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Drought Prediction and Management using Big Data Analytics

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 162 - Number 4
Year of Publication: 2017
Authors:
Himani Shah, Vinita Rane, Jayesh Nainani, Benita Jeyakumar, Nupur Giri
10.5120/ijca2017913276

Himani Shah, Vinita Rane, Jayesh Nainani, Benita Jeyakumar and Nupur Giri. Drought Prediction and Management using Big Data Analytics. International Journal of Computer Applications 162(4):27-30, March 2017. BibTeX

@article{10.5120/ijca2017913276,
	author = {Himani Shah and Vinita Rane and Jayesh Nainani and Benita Jeyakumar and Nupur Giri},
	title = {Drought Prediction and Management using Big Data Analytics},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2017},
	volume = {162},
	number = {4},
	month = {Mar},
	year = {2017},
	issn = {0975-8887},
	pages = {27-30},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume162/number4/27233-2017913276},
	doi = {10.5120/ijca2017913276},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

The prediction of occurrence of droughts has been a challenging task. However, it is necessary that prediction is done with at most accuracy to prevent loss of life and property. Based on the previous year’s rainfall, temperature and evapotranspiration data, DDI will be calculated which will be based on SPI, SPEI, PDSI, PHDI and ZIND indices. This proposed index will be trained using random forest algorithm and the output will help to predict the severity of drought for the upcoming years. Also, round robin algorithm with dynamic quantum size is used for resource allocation for the victims of drought affected areas.

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Keywords

Drought prediction, rainfall, drought indices, prediction models, resource allocation