Deep learning based long-term rainfall forecasting for meteorological subdivisions in india

Abstract

Rainfall forecasting is very important because heavy and irregular rainfall can have many impacts like destruction of crops and farms, damage of property so a better forecasting model is essential for an early warning that can minimize risks to life and property and managing the agricultural farms in better way. This prediction mainly helps farmers and water resources can be utilized efficiently. Rainfall prediction is a challenging task and the results should be accurate. A good forecast of rainfall is essential for proper agricultural investment. Prediction of time series data in meteorology can assist in decision-making processes carried out by organizations responsible for the prevention of disasters. This paper presents Multi-layer Long Short-Term Memory (Multi-layer LSTM) based Recurrent Neural Network (RNN), Functional Transduction and Conformer model to predict rainfall. The neural network is trained and tested using a standard dataset of rainfall. The parameters considered for the evaluation of the performance and the efficiency of the proposed rainfall prediction model are Root Mean Square Error (RMSE), accuracy, number of epochs, loss, and learning rate of the network.