Modeling Civilian Causalities in Afghanistan from 2009 and 2017
Aissa Boudjella1, Fazal Mazahari2, Hamidullah Hamidy3
1Aissa Boudjella, Division of Science, Technology and Mathematics, American University of Afghanistan, Kabul, Afghanistan.
2Fazal Mazahari, Department of Mathematics and Science, American University of Afghanistan.
3Hamidullah Hamidy, MBA Student, Department of Business, American University of Afghanistan.
Manuscript received on 24 June 2019 | Revised Manuscript received on 07 July 2019 | Manuscript Published on 17 July 2019 | PP: 97-101 | Volume-8 Issue-2S July 2019 | Retrieval Number: B10150782S19/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this investigation, the Afghan civilian casualties caused by both warring parties have been examined between 2009 and 2017. The casualties include both deaths and injuries. We focus on two different types of incidents, the types of attacks that took lives of non-combatants, and the civilian casualties caused by involved parties. We developed a linear and quadratic regression equation based on the least square method of estimation to analyze the number of casualties from 2009 to 2017. We fit linear and quadratic trends to time series starting from 2009 until 2017 to describe the casualty. The aim is to show how simple linear regression analysis can be used to forecast future death rate. The predicted results from (2017-2020) show that the civilian casualties by both warring parties will continue to increase. However, Afghan civilians will continue suffering more casualties caused by pro and anti-government and complex attacks including suicide attacks. The least estimated regression equation adequately describes relationship between civilian casualties and the time period with a high R-squared. The information, the number of death and injuries, maybe obtained from the extracted parameters such as a slope, y-intercept that are function of the type of causalities. This approach of modelling in a linear regression of civilian casualties simplifies significantly the analysis to help policy makers in comprehension of change in Afghanistan causality. The results can help develop appropriate strategies and assess the war and civilian casualties in managing operations and educating people for the future sustainability ethical piratical.
Keywords: Civilian Casualties; Anti-Government Elements Groups (Ages); Attacks; Warring Parties.
Scope of the Article: Social Sciences