000			a
999			_c22712 _d22712
003			OSt
005			20250424142651.0
008			250424b xxu||||| |||| 00| 0 eng d
040			_aAIKTC-KRRC _cAIKTC-KRRC
100			_925981 _aSurana, Amruta V.
245			_aEnsemble fine tuned multi layer perceptron for predictive analysis of weather patterns and rainfall forecasting from satellite data
250			_aVol.15(2), Oct
260			_aChennai _bICT Academy _c2024
300			_a3491-3496p.
520			_aThe 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.
650		0	_94622 _aComputer Engineering
700			_925982 _aPawar, Suvarna Eknath
773	0		_tICTACT Journal on Soft Computing (IJSC) _dChennai ICT Academy
856			_uhttps://ictactjournals.in/paper/IJSC_Vol_15_Iss_2_Paper_3_3491_3496.pdf _yClick here
942			_2ddc _cAR