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首页> 外文会议>International Conference on Applied Informatics and Computing Theory >Community Learning Based Response Process Optimization: Flood-Threatened Communities of Lover Sava Valley in Slovenia
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Community Learning Based Response Process Optimization: Flood-Threatened Communities of Lover Sava Valley in Slovenia

机译：基于社区学习的响应过程优化：斯洛文尼亚情人群的洪水威胁社区

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Between 2009 and 2015, the communities located in the Lower Sava valley experienced five flood events. The flood events occurred due to continuous rain in central and north-eastern parts of Slovenia. The least threatening of the flood events included increased water level of the Sava and the Krka rivers, which isolated only few houses from the rest of the community. The most devastating event caused several roadblocks, flooding the entire areas of the communities located close to the two rivers. The data on the rivers' flow rates and levels during the flood events was obtained from the Slovenian Environment Agency database, and data on the severity of the flood events from the Administration for Civil Protection and Disaster Relief database. We merged both types of data in a single database and created a timeline of the events with river dynamics for every event. Based on past events, the communities have learned how to react and protect any endangered property. The communities near the Sava and the Krka in the Lover Sava valley date back to the times before the Franciscan cadastre. Floods occurred several times in the past, but the respective communities learned their first significant lesson only in 2010 when they were affected by a flood of historic proportions. Several types of tacit knowledge emerged during that event and the events that followed almost every year since. We identified a new knowledge base concerning when, to what extent, and how to organize the protection of threatened households. To be able to create a community-learning model, we conducted semi-structured interviews with people from the households threatened by the flood events after 2009. The learning model, supported by a timeline of the events, revealed which event affected the learning process and how. Based on the emerged knowledge, communities not only changed their own behaviour but also influenced the response process of public services. The influences manifested themselves as two type of information delivered to public services. The first type provided public services with new insight into endangered areas that would otherwise remain undisclosed, along with the need for distress assistance. The second type of information provided public services with an updated overview of the local water level situation, not covered by the official reports. Based on the community's informal information source, public services were able to adjust their on-field response process in order to support the endangered communities. The data on information exchange was taken from the database of the national Administration for Civil Protection and Disaster Relief database, and local Civil Protection Command logbook. We created learning model, where we merged the data on response process modification with the timeline of the events and the community learning process. We used different statistical methods to discover which community performed best as a learner and influenced public services the most. Further, we designed response process optimization algorithm, based on prevention and educational measures. It successfully raised self-reliant flood protection of the endangered communities and at the same time reduced workload of the flood response entities. Consequently we were able to detect similar dependencies between different process dimension pairs, which opened possible new research issue.
机译：2009年至2015年间，位于萨瓦省较低的山谷的社区经历过五次洪水事件。由于斯洛文尼亚中部和东北部门的连续下雨，洪水事件发生。洪水事件的最少威胁包括萨瓦和克尔卡河的水平增加，其中距离社区其他社区中只有很少的房屋。最毁容的事件导致了几个障碍，淹没了靠近两条河流的社区的整个领域。洪水事件中的河流流速和水平的数据是从斯洛文尼亚环境署数据库获得的，以及关于民事保护和救灾数据库管理局的洪水事件严重程度的数据。我们在单个数据库中合并了两种类型的数据，并为每个事件创建了具有河流动力学的事件的时间表。根据过去的事件，社区已经了解了如何应对和保护任何濒危财产。在萨达瓦萨尔利的萨瓦和克尔卡附近的社区返回了在Franciscan Cadastre之前的时间。过去发生了多次洪水，但各自的社区仅在2010年时学会了他们的第一个重要课程，当他们受到洪水灾难的影响时，他们只会受到影响。在该事件期间出现了几种类型的默契知识，并从那时起就随后随之而来的事件。我们确定了一个关于何时，在多大程度上的新知识库，以及如何组织受威胁家庭的保护。为了能够创建一个社区学习模式，我们在2009年之后向来自洪水事件威胁的家庭的人们进行了半结构化访谈。由事件时间表提供的学习模式，揭示了哪些事件影响了学习过程和如何。根据出现的知识，社区不仅改变了自己的行为，还影响了公共服务的响应过程。影响表现为自向公共服务的两种信息。第一种类型为公共服务提供了新的服务，进入濒危区域，否则仍未披露，以及需要遇险援助。第二种信息提供了公共服务，并在官方报告中未涵盖当地水位情况的更新概览。基于社区的非正式信息来源，公共服务能够调整其现场响应过程，以支持濒危社区。信息交流数据是从国家民事保护和救灾数据库管理数据库以及当地民事保护指挥日志。我们创建了学习模型，我们将数据与事件和社区学习过程的时间表合并了关于响应过程修改的数据。我们使用不同的统计方法来发现哪个社区作为学习者最良好，最多影响公共服务。此外，我们设计了基于预防和教育措施的响应过程优化算法。它成功地提高了濒危社区的自立防洪，同时减少了洪水响应实体的工作量。因此，我们能够检测不同过程维度对之间的相似依赖性，该对在开辟了可能的新研究问题。

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