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.
L.-L. Li, W. Du· Advanced Electromagnetics· 0 citations
Reliable monitoring and forecasting of large-scale environmental hazards require efficient sensing, robust information transmission, and intelligent analysis of heterogeneous spatiotemporal data. This study proposes a smart ice-prevention framework for the Yellow River based on distributed sensing networks, digital twin technology, and multi-source information fusion. A hierarchical architecture integrating monitoring and perception networks, communication infrastructure, cloud-based computing resources, and digital twin platforms is developed to support real-time acquisition and management of hydrological, meteorological, and ice-condition information. To improve situational awareness and forecasting capability, heterogeneous data from ground sensors, video monitoring systems, unmanned aerial vehicles, and remote-sensing platforms are fused through a multidimensional spatiotemporal data model. Machine-learning-based prediction models, pattern-recognition algorithms, and knowledge-driven reasoning mechanisms are further employed to achieve ice-condition forecasting, early warning generation, and emergency-response support. Experimental deployment demonstrates that the proposed framework significantly enhances monitoring efficiency, forecasting accuracy, and decision-support capability for ice-prevention operations. By integrating distributed sensing, spatiotemporal information fusion, digital twin modeling, and intelligent forecasting, the proposed framework provides an effective methodology for large-scale monitoring systems, environmental sensing networks, and data-driven hazard management in complex dynamic environments.
W. Du, L. Li· Advanced Electromagnetics· 0 citations
