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  Published Paper Details:

  Paper Title

Accurate Rainfall Prediction and Preparedness by using Machine Learning Approaches

  Authors

  Mr. S S Dinesh Naik,  Smt M. Prashanthi

  Keywords

Naive Bayes, Decision Trees, SVM, Logistic Regression, ANN, and LSTM enhance rainfall prediction through machine learning.

  Abstract


Accurate rainfall prediction is crucial for effective disaster preparedness and resource management. This study explores the application of machine learning techniques to predict rainfall using historical meteorological data. We employ various algorithms, including Naive Bayes, Support Vector Machines (SVM), Decision Trees, Logistic Regression, Long Short-Term Memory (LSTM), Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN). The dataset, sourced from Kaggle, includes features such as temperature, humidity, wind speed, and atmospheric pressure. Preprocessing steps, including handling missing data, feature selection, and data normalization, are applied to enhance model performance. The results demonstrate the effectiveness of machine learning in improving rainfall prediction accuracy, with implications for better disaster management and agricultural planning.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT25A1160

  Paper ID - 297573

  Page Number(s) - i875-i880

  Pubished in - Volume 13 | Issue 11 | November 2025

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Mr. S S Dinesh Naik,  Smt M. Prashanthi,   "Accurate Rainfall Prediction and Preparedness by using Machine Learning Approaches", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 11, pp.i875-i880, November 2025, Available at :http://www.ijcrt.org/papers/IJCRT25A1160.pdf

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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
ISSN
ISSN and 7.97 Impact Factor Details


ISSN
ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
ISSN
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