Ooms, D. (2023). Civil-Military Interaction: a Case Study to validate a Conceptual Framework. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 501–515). Omaha, USA: University of Nebraska at Omaha.
Abstract: International peace operations in response to complex emergencies require effective interaction between international civil and military participants and local actors. Although these operations frequently occur worldwide, civil-military interaction (CMI) remains problematic. CMI problems are described in the literature at length. However, the knowledge management aspects of these problems have received less attention. The feasibility of technical support solutions for CMI should be investigated using a design science approach. This requires validated models of the structural and behavioral characteristics of the CMI domain. A CMI conceptual framework providing such models has been proposed earlier and should be validated. A case study has been conducted into a Netherlands military CMI organization. This study provides for initial user validation of the models. In follow-on research, the validated conceptual framework is used to structure the investigation of CMI problems, knowledge process deficiencies, and their causal relations. It may subsequently support knowledge engineering-based solution design.
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Herrera, L. C., & Gjøsæter, T. (2023). Leveraging Crisis Informatics Experts: A co-creating approach for validation of social media research insights. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 439–448). Omaha, USA: University of Nebraska at Omaha.
Abstract: Validation of findings is a challenge in practice-based research. While analysis is being conducted and findings are being constructed out of data collected in a defined period, practitioners continue with their activities. This issue is exacerbated in the field of crisis management, where high volatility and personnel turnover make the capacity to attend research demands scarce. Therefore, conducting classic member validation is logistically challenging for the researcher. The need for rigor and validity calls for alternative mechanisms to fulfill requirements for academic research. This article presents an approach for validation of results of a qualitative study with public organizations that use social media as a source of information in the context of crisis management. The unavailability of original interview-objects to validate our findings resulted in an alternative validation method that leveraged experts in crisis informatics. By presenting our approach, we contribute to encouraging rigor in qualitative research while maintaining the relationship between practice and academia.
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Per-Anders Oskarsson, Niklas Hallberg, Johan Nordström, Magdalena Granåsen, & Mari Olsén. (2022). Assessment of Collaborative Crisis Management Capability by Generic Questions. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 385–391). Tarbes, France.
Abstract: Societies need the ability to respond to crises such as terrorism, pandemics and natural disasters. Hence, it is essential to ensure that the capability of crisis management is attained, maintained, and developed. Since large crises cannot be handled by single organizations, collaborative crisis management capability is needed. The objective of this work was to provide support by an instrument for assessment of collaborative crisis management capability. The work was iteratively performed in a workgroup. The outcome was two templates with sets of generic questions, one for assessment of the actual capabilities and one for assessment of the preconditions of the capabilities. The templates mainly focus on assessment of collaborative crisis management capability. However, since the questions are generically formulated, they should be usable for assessments of any type of crisis management capability.
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Shivam Sharma, & Cody Buntain. (2021). An Evaluation of Twitter Datasets from Non-Pandemic Crises Applied to Regional COVID-19 Contexts. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 808–815). Blacksburg, VA (USA): Virginia Tech.
Abstract: In 2020, we have witnessed an unprecedented crisis event, the COVID-19 pandemic. Various questions arise regarding the nature of this crisis data and the impacts it would have on the existing tools. In this paper, we aim to study whether we can include pandemic-type crisis events with general non-pandemic events and hypothesize that including labeled crisis data from a variety of non-pandemic events will improve classification performance over models trained solely on pandemic events. To test our hypothesis we study the model performance for different models by performing a cross validation test on pandemic only held-out sets for two different types of training sets, one containing only pandemic data and the other a combination of pandemic and non-pandemic crisis data, and comparing the results of the two. Our results approve our hypothesis and give evidence of some crucial information propagation upon inclusion of non-pandemic crisis data to pandemic data.
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Mirko Zaffaroni, & Claudio Rossi. (2020). Water Segmentation with Deep Learning Models for Flood Detection and Monitoring. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 66–74). Blacksburg, VA (USA): Virginia Tech.
Abstract: Flooding is a natural hazard that causes a lot of deaths every year and the number of flood events is increasing worldwide because of climate change effects. Detecting and monitoring floods is of paramount importance in order to reduce their impacts both in terms of affected people and economic losses. Automated image analysis techniques capable to extract the amount of water from a picture can be used to create novel services aimed to detect floods from fixed surveillance cameras, drones, crowdsourced in-field observations, as well as to extract meaningful data from social media streams. In this work we compare the accuracy and the prediction performances of recent Deep Learning algorithms for the pixel-wise water segmentation task. Moreover, we release a new dataset that enhances well-know benchmark datasets used for multi-class segmentation with specific flood-related images taken from drones, in-field observations and social media.
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