Developing Models Inspired by CNN and DNN for more Effective Precipitation Forecasting Techniques
Keywords:
Convolution Neural Network, City mean precipitation, forecasting, Deep Neural NetworkAbstract
The study aims to analyze some changes in rainfall behavior in (10) global cities, including Tokyo, Osaka, Shanghai, Chicago, Istanbul, Buenos Aires, Amsterdam, Madrid, Florance, and New York using an advanced machine learning (ML) approach for predicting the average precipitation. Utilizing the Deep Neural Network (DNN) and Convolution Neural Network (CNN) model, we have acquired mean precipitation monthly data from the selected cities with covering the period of 2000-2023. In order to assess mean precipitation in cities, we effectively employed both the average mutual information (AMI) during the model’s development. Constructed using both a training subset (80% of data from 2000 to 2018) and a testing subset (20% of data from 2019 to 2023), MP forecasting models for ten cities were established. When compared to the DNN model, the CNN model truly shined by showcasing its remarkable potential as a high-level model in accurately estimating mean precipitation values of cities while demonstrating superior generalization ability and minimal variance.
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