Location Agnostic Adaptive Rain Precipitation Prediction using Deep
  Learning
Md Shazid Islam Md Saydur Rahman Md Saad Ul Haque Farhana Akter Tumpa Md Sanzid Bin Hossain Abul Al Arabi
Rain precipitation prediction is a challenging task as it depends on weather
and meteorological features which vary from location to location. As a result,
a prediction model that performs well at one location does not perform well at
other locations due to the distribution shifts. In addition, due to global
warming, the weather patterns are changing very rapidly year by year which
creates the possibility of ineffectiveness of those models even at the same
location as time passes. In our work, we have proposed an adaptive deep
learning-based framework in order to provide a solution to the aforementioned
challenges. Our method can generalize the model for the prediction of
precipitation for any location where the methods without adaptation fail. Our
method has shown 43.51%, 5.09%, and 38.62% improvement after adaptation using a
deep neural network for predicting the precipitation of Paris, Los Angeles, and
Tokyo, respectively.