Performance Analysis of Classifiers in Identification of Dry and Wet Spells During the Monsoon Period
Harikumar Rajaguru1, Monish Ramesh2, Manoranjith.K3
1Harikumar Rajaguru, Department of ECE, Bannari Amman Institute of Technology Sathyamangalam.
2Monish Ramesh, Department of ECE, Bannari Amman Institute of Technology Sathyamangalam.
3Manoranjith.K, Department of ECE, Bannari Amman Institute of Technology Sathyamangalam.
Manuscript received on 09 March 2019 | Revised Manuscript received on 18 March 2019 | Manuscript published on 30 July 2019 | PP: 3342-3346 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2430078219/19©BEIESP | DOI: 10.35940/ijrte.B2430.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper aims to identify the rainy and non rainy period of the Indian Peninsular monsoon. The IMD observations were taken over a period of one hundred days from 23rd October 2018 to 30th January 2019 with ninety five wet days and five dry dates. Ten observational features like Max, Min and Average temperatures, Rain fall wind Speed, atmospheric Pressure, Illumination, Visibility, relative cloud density and relative humidity are acquired from the IMD data for peninsular India. These features are further reduced by four statistical parameters such as, mean, variance, skewness and kurtosis. Histogram plots show that the measured features and their statistical parameters follows a non linear pattern. Therefore, a group of five classifiers namely, non linear regression, linear regression, Expectation Maximization, logistic regression and Bayesian linear Discriminant are used to analyze the classification efficiency. All the classifiers attains more the 85% of Classification accuracy (average) in the both dry and wet spell period of monsoon observation
Index Terms: Monsoon, Dry and Wet Spell, Nonlinear Regression, Linear Regression, Expectation Maximization, Logistic Regression, Bayesian Linear Discriminant, Accuracy.
Scope of the Article: High Performance Computing