Abstract
Water levels in rivers are measured by various devices installed mostly in remote locations along the rivers, and the collected data are then transmitted via telemetry systems to a data centre for further analysis and utilisation, including producing early warnings for risk situations. So, the data quality is essential. However, the devices in the telemetry station may malfunction and cause errors in the data, which can result in false alarms or missed true alarms. Finding these errors requires experienced humans with specialised knowledge, which is very time-consuming and also inconsistent. Thus, there is a need to develop an automated approach. In this paper, we firstly investigated the applicability of Deep Reinforcement Learning (DRL). The testing results show that whilst they are more accurate than some other machine learning models, particularly in identifying unknown anomalies, they lacked consistency. Therefore, we proposed an ensemble approach that combines DRL models to improve consistency and also accuracy. Compared with other models, including Multilayer Perceptrons (MLP) and Long Short-Term Memory (LSTM), our ensemble models are not only more accurate in most cases, but more importantly, more reliable.
Subject
Water Science and Technology,Aquatic Science,Geography, Planning and Development,Biochemistry
Reference61 articles.
1. Thai Flood 2011: Rapid Assessment for Resilient Recovery and Reconstruction Planning,2012
2. Disaster Risk Reduction in Thailand: Status Report 2020,2020
3. Developing Ensemble Methods for Detecting Anomalies in Water Level Data;Khampuengson;Proceedings of the International Conference on Innovative Techniques and Applications of Artificial Intelligence,2020
4. Some fundamental issues in ensemble methods
5. Anomaly detection in ECG time signals via deep long short-term memory networks;Chauhan;Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA),2021
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献