Agricultural reservoirs are increasingly exposed to disaster risks due to climate change-induced extreme precipitation and progressive facility aging. In South Korea, more than 20 reservoir collapse incidents have occurred since 2000, including 25 embankment failures during July–August 2020 alone. Of the 17,106 agricultural reservoirs nationwide, 50.8% were constructed before 1945 and 85.4% are over 30 years old, underscoring the urgent need for advanced safety monitoring systems. Analysis of 101 reservoir failures over the past two decades indicates that reservoirs aged 55–60 years exhibit the highest failure rates, with concentrated rainfall identified as the dominant triggering factor.Current reservoir safety management systems rely primarily on deterministic approaches with simple threshold-based sensor decision rules, which are inadequate for addressing uncertainties in hydrological processes, geotechnical conditions, and structural behavior. Existing early warning concepts often assume automated spillway controls or movable weirs that are impractical for small- and medium-sized agricultural reservoirs, while fragmented implementation of rehabilitation projects, disaster monitoring, and warning systems hinders integrated risk management and effective disaster response.This study presents the development of a modular integrated reservoir monitoring system designed to overcome these limitations through three core components: (1) heterogeneous modular sensor technology; (2) an on-device integrated operation platform; and (3) a big data-based disaster analysis framework.The modular sensor system integrates three or more hybrid sensor types to enable simultaneous surface and subsurface monitoring. A modular architecture with interchangeable sensor blocks allows flexible deployment, independent replacement, and future system upgrades. Laboratory performance evaluations confirmed measurement accuracy and stability under diverse environmental conditions.The on-device integrated operation platform resolves data heterogeneity through standardized data transformation and mapping protocols. A unified gateway supports real-time data streaming and messaging via broker-based communication, enabling bidirectional data processing for monitoring, control, and fault detection. Dual data backup mechanisms ensure system continuity during network disruptions, while edge computing capabilities reduce latency for critical decision-making when on-site access is restricted during extreme weather events.The big data analytics framework focuses on minimizing measurement errors and processing anomalies inherent in heterogeneous sensor networks. By analyzing disaster-related anomaly patterns and applying multi-sensor data fusion techniques, the system enhances early warning detection capability beyond that of single-sensor approaches.The proposed integrated system addresses key operational challenges, including multi-manufacturer sensor compatibility, remote accessibility under adverse conditions, cost-effective scalability, and automated decision support that reduces reliance on subjective operator judgment. Field implementation targets aging reservoirs with high-risk profiles identified through historical failure analysis, providing testbeds for system validation and refinement.This research establishes a technical foundation for risk-based reservoir safety assessment that explicitly incorporates hydrological, geotechnical, and structural uncertainties, representing a transition from deterministic to probabilistic monitoring paradigms consistent with international best practices in dam safety management(Project No. RS-2025-02263904, second year).

Internet of Things (IoT)-based slope monitoring systems support real-time stability surveillance, but frequent false alarms cause alarm fatigue and delay responses to genuine hazards. We developed a clustering-based framework that classifies alarm causes into three categories—Malfunction, Actual Displacement, and Others—from inclinometer and extensometer data. From 55 alarm events recorded at 43 monitoring stations in 2025, 85 domain-informed features (statistical, waveform, spatial-correlation, trend, sudden-synchronization, and malfunction-pattern) were extracted and compared across five feature-extraction methods, six selection levels, and three clustering algorithms. Because the feature ranking uses expert-assigned labels, the framework is characterized as label-informed feature ranking followed by unsupervised clustering, and it was evaluated with 100 random seeds, nested leave-one-site-out validation, bootstrap confidence intervals, and permutation tests. A compact set of five features—three cross-correlation-based synchronization features and two spatial-correlation features—was stable across seeds and achieved a leave-one-site-out accuracy of 0.80 (95% CI 0.69–0.89) with the full 8 h window. Using only data recorded up to the alarm time, accuracy rose to 0.91 (0.84–0.98), 12 of the 13 Actual Displacement events were recovered (recall 0.92), and no non-displacement event was assigned to the displacement cluster. Synchronization features separated genuine displacement from the other causes, whereas spatial-correlation features distinguished external disturbance from sensor malfunction. The interpretable framework is proposed as a decision-support filter that complements conventional threshold-based alarms.