Paper Info
Title
Artificial Neural Network And Machine Learning Based Methods For Population Estimation Of Rohingya Refugees: Comparing Data-Driven And Satellite Image-Driven Approaches
Abstract
Manual field-based population census data collection method is slow and expensive, especially for refugee management situations where more frequent censuses are necessary. This study aims to explore the approaches of population estimation of Rohingya migrants using remote sensing and machine learning. Two different approaches of population estimation viz., (i) data-driven approach and (ii) satellite image-driven approach have been explored. A total of 11 machine learning models including Artificial Neural Network (ANN) are applied for both approaches. It is found that, in situations where the surface population distribution is unknown, a smaller satellite image grid cell length is required. For data-driven approach, ANN model is placed fourth, Linear Regression model performed the worst and Gradient Boosting model performed the best. For satellite image-driven approach, ANN model performed the best while Ada Boost model has the worst performance. Gradient Boosting model can be considered as a suitable model to be applied for both the approaches.
Year
DOI
Venue
2019
10.1142/S2196888819500246
VIETNAM JOURNAL OF COMPUTER SCIENCE
Keywords
Field
DocType
Artificial neural network, machine learning, population estimation, Rohingya, satellite image, remote sensing
Population,Data collection,Data-driven,Refugee,Computer science,Artificial intelligence,Artificial neural network,Census,Machine learning,Satellite image
Journal
Volume
Issue
ISSN
6
4
2196-8888
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Nahian Ahmed100.68
Nazmul Alam Diptu200.68
M. Sakil Khan Shadhin300.34
M. Abrar Fahim Jaki400.34
M. Ferdous Hasan500.34
M. Naimul Islam600.34
Rashedur M. Rahman721.06