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@INPROCEEDINGS{Hassanian:1047378,
      author       = {Hassanian, Reza and Cavallaro, Gabriele and Riedel, Morris},
      title        = {{I}ntegrating remote sensing and deep learning forecasting
                      model: {A} fluid-environment interface study},
      publisher    = {IEEE},
      reportid     = {FZJ-2025-04266},
      pages        = {n/a},
      year         = {2025},
      comment      = {2025 9th International Symposium on Innovative Approaches
                      in Smart Technologies (ISAS)},
      booktitle     = {2025 9th International Symposium on
                       Innovative Approaches in Smart
                       Technologies (ISAS)},
      abstract     = {This 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.},
      month         = {Jun},
      date          = {2025-06-27},
      organization  = {2025 9th International Symposium on
                       Innovative Approaches in Smart
                       Technologies, Gaziantep (Turkiye), 27
                       Jun 2025 - 28 Jun 2025},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / EUROCC-2 (DEA02266)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel-1)DEA02266},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1109/ISAS66241.2025.11101766},
      url          = {https://juser.fz-juelich.de/record/1047378},
}