leena Elneel

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Hussain Al-Ahmad

Global climate change in recent years has resulted in significant changes in sea levels at both global and local scales. Various oceanic and climatic factors play direct and indirect roles in influencing sea level changes, such as temperature, ocean heat, and Greenhouse gases (GHG) emissions. This study examined time series analysis models, specifically Autoregressive Moving Average (ARIMA) and Facebook’s prophet, in forecasting the Global Mean Sea Level (GMSL). Additionally, Vector Autoregressive (VAR) model was utilized to investigate the influence of selected oceanic and climatic factors contributing to sea level rise, including ocean heat, air temperature, and GHG emissions. Moreover, the models were applied to regional sea level data from the Arabian Gulf, which experienced higher fluctuations compared to GMSL. Results showed the capability of autoregressive models in long-term forecasting, while the Prophet model excelled in capturing trends and patterns in the time series over extended periods of time.

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Exploring Key Aspects of Sea Level Rise and Their Implications: An Overview

January 2024

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129 Reads

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1 Citation

Water

Leena Elneel

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[...]

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Hussain Al-Ahmad

Sea level rise (SLR) is one of the most pressing challenges of climate change and has drawn noticeable research interest over the past few decades. Factors induced by global climate change, such as temperature increase, have resulted in both direct and indirect changes in sea levels at different spatial scales. Various climatic and non-climatic events contribute to sea level changes, posing risks to coastal and low-lying areas. Nevertheless, changes in sea level are not uniformly distributed globally due to several regional factors such as wave actions, storm surge frequencies, and tectonic land movement. The high exposure to those factors increases the vulnerability of subjected areas to SLR impacts. The impacts of events induced by climate change and SLR are reflected in biophysical, socioeconomic, and environmental aspects. Different indicator-based and model-based approaches are used to assess coastal areas’ vulnerabilities, response to impacts, and implementation of adaptation and mitigation measures. Various studies have been conducted to project future SLR impacts and evaluate implemented protection and adaptation approaches, aiding policymakers in planning effective adaptation and mitigation measures to reduce damage. This paper provides an overview of SLR and its key elements, encompassing contributing factors, impacts, and mitigation and adaptation measures, featuring a dedicated section on the Arabian Gulf, a semi-enclosed sea.


Citations (2)


... Two approaches were employed to assess the univariate time series of GMSL. In the first scenario, the p, d, and q parameters representing ARIMA model components (see section "Autoregressive models" for more details) were set to (2,1,3) and applied to the seasonal GMSL time series. This indicates that 2 lagged observations (p) were used, a first-order differencing (d) was applied to ensure the stationarity requirement of the input data, and a moving average window of size 3 (q) was used. ...

Reference:

Examining sea levels forecasting using autoregressive and prophet models
Exploring Key Aspects of Sea Level Rise and Their Implications: An Overview

Water

... Error correction was performed in [39] on historical tide gauge data of Hong Kong coasts and used multiple regression models to calculate changes in sea levels. Regression models such as Auto-Regressive Integrated Moving Average (ARIMA) and Vector Auto-Regression (VAR) were also used to forecast changes in both GMSL and RMSL [96][97][98]. A comparative study on the use of historical GMSL data for forecasting using different machine and deep learning algorithms was conducted in [99], where the results showed that deep learning algorithms such as Dense Neural Network (DNN) and WaveNet Convolutional Neural Networks could provide more reliable results than linear regression. ...

Forecasting Global Mean Sea Level Rise using Autoregressive Models