The accurate prediction of weather patterns and rainfall forecasting is
critical for various sectors, including agriculture, disaster
management, and water resource planning. Traditional models often
struggle to capture the complex interactions between atmospheric
variables, particularly when integrating diverse types of satellite data
(binary, categorical, and numerical). To address this challenge, an
ensemble fine-tuned multi-layer perceptron (MLP) model is developed,
combining the strengths of multiple machine learning techniques for
more robust predictions. The primary problem is the difficulty in
handling mixed data types while maintaining high prediction accuracy.
Satellite data, including binary indicators (e.g., cloud presence),
categorical features (e.g., cloud types), and numerical variables (e.g.,
temperature, humidity, and wind speed), provide rich information but
require specialized processing for effective forecasting. The proposed
method involves fine-tuning an ensemble of MLP models with
backpropagation, dropout regularization, and batch normalization to
reduce overfitting and enhance generalization. The ensemble
integrates predictions from individual MLP models, each trained on
different subsets of features (binary, categorical, numerical). This
technique allows the model to leverage complementary strengths and
produce more accurate rainfall forecasts. Satellite data is preprocessed
and normalized before training, and categorical variables are one-hot
encoded to ensure compatibility with the MLP architecture. Results
from testing on historical satellite weather datasets demonstrate
significant improvements in forecast accuracy. The ensemble MLP
achieved an accuracy of 91.3%, with a precision of 90.7%, recall of
89.5%, and an F1-score of 90.1%. The model performed exceptionally
well in identifying critical rainfall events, reducing false positives by
12% compared to traditional models.