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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | ENHANCING THE ACCURACY OF MUMBAI RAINFALL PREDICTION PROCESS THROUGH MINING TECHNIQUES |
| Authors Name: | Ms. B. MADHUVANTHI , Dr. T.S. BASKARAN |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_204468
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| Published Paper Id: | IJNRD2309061 |
| Published In: | Volume 8 Issue 9, September-2023 |
| DOI: | |
| Abstract: | The changing of physical characteristics of the hydrological system has cause natural phenomenon which leads to heavy rainfall. It is one of the problems of economic damages and affects people. Rainfall is one of the greatest challenges in the meteorological department. Rainfall prediction is necessary to inform people and prepare them in advance about the upcoming weather condition. Rainfall prediction involves recording the various parameters of weather like temperature, dew point, wind speed, visibility, and precipitation. It has been seen that data mining techniques have achieved good performance and accuracy in rainfall prediction. This research work aims to compare the performance of a few data mining algorithms for predicting rainfall using historical weather data of Mumbai, India, which is collected from the World Metrological Organization (WMO). From the collected weather data which comprises 11 attributes, only 5 attributes that are most relevant to rainfall prediction are considered. The data mining process model is followed to obtain accurate and correct prediction results. In this thesis, various data mining algorithms were explored which include decision tree-based J48, Random Forest, Logistic Regression, Naive Bayes and Support Vector Machine (SVM). The experimental results show that the Random Forest algorithm has a good level of accuracy than other algorithms. |
| Keywords: | Rainfall Prediction, Data Mining, Naive Bayesian, Support Vector Machine, Logistic Regression, J48, Random Forest. |
| Cite Article: | "ENHANCING THE ACCURACY OF MUMBAI RAINFALL PREDICTION PROCESS THROUGH MINING TECHNIQUES", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 9, page no.a493-a508, September-2023, Available :http://www.ijnrd.org/papers/IJNRD2309061.pdf |
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| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 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:IJNRD2309061 Registration ID: 204468 Published In: Volume 8 Issue 9, September-2023 DOI (Digital Object Identifier): Page No: a493-a508 Country: Thanjavur, Tamilnadu, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2309061 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2309061 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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