ENHANCING THE ACCURACY OF MUMBAI RAINFALL PREDICTION PROCESS THROUGH MINING TECHNIQUES

Registration ID: IJNRD_204468

Published ID: IJNRD2309061

DOI: Click Here to Get

Ms. B. MADHUVANTHI , Dr. T.S. BASKARAN

Rainfall Prediction, Data Mining, Naive Bayesian, Support Vector Machine, Logistic Regression, J48, Random Forest.

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.

Published Paper   E-Certificate

Ms. B. MADHUVANTHI & Dr. T.S. BASKARAN (September-2023). ENHANCING THE ACCURACY OF MUMBAI RAINFALL PREDICTION PROCESS THROUGH MINING TECHNIQUES. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(9), a493-a508. https://ijnrd.org/papers/IJNRD2309061.pdf

Volume 8 Issue 9, September-2023

Pages : a493-a508

Paper Reg. ID: IJNRD_204468

Published Paper Id: IJNRD2309061

Downloads: 000121980

Research Area: Computer Science & Technology 

Country: Thanjavur, Tamilnadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2309061.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2309061

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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