Rainfall analysis and prediction using ensemble learning for Karnataka State

Govardhana Meti, Ravi Kumar Guralamata Krishnegowda, Gudihalli Savitha Swamy


Accurate prediction of rainfall can save farmers from crop damage as erratic rains in India have caused agricultural losses. Due to continuous climate change, rainfall and weather prediction has become essential. Rain causes damage to people and property. So, rainfall forecasting is crucial for crop as our country’s economy is still heavily dependent on agriculture. Using machine learning (ML) and deep learning (DL) models, we can train these models for the rainfall dataset and make predictions. In this paper, we estimate rainfall over the Karnataka region using a stacking ensemble model on a rainfall dataset collected between 1901 and 2015.


Ensemble; Rainfall forecasting; Rainfall prediction; Stacking; XGBoost regressor

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DOI: http://doi.org/10.11591/ijeecs.v32.i2.pp1187-1198


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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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