Ensuring the completeness and accuracy of dam safety monitoring data is critical for structural health assessment and risk management. Traditional monitoring systems often lack mechanisms to systematically validate data integrity across time and space, resulting in missed anomalies and delayed interventions. This paper presents a web-based framework for spatiotemporal integrity verification tailored to dam safety applications. The framework combines rule-based validation logic, semantic metadata modeling, and interactive web visualization to automate the detection of missing values, inconsistent sampling, and logical violations. It supports real-time data ingestion, validation execution, and anomaly reporting through an intuitive web interface. A case study using publicly available dam datasets demonstrates the system’s effectiveness in identifying data gaps and improving operational awareness. Evaluation results confirm high accuracy in anomaly detection, efficient processing under load, and enhanced usability for engineering users. The proposed solution offers a scalable, extensible, and domain-aware platform for intelligent infrastructure monitoring.

Evidence for image classification can be considered to come from two sources: traditional statistical information derived algorithmically from image data, and model-based evidence arising from previous expertise and experience in a given application