ICTACT Journal on Soft Computing

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Ensemble Fine Tuned Multi Layer Perceptron for Predictive Analysis of Weather Patterns and Rainfall Forecasting from Satellite Data


Affiliations
1 Department of Computer Engineering, Sinhgad Institute of Technology, India
2 School of Computing, MIT Art, Design and Technology University, India
3 Department of Geography, Bhairab Ganguly College, India
4 Department of Computer Engineering, Ajeenkya D Y Patil School of Engineering, India
5 Department of Electronics and Communication Engineering, Haldia Institute of Technology, India

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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

Keywords

Machine Learning, Weather Forecasting, Rainfall Prediction, MultiLayer Perceptron, Satellite Data
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Abstract Views: 6




  • Ensemble Fine Tuned Multi Layer Perceptron for Predictive Analysis of Weather Patterns and Rainfall Forecasting from Satellite Data

Abstract Views: 6  | 

Authors

Amruta V Surana
Department of Computer Engineering, Sinhgad Institute of Technology, India
Suvarna Eknath Pawar
School of Computing, MIT Art, Design and Technology University, India
Shrinwantu Raha
Department of Geography, Bhairab Ganguly College, India
Nilesh Mali
Department of Computer Engineering, Ajeenkya D Y Patil School of Engineering, India
Tilak Mukherjee
Department of Electronics and Communication Engineering, Haldia Institute of Technology, India

Abstract


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

Keywords


Machine Learning, Weather Forecasting, Rainfall Prediction, MultiLayer Perceptron, Satellite Data