Applications of Deep Learning Models for Forecasting and Modelling Rainwater in Moscow

¹ University of Alkafeel, Najaf, Iraq
² Centurion University of Technology and Management, Odisha, India
³ South Ural State University, Chelyabinsk, Russia
⁴ G. B. Pant University of Agriculture and Technology, Uttarakhand, India
⁵ VCSG Uttarakhand University of Horticulture and Forestry, Uttarakhand, India
⁶ Jawaharlal Nehru Krishi Vishwavidyalaya, Jabalpur, India
⁷ University of Delhi, New Delhi, India
⁸ Indian Institute of Forest Management, Bhopal, India
⁹ Lagos State University, Lagos, Nigeria

^* Corresponding Author: ali.j.r@alkafeel.edu.iq

Abstract

To model and forecast complex time series data, machine learning has become a major field. This machine learning study examined Moscow rainfall data's future performance. The dataset is split into 65% training and 35% test sets to build and validate the model. We compared these deep learning models using the Root Mean Square Error (RMSE) statistic. The LSTM model outperforms the BILSTM and GRU models in this data series. These three models forecast similarly. This information could aid the creation of a complete Moscow weather forecast book. This material would benefit policymakers and scholars. We also believe this study can be used to apply machine learning to complex time series data, transcending statistical approaches.