Study of Poverty Prediction using Remote Sensing Data
Rekha S. Thorat
, Dr. Praveen Shetiye , Dr. Avinash K. Gulve
Remote sensing data, Machine Learning, DHS data.
In this paper, a study on poverty prediction using remote sensing data is done. Remote sensing method of prediction poverty is an efficient method in terms of time consumption, cost, and effort required than the household survey method. For this approach various machine learning methods like classification, regression, clustering and dimension reduction are used to train the model. In the training phase, household survey data is used. This GDP, school enrollment, CO2 emissions, poverty headcount ratio, life expectancy at birth, GNI per capita and census data is made freely available for research purpose by World Bank Group in the form of statistics, and DHS (demographic health survey) data is available on DHS program’s site. Satellite daytime and nighttime data can be taken from public and private domains of the satellite. After data collection and execution of model with machine learning methods, various results are computed with different and maximum accuracy.
"Study of Poverty Prediction using Remote Sensing Data", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.4, Issue 7, page no.338 - 341, July-2019, Available :https://ijsdr.org/papers/IJSDR1907057.pdf
Volume 4
Issue 7,
July-2019
Pages : 338 - 341
Paper Reg. ID: IJSDR_190818
Published Paper Id: IJSDR1907057
Downloads: 000347213
Research Area: Engineering
Country: Aurangabad, Maharashtra, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
