Research on the Construction of a Flood Control Four-Forecast Application System Based on Large-Model Intelligent Agent Application Architecture
TL;DR
The framework leverages 5G-enabled wireless communication, edge-cloud computing collaboration, and antenna-supported sensing platforms, offering an engineering-oriented solution for rapid, adaptive, and robust flood control operations in industrial environments.
Abstract
This study presents the design and implementation of a flood control “Four Predictions” (forecasting, early warning, simulation, contingency planning) application system tailored in flood-prone areas, based on a Large-Model Intelligent Agent Application Architecture. The system integrates multimodal flood control data, including meteorological observations, hydrological measurements, satellite remote sensing imagery, and social media reports, processed through a dynamic data fusion framework. The model layer combines domain-adapted large language models (BERT) with specialized micro-model clusters for precipitation forecasting, dam breach simulation, and regional vulnerability assessment. Perception agents employ Isolation Forest and Kalman filtering for real-time anomaly detection, cognitive agents utilize dynamic Bayesian networks and DQN-based adaptive warning thresholds, and action agents manage emergency resource allocation via an auction mechanism and optimized evacuation routing. Multiscale 3D simulations, ST-ConvNet forecasting, and uncertainty quantification through Monte Carlo sampling provide precise, high-resolution support for decision-making. Technical validation against historical extreme rainfall events confirms the system’s ability to enhance flood response accuracy and reduce false alarms. The framework leverages 5G-enabled wireless communication, edge-cloud computing collaboration, and antenna-supported sensing platforms, offering an engineering-oriented solution for rapid, adaptive, and robust flood control operations in industrial environments.