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.

我们提出了一种自适应深度学习框架，可以为任何地点的降雨预测提供解决方案，并通过使用深度神经网络对巴黎、洛杉矶和东京的降雨预测进行适应后，取得了 43.51%、5.09% 和 38.62% 的改进。