Location Agnostic Adaptive Rain Precipitation Prediction using Deep
Learning
- URL: http://arxiv.org/abs/2402.01208v1
- Date: Fri, 2 Feb 2024 08:26:42 GMT
- Title: Location Agnostic Adaptive Rain Precipitation Prediction using Deep
Learning
- Authors: Md Shazid Islam, Md Saydur Rahman, Md Saad Ul Haque, Farhana Akter
Tumpa, Md Sanzid Bin Hossain, Abul Al Arabi
- Abstract summary: Rain precipitation prediction is a challenging task as it depends on weather and meteorological features which vary from location to location.
We have proposed an adaptive deep learning-based framework in order to provide a solution to the aforementioned challenges.
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.
- Score: 2.0971479389679337
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
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