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Home
Rainfall Forecast In Brazil Using Machine Learning.

Home
Rainfall Forecast In Brazil Using Machine Learning.

Related Experiment Video

A Protocol for Conducting Rainfall Simulation to Study Soil Runoff

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Published on: April 3, 2014

Rainfall forecast in Brazil using machine learning.

Sidney T da Silva¹, Letícia C Milani¹, Enrique C Gabrick²

¹Department of Chemical, Federal University of Paraná, Curitiba 81531-980, PR, Brazil.

Chaos (Woodbury, N.Y.)

|July 7, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Machine learning models accurately predict rainfall across five Brazilian regions. Random forest models showed the best performance, outperforming recurrent neural networks for precipitation forecasting.

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Area of Science:

Environmental Science
Data Science
Climate Science

Background:

Machine learning (ML) is vital for predicting climate patterns and extreme weather events.
Accurate rainfall forecasting supports agriculture, water management, energy, and public safety.
ML models can anticipate climate shifts, enabling proactive planning and disaster mitigation.

Purpose of the Study:

To evaluate three ML models for precipitation prediction in five Brazilian regions.
To compare the performance of Random Forest, Long Short-Term Memory, and Bidirectional Long Short-Term Memory models.
To assess the effectiveness of ML in forecasting rainfall patterns using climate reanalysis data.

Main Methods:

Utilized Random Forest, Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (BiLSTM) models.

Trained models using local temperature and Atlantic Ocean temperature as input features.

Used total precipitation as the target variable for prediction across five Brazilian regions.

Main Results:

All evaluated ML models demonstrated satisfactory performance in precipitation prediction.
The Random Forest model achieved lower average absolute errors compared to LSTM and BiLSTM.
The study confirms the efficacy of ML techniques in forecasting rainfall patterns.

Conclusions:

Machine learning models are effective tools for rainfall forecasting in Brazil.
Random Forest offers a robust and accurate approach for precipitation prediction.
Accurate rainfall forecasts enhance climate change adaptation and resource management strategies.