TY - GEN
T1 - A hybrid approach to rainfall classification and prediction for crop sustainability
AU - Rao, Prajwal
AU - Sachdev, Ritvik
AU - Pradhan, Tribikram
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84954515851&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84954515851&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-28658-7_39
DO - 10.1007/978-3-319-28658-7_39
M3 - Conference contribution
AN - SCOPUS:84954515851
SN - 9783319286563
VL - 425
T3 - Advances in Intelligent Systems and Computing
SP - 457
EP - 471
BT - Advances in Signal Processing and Intelligent Recognition Systems - Proceedings of 2nd International Symposium on Signal Processing and Intelligent Recognition Systems, SIRS 2015
PB - Springer Verlag
T2 - 2nd International Symposium on Signal Processing and Intelligent Recognition Systems, SIRS 2015
Y2 - 16 December 2015 through 19 December 2015
ER -