scholarly journals Hurricane evacuation demand models with a focus on use for prediction in future events

2016 ◽  
Vol 87 ◽  
pp. 90-101 ◽  
Author(s):  
Kecheng Xu ◽  
Rachel A. Davidson ◽  
Linda K. Nozick ◽  
Tricia Wachtendorf ◽  
Sarah E. DeYoung
Keyword(s):  
Hurricane Evacuation ◽  
Demand Models ◽  
Future Events

Moving toward a brighter future: The effects of desire on judgments about the likelihood of future events.

2016 ◽  
Vol 2 (1) ◽  
pp. 33-48
Author(s):  
Heather C. Lench ◽  
Rachel Smallman ◽  
Logan A. Berg
Keyword(s):  
Future Events ◽  
Brighter Future

Future Events Scale

2006 ◽  
Author(s):  
Aaron L. Wichman ◽  
Darcy A. Reich ◽  
Gifford Weary
Keyword(s):  
Future Events

FORECASTING MIGRATION FLOWS USING PREDICTIVE ANALYTICS

2020 ◽  
pp. 45-54
Author(s):  
Yulia V. Paukova ◽  
◽  
Konstantin V. Popov ◽  
Keyword(s):  
Information Technologies ◽  
Predictive Analytics ◽  
Migration Policy ◽  
Modern World ◽  
Migration Activity ◽  
Wide Range ◽  
Future Events ◽  
The Russian Federation ◽  
Migration Flows ◽  
Develop State

The present article considers the need to predict migration flows using Predictive Analytics. The Russian Federation is a center of migration activity. The modern world is changing rapidly. An effective migration policy requires effective monitoring of migration flows, assessing the current situation in our and other countries and forecasting migration processes. There are information systems in Russia that contain a wide range of information about foreign citizens and stateless persons that provide the requested information about specific foreign citizens, including grouping it on various grounds. However, it is not possible to analyze and predict it automatically using thousands of parameters. Special attention in Russia is paid to digitalization. Using information technologies (artificial intelligence, machine learning and big data analysis) to forecast migration flows in conditions of variability of future events will allow to take into account a number of events and most accurately predict the quantitative and so-called "qualitative" structure of arrivals. The received information will help to develop state policy and to take appropriate measures in the field of migration regulation. The authors come to the conclusion that it is necessary to amend existing legal acts in order to implement information technologies of Predictive Analytics into the practice of migration authorities.

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