A hybrid approach to rainfall classification and prediction for crop sustainability

Prajwal Rao, Ritvik Sachdev, Tribikram Pradhan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Indian Agriculture is primarily dependent on rainfall distribution throughout the year. There have been several instances where crops have failed due to inadequate rainfall. This study aims at predicting rainfall considering those factors which have been correlated against precipitation, across various crop growing regions in India by using regression analysis on historical rainfall data. Additionally, we’ve used season-wise rainfall data to classify different states into crop suitability for growing major crops. We’ve divided the four seasons of rainfall as winter, pre-monsoon, monsoon, and post-monsoon. Finally, a bipartite cover is used to determine the optimal set of states that are required to produce all the major crops in India, by selecting a specific set of crops to be grown in every state, and selecting the least number of states to achieve this. The data used in this paper is taken from the Indian Meteorological Department (IMD) and Open Government Data (OGD) Platform India published by the Government of India.

Original languageEnglish
Title of host publicationAdvances in Signal Processing and Intelligent Recognition Systems - Proceedings of 2nd International Symposium on Signal Processing and Intelligent Recognition Systems, SIRS 2015
PublisherSpringer Verlag
Pages457-471
Number of pages15
Volume425
ISBN (Print)9783319286563
DOIs
Publication statusPublished - 2016
Event2nd International Symposium on Signal Processing and Intelligent Recognition Systems, SIRS 2015 - Trivandrum, India
Duration: 16-12-201519-12-2015

Publication series

NameAdvances in Intelligent Systems and Computing
Volume425
ISSN (Print)2194-5357

Conference

Conference2nd International Symposium on Signal Processing and Intelligent Recognition Systems, SIRS 2015
CountryIndia
CityTrivandrum
Period16-12-1519-12-15

Fingerprint

Crops
Rain
Sustainable development
Regression analysis
Agriculture

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Rao, P., Sachdev, R., & Pradhan, T. (2016). A hybrid approach to rainfall classification and prediction for crop sustainability. In Advances in Signal Processing and Intelligent Recognition Systems - Proceedings of 2nd International Symposium on Signal Processing and Intelligent Recognition Systems, SIRS 2015 (Vol. 425, pp. 457-471). (Advances in Intelligent Systems and Computing; Vol. 425). Springer Verlag. https://doi.org/10.1007/978-3-319-28658-7_39
Rao, Prajwal ; Sachdev, Ritvik ; Pradhan, Tribikram. / A hybrid approach to rainfall classification and prediction for crop sustainability. Advances in Signal Processing and Intelligent Recognition Systems - Proceedings of 2nd International Symposium on Signal Processing and Intelligent Recognition Systems, SIRS 2015. Vol. 425 Springer Verlag, 2016. pp. 457-471 (Advances in Intelligent Systems and Computing).
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abstract = "Indian Agriculture is primarily dependent on rainfall distribution throughout the year. There have been several instances where crops have failed due to inadequate rainfall. This study aims at predicting rainfall considering those factors which have been correlated against precipitation, across various crop growing regions in India by using regression analysis on historical rainfall data. Additionally, we’ve used season-wise rainfall data to classify different states into crop suitability for growing major crops. We’ve divided the four seasons of rainfall as winter, pre-monsoon, monsoon, and post-monsoon. Finally, a bipartite cover is used to determine the optimal set of states that are required to produce all the major crops in India, by selecting a specific set of crops to be grown in every state, and selecting the least number of states to achieve this. The data used in this paper is taken from the Indian Meteorological Department (IMD) and Open Government Data (OGD) Platform India published by the Government of India.",
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Rao, P, Sachdev, R & Pradhan, T 2016, A hybrid approach to rainfall classification and prediction for crop sustainability. in Advances in Signal Processing and Intelligent Recognition Systems - Proceedings of 2nd International Symposium on Signal Processing and Intelligent Recognition Systems, SIRS 2015. vol. 425, Advances in Intelligent Systems and Computing, vol. 425, Springer Verlag, pp. 457-471, 2nd International Symposium on Signal Processing and Intelligent Recognition Systems, SIRS 2015, Trivandrum, India, 16-12-15. https://doi.org/10.1007/978-3-319-28658-7_39

A hybrid approach to rainfall classification and prediction for crop sustainability. / Rao, Prajwal; Sachdev, Ritvik; Pradhan, Tribikram.

Advances in Signal Processing and Intelligent Recognition Systems - Proceedings of 2nd International Symposium on Signal Processing and Intelligent Recognition Systems, SIRS 2015. Vol. 425 Springer Verlag, 2016. p. 457-471 (Advances in Intelligent Systems and Computing; Vol. 425).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Rao P, Sachdev R, Pradhan T. A hybrid approach to rainfall classification and prediction for crop sustainability. In Advances in Signal Processing and Intelligent Recognition Systems - Proceedings of 2nd International Symposium on Signal Processing and Intelligent Recognition Systems, SIRS 2015. Vol. 425. Springer Verlag. 2016. p. 457-471. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-319-28658-7_39