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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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| Paper Title: | Machine learning approaches for accurate rainfall prediction and preparedness |
| Authors Name: | Mr. Aala Ravikiran , Muthyala.Sumasri , Shaik.umera , Chitikela.Manikanta |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_200638
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| Published Paper Id: | IJRTI2502018 |
| Published In: | Volume 10 Issue 2, February-2025 |
| DOI: | |
| Abstract: | Rainfall prediction is critical for raising awareness about the potential risks associated with extreme weather events and enabling individuals to take proactive safety measures. This study leverages machine learning algorithms to forecast rainfall, recognizing the significant impact that both insufficient and excessive rainfall can have on rural and urban areas. Due to the complex nature of rainfall, which is influenced by atmospheric, oceanic, and geographical factors, predicting rainfall remains a challenging task. To address this, the research employs a variety of data preprocessing techniques, including outlier analysis, correlation analysis, and feature selection. Several machine learning models are applied, including Naive Bayes (NB), Decision Tree, Support Vector Machine (SVM), Random Forest, and Logistic Regression, to develop a reliable rainfall prediction model. The study places particular emphasis on selecting the most relevant features to improve the model's accuracy. Additionally, Artificial Neural Networks (ANN) are used, with feature selection enhancing their predictive performance. To explore regional rainfall patterns, k-means clustering and Principal Component Analysis (PCA) are applied, focusing on rainfall behaviour in Australia. As part of the research, a userfriendly web application is developed using Flask, making the machine learning model more accessible to the general public. This web-based system allows users to easily interact with the rainfall prediction model and gain valuable insights into weather patterns. Overall, the study demonstrates the effectiveness of various machine learning techniques in predicting rainfall, providing valuable insights for improving preparedness and decision-making based on Australian weather data. |
| Keywords: | Rainfall Prediction, Machine Learning, Weather Forecasting, Data Preprocessing, Feature Selection, Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest, Decision Tree, Naive Bayes (NB), Logistic Regression, Outlier Analysis, Principal Component Analysis (PCA), K-means Clustering |
| Cite Article: | "Machine learning approaches for accurate rainfall prediction and preparedness ", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 2, page no.a134-a140, February-2025, Available :http://www.ijrti.org/papers/IJRTI2502018.pdf |
| Downloads: | 00086 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2502018
Registration ID:200638
Published In: Volume 10 Issue 2, February-2025
DOI (Digital Object Identifier):
Page No: a134-a140 Country: -, -, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2502018 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2502018 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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