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A researcher at the UECSDR obtains acceptance to publish a scientific research in the Clarivate container
2025-03-25
Assistant Lecturer Mohamed Majeed Hamid, a researcher in the Department of Climate Meteorology, obtained acceptance for the publication of a scientific research in a sober journal within the Clarivate Scientific Publishing Container with the participation of researchers from Malaysian, Hungarian, Indian and Egyptian universities. The researcher obtained acceptance of the publication of the tagged research.
(Forecasting Monthly Runoff in a Glacierized Catchment: A Comparison of Extreme Gradient Boosting (XGBoost) and Deep Learning Models)
In PLOS ONE, a Q1 magazine, in the Clarivate Sight Score Container: 6.2.
The research deals with the study of surface flow in one of the glacial watersheds in the Sweden region (the study area) as it depends heavily on the melting of ice and is very sensitive to climate changes, which plays a major role in affecting the amount of surface flow. The accelerated melting of ice as a result of climate changes and the noticeable rise in temperatures increases the amount of surface floods and thus causes natural hazards such as floods, so the future forecasting of the amount of surface water is a very complex dynamic process and the accuracy of forecasting helps in better water management as well as contributes to the development of an effective plan to reduce the side effects of floods that threaten the areas surrounding the studied area.
The study used several models of artificial intelligence, deep learning models, and machine learning models, as well as a comprehensive evaluation of the performance of the models used in this scientific paper, in addition to that, an algorithm was also developed that helps to discover complex points in the time series of surface flow data by identifying inversion points, which are considered key points in the study of surface flow comprehensively as these points are considered points in which there is a sudden and rapid change in the amount of surface and thus choose the best model that can Simulating these points is a good model with high predictive efficiency and reliability.
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