# AIoT Monitoring Technology for Optimal Fill Dam Installation and Operation
## 2 Literature Review
Furthermore, they established a communication network as a control network to ensure secure data management, protecting against external hacking and intrusion. However, in the collected data, normal data cannot be distinguished from abnormal data; the presence of abnormal data can cause malfunctioning, resulting in economic losses. [PERSON] et al. [13] proposed an automated framework based on data microservices for accurate automated analysis of dam monitoring data. The framework comprised structural components, monitoring sensors, finite element models, geometric models, mathematical models, and digital virtual models integrated with deep learning algorithms. Long short-term memory was used for accurate predictions. The framework was tested on a dam safety monitoring site. The results showed that it improved the prediction accuracy of the monitored data with an error of 0.35 mm. In contrast to the above-mentioned studies, this study implements a \"malfunction prevention\" feature to enhance the accuracy. This malfunction prevention feature improves the accuracy of the monitored data by compensating for measurement errors or communication errors. In this study, we employed a statistics-based data filtering technique to achieve this feature, thus minimizing false alarms in anomaly detection. [PERSON] et al. [14] constructed a real-time displacement monitoring system based on GNSS in 37 dams in Korea. The system consists of a base, rover, and server. The base was installed at a point that can represent the ground around a dam facility, and the rover was installed at the major points of the dam structure to monitor the external displacement. The base, which can identify accurate coordinate data, was constructed to correct the errors generated during the signal transmission process from the satellite to the GNSS receiver. The base offsets the errors by transmitting the corrected data to the rover with the same errors through the satellite. The displacement data thus obtained were compared with the data measured using an electronic distance measurement device. The results showed that stable measurement was performed, as the distribution range of the GNSS-based displacement monitoring system data was within \(\pm\)5 mm. In contrast to the previous studies, our study offsets errors through data validation by using \"sensor nodes\" and \"gateways\". The measurement data obtained from the sensor are transmitted to the sensor node to perform a cyclic redundancy check (CRC), and the generated CRC data are selected through data verification at the gateway. The unselected data are excluded by performing re-measurements, and only the selected data are sent to the server to ensure data quality. [PERSON] et al. [15] established a prediction model using an artificial neural network to monitor the deformation of a dam based on its subsidence measurement data. The proposed model was proven to be effective and highly practical in predicting dam deformation. [PERSON] [16] developed IoT applications in Dam Safety and water management. The IoT applications can monitor the entire dam and the main pipeline for 24 h, every day through various sensors. These wireless sensor nodes are connected to each other and transmit the data to a gateway. Common storage space 'CLOUD' stores provide online information to the observer. This has contributed to dam safety and water conservation. [PERSON] et al. [17] proposed a hybrid prediction model based on Bayesian Optimization and Random Forest to predict dam deformation. Initially, the monitoring data were pre-processed, and the parameters for the Random Forest model were configured. After determining the optimal parameters through Bayesian Optimization based on Gaussian processes, the relationship between the dam deformation and influencing factors was explained using the Gini coefficient. The developed prediction model demonstrated high prediction accuracy in tests by showing only a small discrepancy between the predicted values and actual measurements.