HydroAIM: LLM-based Agentic Intelligent Deep Learning Modeling for Hydrological Time Series Forecasting

Deep learning hydrological modeling typically requires extensive expert knowledge in programming, model selection, and data engineering, creating a significant barrier to efficiency and scalability. To address this challenge, we propose HydroAIM, an agentic deep learning modeling system for hydrological time series forecasting based on Large Language Model (LLM). Built upon the Model Context Protocol (MCP) to ensure standardized tool integration and modular extensibility, this system orchestrates a collaborative architecture comprising four specialized agents: task analysis agent, data preprocessing agent, model building agent, and result presentation agent. Supported by a comprehensive internal template library and toolbox, these agents autonomously execute the modeling pipeline from raw data to final evaluation. We conducted extensive compatibility tests across various LLMs and performed rigorous ablation studies to validate the necessity of the components. Experimental evaluation on the CAMELS dataset demonstrates that HydroAIM can generate reliable, expert-level modeling code. Moreover, the deep learning models constructed by HydroAIM significantly comparable to the traditional process-based Sacramento Soil Moisture Accounting (SAC-SMA) model without human intervention. Furthermore, the system also exhibits strong capability in global modeling tasks, offering a robust and scalable solution for intelligent hydrological research.