Deep learning-based chatbot by natural language processing for supportive risk management in river dredging projects

Abstract

Dredging projects play a vital role in water conservation, especially for flood control, but they often encounter natural and human-induced risks. This study addresses these challenges by creating a comprehensive knowledge base for dredging risks. This knowledge base serves several essential functions: it aids in immediate and thorough risk prevention, provides practical risk management recommendations, and assists dredging personnel in making informed decisions when dealing with risks. Furthermore, the study introduces an advanced deep-learning model that enhances access to critical information. Users can quickly find relevant risk prevention strategies by inputting keywords. The model's distinctive feature is its ability to analyze unforeseen risks through natural language processing, predict the potential impact and frequency of unexpected events, and propose appropriate risk response measures. This empowers dredging personnel to conduct preliminary risk assessments confidently. The deep learning model seamlessly integrates into LINE's communication platform, creating a river dredging project risk knowledge chatbot. This user-friendly chatbot is accessible to personnel at all project stages, enabling real-time interaction. It offers a practical way to develop and implement risk prevention plans and response measures. In summary, this research presents an innovative approach that enhances the efficiency and safety of dredging projects. By merging a knowledge base with cutting-edge technology and real-time communication, it equips dredging personnel to manage known and unforeseen risks proficiently, ultimately contributing to the success of water conservancy and flood control projects.