Vinay Pursnani, Carlos Erazo Ramirez, Muhammed Yusuf Sermet, Ibrahim Demir

In the hydrology and environmental domains, researchers often encounter complex hydrological models, evolving frameworks and libraries, and complex documentation, which necessitate both domain knowledge and coding expertise. This paper introduces HydroSuite-AI, a large language model-enhanced web application designed to address these challenges by integrating three open-source libraries: HydroLang, HydroCompute, and HydroRTC. HydroSuite-AI assists researchers by generating code snippets, providing an execution environment, and answering factual questions related to these libraries, thereby facilitating seamless integration into existing hydrological workflows. Through natural language processing and generative AI techniques, HydroSuite-AI aims to streamline analysis processes and improve user productivity. The effectiveness of the application is assessed through case studies and user feedback, demonstrating its potential to support hydrological research and education by offering an accessible and comprehensive platform for data analysis, code generation, and knowledge dissemination.