Open AccessA Hybrid Model Approach for the Prediction of Rainfall
Author(s) -
M. Mallika,
M. Nirmala
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1770/1/012108
Subject(s) - mean squared error , mean absolute percentage error , precipitation , mean absolute error , statistics , mean square , approximation error , mathematics , environmental science , meteorology , geography
For maintaining the atmospheric balance, precipitation is very much helpful. Rainfall is one of the forms of precipitation Though excess rainfall causes several damages to the earth in various ways, it is considered very precious as it is one of the essentials for the existence of human beings. As a result, a prediction of rainfall also forms a major part in planning things. This paper proposes a new hybrid model Moving average-kNN for doing the prediction. Error measures Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) have been used for the validity of the model.