Abstract
India is a country with leading economy driven by agriculture, which is majorly depended on the climatic changes. Rainfall events in India have diversified irregular pattern across the country, which makes it more complex and challenging to mine. Rainfall data comes under the meteorological data which is spatiotemporal in nature. The meteorological data is increasing day by day, to handle that huge amount of data imposing a wide range of challenges for storage and analysis. Big data technologies, like Hadoop Distributed File System (HDFS), Hbase, and many query processing tools for processing data like Hive and Pig are popularly known to handle such type of data. But these tools and techniques individually are inadequate for mining data efficiently. We have reviewed the research in the area of mining rainfall data and identified the gaps in the existing approaches.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
https://www.toppr.com/guides/geography/climate/climate-of-india/
https://www.omnisci.com/learn/resources/technical-glossary/spatial-temporal
Nair A, Ajith K, Nair KS (2014) Spatio-temporal analysis of rainfall trends over a maritime state (Kerala) of India during the last 100 years. Atmos Environ 88:123–132
Rao P, Sachdev R, Pradhan TA (2016) Hybrid approach to rainfall classification and prediction for crop sustainability. In: Thampi S, Bandyopadhyay S, Krishnan S, Li KC, Mosin S, Ma M (eds) Advances in Signal processing and intelligent recognition systems. Advances in intelligent systems and computing, vol 425. Springer, Cham
Nikam V, Meshram BB (2013) Modeling rainfall prediction using data mining method: a bayesian approach. In: Fifth international conference on computational intelligence, modelling and simulation, pp 132–136
Suryanarayana V, Sathish B, Ranganayakulu A, Ganesan P (2019) Novel weather data analysis using Hadoop and MapReduce. In: 5th International conference on advanced computing and communication systems (ICACCS), Coimbatore, India
Zainudin S, Jasim D, Abu BA (2016) Comparative analysis of data mining techniques for malaysian rainfall prediction
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Vyas, K. (2021). Big Data Mining on Rainfall Data. In: Kotecha, K., Piuri, V., Shah, H., Patel, R. (eds) Data Science and Intelligent Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 52. Springer, Singapore. https://doi.org/10.1007/978-981-15-4474-3_10
Download citation
DOI: https://doi.org/10.1007/978-981-15-4474-3_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-4473-6
Online ISBN: 978-981-15-4474-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)