Development of standard loss analysis model using big data

Seungho Lee, Chang-Mok Lim, Chahwa Lee, Y. Cho, Jaeoh Kim
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Abstract

The Republic of Korea Army conducts simulations during peacetime using ground operation resource requirements analysis model (GORRAM) to determine potential losses when at war based on the latest operation plan. Although war-game simulation can yield reliable results, it takes considerable amount of time and effort to build a database and generate scenarios. Therefore, a study is required to supplement the detailed war-game simulation method to quickly determine expected losses. Using data built-in GORRAM, we tested the significance of four factors using beta regression analysis. While multiple regression is most commonly used to model the causality, beta regression is a powerful method for modeling response variables in the (0,1) range, such as the loss ratio. We verified that three factors, namely ‘topography’, ‘operational posture’, and ‘friend/foe power ratio’ were related to loss. This study proposes a new method for calculating the expected loss in real-time, overcoming a limitation of existing war-game simulation methods.
利用大数据开发标准损失分析模型
韩国陆军在和平时期使用地面作战资源需求分析模型(GORRAM)进行模拟,以根据最新的作战计划确定战争时的潜在损失。虽然战争模拟可以产生可靠的结果,但建立数据库和生成场景需要花费相当多的时间和精力。因此,需要研究补充详细的兵棋模拟方法,以快速确定预期损失。使用内置GORRAM的数据,我们使用beta回归分析检验了四个因素的显著性。虽然多元回归最常用于建立因果关系模型,但β回归是一种强大的方法,可以对(0,1)范围内的响应变量(如损失比)进行建模。我们证实了三个因素,即“地形”、“作战姿势”和“敌我功率比”与损失有关。本文提出了一种实时计算期望损失的新方法,克服了现有战争模拟方法的局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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