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Indian summer monsoon is a complex and non-linear climatic phenomenon. India being an agricultural country, the monsoon governs the pulse of life for its mankind along with the flora-fauna existing in the subcontinent. It has large impact on agriculture, renewal of fresh-water, and generation of hydro-electricity power. Monsoon is the driving force for development of economy of the country. An attempt is made to study, analyse, and predict Indian summer monsoon. Different machine learning algorithms are used for these purposes. Focus is towards identification of new predictors from different climatic variables influencing the monsoon of the sub-continent and improvement of monsoon prediction models to forecast monsoon with greater accuracy. Various clustering, community detection, and deep learning approaches are utilised to attain the goal.
About the author
Moumita Saha has completed her PhD degree from Computer Science & Engineering at Indian Institute of Technology Kharagpur in 2016. She has received her M.Tech in Computer Science & Technology from BESU Shibpur in 2012 and B.Tech from WBUT in 2010. Her specialisations include pattern recognition, machine learning, data mining, and climate science.
