Mr. Shahira Abdul Lathif Profile

Shahira Abdul Lathif

at Khalifa Univ

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Publications (1)

Proceedings Article | 3 January 2025 Paper

High-resolution monitoring and modelling of thermal plumes from nuclear power plants in the Arabian Gulf

Fahim Abdul Gafoor, Shahira Abdul Lathif, Yacine Addad, Sultan Al Shehhi, Maryam Al Shehhi

Proceedings Volume 13264, 132640E (2025) https://doi.org/10.1117/12.3046095

KEYWORDS: Nuclear power plants, Landsat, Data modeling, Temperature metrology, Satellites, Satellite imaging, Education and training, Earth observing sensors

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Thermal discharges from coastal power plants can significantly impact marine ecosystems, particularly in environmentally sensitive regions. This study focuses on the Barakah Nuclear Power Plant, situated in the hypersaline and thermally extreme waters of the Arabian Gulf. By leveraging satellite observations and advanced machine learning techniques, this research aims to enhance the monitoring and understanding of thermal plume dynamics, providing valuable insights into their spatial and temporal characteristics. In particular, the study seeks to downscale sea surface temperature (SST) data from a coarse resolution of ~2 km (GHRSST) to a high resolution of 100 m, enabling improved spatial and temporal analysis of thermal anomalies. Ambient seawater temperatures in the region, reaching ~36°C during late summer, underscore the importance of accurately assessing thermal plume dispersion. Landsat 8/9 imagery was utilized to derive SST from Band 10 and Band 11, with validation against in-situ measurements confirming its reliability. However, the limited 8-day revisit interval of Landsat satellites restricts continuous monitoring. To address this, the study employed the Extreme Gradient Boosting (XGBoost) algorithm, incorporating GHRSST SST, wind data, and SST biases as input features. The downscaling from ~2 km to 100 m resolution provided enhanced spatial detail of SST patterns. The model was trained on data spanning 2017–2021 and validated with 2022 data, achieving an R² of 0.94 and an RMSE of 1.23°C. The downscaled SST accurately resolved thermal plumes, demonstrating strong agreement with reference data and enabling finer characterization of plume dispersion patterns. This integrated approach highlights the potential of combining satellite remote sensing and advanced machine learning techniques to monitor thermal discharges at high spatial and temporal resolutions, offering a robust framework for assessing environmental impacts in ecologically sensitive coastal regions.

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