  In large lakes, ice cover plays an important role in shipping and navigation,
coastal erosion, regional weather and climate, and aquatic ecosystem function.
In this study, a novel deep learning model for ice cover concentration
prediction in Lake Michigan is introduced. The model uses hindcasted
meteorological variables, water depth, and shoreline proximity as inputs, and
NOAA ice charts for training, validation, and testing. The proposed framework
leverages Convolution Long Short-Term Memory (ConvLSTM) and Convolution Neural
Network (CNN) to capture both spatial and temporal dependencies between model
input and output to simulate daily ice cover at 0.1{\deg} resolution. The model
performance was assessed through lake-wide average metrics and local error
metrics, with detailed evaluations conducted at six distinct locations in Lake
Michigan. The results demonstrated a high degree of agreement between the
model's predictions and ice charts, with an average RMSE of 0.029 for the daily
lake-wide average ice concentration. Local daily prediction errors were
greater, with an average RMSE of 0.102. Lake-wide and local errors for weekly
and monthly averaged ice concentrations were reduced by almost 50% from daily
values. The accuracy of the proposed model surpasses currently available
physics-based models in the lake-wide ice concentration prediction, offering a
promising avenue for enhancing ice prediction and hindcasting in large lakes.
