Trends and Applications for Large Language Models in Water Resource Engineering in the Context of Thailand

Abstract

Development of Large Language Models (LLMs), such as OpenAI's GPT, transformed various fields through their advanced predictive and analytical capabilities. In water resource management, particularly in the context of Thailand, LLMs provided a significant opportunity to enhance forecasting accuracy, resource allocation, and disaster preparedness. This study explored current research trends on the application of LLMs in water resource forecasting and examined strategies for integrating these technologies to address Thailand’s water resource challenges. The study highlighted key methodologies, benefits, and challenges while proposing practical approaches for effectively incorporating LLMs into Thailand’s water management systems. Data was gathered from over 5,000 research articles obtained through keyword searches in ChatGPT, SciSpace, and CrossRef databases. The study analyzed publication trends and keyword frequencies using VOSViewer and classified water resource-related research at the continental level, with a specific focus on Thailand in comparison to ASEAN and global trends. The findings indicated that water resource engineering remained closely linked to climate change research, with Thailand showing a significant increase in related studies. However, recent trends revealed an exponential growth in AI and Machine Learning applications within water resource engineering. Therefore, it was concluded that in the future, there will be a rising number of research publications integrating AI, water resource engineering, and climate change.