疏濬工程為台灣防洪治水之重要水利工程，於執行時常遇自然、人為風險事件，同時相關單位常因資深疏濬人員退休、離職造成知識斷層，加上各河川局所遇風險屬性不同且缺乏經歷分享平台，使得各河局風險實務辦理方法無法相互交流。因此先前研究建立疏濬風險知識庫，即時提供風險預防綜合評估建議及風險事件實務辦理方法，協助全台疏濬人員遇風險進行疏濬決策；然舊有之系統具有實務辦理方法不完全、僅能以專家系統查詢、僅能於電腦前使用，且對於未知風險事件無法給予回應等缺點。爰此，本研究採訪十處河川局疏濬人員，蒐集相關經驗及知識，將原知識庫缺漏之風險事件實務辦理方法蒐集齊全；建立疏濬工程風險知識聊天機器人，其中包含「智慧風險預防決策分析」及「智慧風險檢索」系統。「智慧風險預防決策分析」使用於風險防阻階段，如使用者輸入之風險描述未收錄於風險知識庫內，則以自然語言學習技術建立之轉譯器雙向編碼表述風險分類模型，預測該描述風險子分類與風險影響程度/發生頻率分類；反之，則採用本研究建立之關鍵字搜尋法，輸入問題敘述即可進行風險事件搜索，取得對應之風險預防措施、風險影響程度/發生頻率分類、專案管理知識體系之知識領域範疇，提醒使用者風險發生頻率及影響程度高低、涉及範圍及可能因應對策，並以視覺化方式呈現，提供使用者直觀的綜合評估結果。「智慧風險檢索」則使用於風險事件發生後，運用本研究建立之關鍵字搜索及專家系統進行搜索，連結至全台相關疏濬風險事件實務辦理方法，協助進行該項疏濬風險因應決策。本研究為使疏濬工程人員面臨風險時能有一套即時評判及辦理依據進行疏濬風險回應。該風險管理系統建構於LINE通訊軟體平台上，以友善之聊天機器人介面呈現「智慧風險預防決策分析」及「智慧風險檢索」功能，研究成果預期作為疏濬工程單位經驗知識傳承及分享的決策輔助工具。

The dredging project is a significant water conservancy project for flood control in Taiwan. However, the implementation of dredging projects has three main difficulties. These are diverse natural and man-made risk events; the knowledge gaps caused by the retirement or resignation of senior dredgers, and the lack of experience sharing platforms preventing effective problem resolution. To solve these problems, the previous research established a dredging project risk knowledge base. This knowledge base not only provides comprehensive risk prevention assessment recommendations and the practical solutions for risk events, but also assists dredging personnel in Taiwan to make dredging decisions when encountering risks. However, the existing system has several shortcomings, including incomplete solution management methods, querying by expert system only, operation by computer only, and lack of response to unknown risk events.
Therefore, this study adopts the following methods to overcome the shortcomings of the previous system. First, this work collects the experience and knowledge of the dredging engineering personnel by interviewing ten River Management Offices, and collects practical solutions for risk events that are missing from the original knowledge base. This investigation also establishes a smart risk search system that includes a text matching method. The user can search related dredging risk prevention proposals in Taiwan by typing keywords. Second, this study establishes a smart risk prevention decision analysis system for unknown risk events. This system generates risk impact–frequency analysis maps and risk responses from keywords typed into a natural language processing risk classification model to assist the dredging personnel in the preliminary risk analysis of the unknown risks.
Finally, this study applies the proposed system to establish a dredging project risk knowledge map with LINE, the most popular communication software in Taiwan. Dredging project risk knowledge map enables relevant personnel to consult information on dredging project risk prevention programs and risk response measures at any time and place. This research will hopefully simplify the implementation of dredging projects, and reduce the possibility of risk.

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