Utilizing Machine Learning and Deep Learning Techniques for Forecasting Rainfall and Weather: A Review

Daniela Adolfina Ndaumanu; Risnu Irviandi

doi:10.53842/kzexvv23

Authors

Daniela Adolfina Ndaumanu College of Meteorology Climatology and Geophysics Author
Risnu Irviandi Diponegoro University Author

DOI:

https://doi.org/10.53842/kzexvv23

Keywords:

Machine Learning, Machine Learning, Deep Learning, Deep Learning, Weather Prediction, Weather Prediction, Rainfall Prediction, Rainfall Prediction, Rainfall Forecasting, Rainfall Forecast, Weather Forecast, Weather Forecast, Neural Network, Neural Network

Abstract

Machine learning and deep learning are vital for achieving precise rainfall and weather forecasting, which is crucial for agricultural planning, managing water resources, and reducing disaster risks. This study reviews a range of literature on weather and rainfall forecasting, emphasizing deep learning techniques. Additionally, it examines the performance of various machine learning models, including Long Short-Term Memory (LSTM) networks and Support Vector Regression (SVR), in improving forecast accuracy. These methods show notable improvements in accuracy over traditional models. The study’s findings suggest that enhanced machine learning and deep learning models can significantly benefit weather forecasting, aiding in climate change adaptation efforts.

Author Biographies

Daniela Adolfina Ndaumanu, College of Meteorology Climatology and Geophysics

Instrumentation-MCG Program Study, College of Meteorology Climatology and Geophysics
Risnu Irviandi, Diponegoro University

Physics Departement, Faculty of Science and Mathematics, Diponegoro University

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