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Further distribution of all or part of this report is subject to the Distribution Statement appearing on the front cover. / One of the global security challenges the United States faces is disaster coupled with political instability. The U.S. Military‘s ability to rapidly respond to disasters enhances regional and global security and stability. Foreign Humanitarian Assistance and Disaster Relief (FHA/DR), increasingly a mission that relies on a significant military component, focuses on the provision of goods and services such as health care, supplies necessary for survival, and infrastructure repair, with the goal of reducing the immediate human suffering. The disaster in this project‘s scenario is catastrophic flooding that occurs in one of Africa‘s most populated and wealthiest countries that threatens the stability and development of West Africa. This project, employing a systems engineering methodology, focuses on the 60 days after the disaster and the requirements to provide this assistance in the form of goods and services. Many system-of-systems architectures were developed to investigate the effectiveness of utilizing a Seabase for the primary delivery of aid. Two simulation tools, SimKit, and STELLA, were used to model and examine these architectures with the former addressing the delivery and throughput concerns while the latter focused on the satisfaction of the population and the limitation of criminal activity. Based on the results of modeling, the team provided recommendations relative to the most effective architectures in influencing the population of this coastal area as well as accomplishing the FHA/DR mission.

The military recently recognized the importance of taking sociocultural factors into consideration. Therefore, Human Social Culture Behavior (HSCB) modeling has been getting much attention in current and future operational requirements to successfully understand the effects of social and cultural factors on human behavior. There are different kinds of modeling approaches to the data that are being used in this field and so far none of them has been widely accepted. HSCB modeling needs the capability to represent complex, ill-defined, and imprecise concepts, and soft computing modeling can deal with these concepts. There is currently no study on the use of any computational methodology for representing the relationship between adverse events and infrastructure development investments in an active war theater. This study investigates the relationship between adverse events and infrastructure development projects in an active war theater using soft computing techniques including fuzzy inference systems (FIS), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFIS) that directly benefits from their accuracy in prediction applications. Fourteen developmental and economic improvement project types were selected based on allocated budget values and a number of projects at different time periods, urban and rural population density, and total adverse event numbers at previous month selected as independent variables. A total of four outputs reflecting the adverse events in terms of the number of people killed, wounded, hijacked, and total number of adverse events has been estimated. For each model, the data was grouped for training and testing as follows: years between 2004 and 2009 (for training purpose) and year 2010 (for testing). Ninety-six different models were developed and investigated for Afghanistan iv and the country was divided into seven regions for analysis purposes. Performance of each model was investigated and compared to all other models with the calculated mean absolute error (MAE) values and the prediction accuracy within ±1 error range (difference between actual and predicted value). Furthermore, sensitivity analysis was performed to determine the effects of input values on dependent variables and to rank the top ten input parameters in order of importance. According to the the results obtained, it was concluded that the ANNs, FIS, and ANFIS are useful modeling techniques for predicting the number of adverse events based on historical development or economic projects’ data. When the model accuracy was calculated based on the MAE for each of the models, the ANN had better predictive accuracy than FIS and ANFIS models in general as demonstrated by experimental results. The percentages of prediction accuracy with values found within ±1 error range around 90%. The sensitivity analysis results show that the importance of economic development projects varies based on the regions, population density, and occurrence of adverse events in Afghanistan. For the purpose of allocating resources and development of regions, the results can be summarized by examining the relationship between adverse events and infrastructure development in an active war theater; emphasis was on predicting the occurrence of events and assessing the potential impact of regional infrastructure development efforts on reducing number of such events.