001047378 001__ 1047378
001047378 005__ 20251127154852.0
001047378 0247_ $$2doi$$a10.1109/ISAS66241.2025.11101766
001047378 037__ $$aFZJ-2025-04266
001047378 1001_ $$0P:(DE-HGF)0$$aHassanian, Reza$$b0
001047378 1112_ $$a2025 9th International Symposium on Innovative Approaches in Smart Technologies$$cGaziantep$$d2025-06-27 - 2025-06-28$$gISAS$$wTurkiye
001047378 245__ $$aIntegrating remote sensing and deep learning forecasting model: A fluid-environment interface study
001047378 260__ $$bIEEE$$c2025
001047378 29510 $$a2025 9th International Symposium on Innovative Approaches in Smart Technologies (ISAS)
001047378 300__ $$an/a
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001047378 520__ $$aThis study proposes an approach that integrates remote sensing (RS) data with deep learning (DL) techniques to predict variations in lake area. The model incorporates key input variables, including historical rainfall and glacier temperature data, which are the primary contributors to lake inflow. As global warming accelerates, changes in glacier melting patterns and increased evaporation due to rising temperatures are significantly impacting water resource ecosystems. To address this, the study leverages multiyear historical data on correlated variables-glacier temperature, rainfall, and lake area-to forecast future lake area dynamics. A sequential deep learning model based on gated recurrent units (GRUs) is developed and evaluated using current RS data. The model achieves strong predictive performance, with a Root Mean Square Error (RMSE) of 0.005, a Mean Absolute Error (MAE) of 0.004, and a coefficient of determination (R2) of 0.99. These results demonstrate the effectiveness of integrating RS data with DL models for environmental forecasting, particularly when supported by wellestablished correlations among input variables.
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001047378 7001_ $$0P:(DE-Juel1)171343$$aCavallaro, Gabriele$$b1$$ufzj
001047378 7001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b2$$ufzj
001047378 770__ $$z979-8-3315-1482-2
001047378 773__ $$a10.1109/ISAS66241.2025.11101766
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001047378 9141_ $$y2025
001047378 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
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