Anomaly Detection in Dam Behaviour with Machine Learning Classification Models
Dam safety assessment is typically made by comparison between the outcome of some predictive model and measured monitoring data. This is done separately for each response variable, and the results are later interpreted before decision making. In this work, three approaches based on machine learning...
Main Authors: | Fernando Salazar, André Conde, Joaquín Irazábal, David J. Vicente |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-08-01
|
Series: | Water |
Subjects: | |
Online Access: | https://www.mdpi.com/2073-4441/13/17/2387 |
Similar Items
-
A Method for Anomaly Detection in Big Data based on Support Vector Machine
by: Masoud Harimi, et al.
Published: (2019-09-01) -
An Ionospheric Anomaly Monitor Based on the One Class Support Vector Algorithm for the Ground-Based Augmentation System
by: Zhen Gao, et al.
Published: (2021-10-01) -
Comparison of New Anomaly Detection Technique for Wind Turbine Condition Monitoring Using Gearbox SCADA Data
by: Conor McKinnon, et al.
Published: (2020-10-01) -
Cascade of One Class Classifiers for Water Level Anomaly Detection
by: Fabian Hann Shen Tan, et al.
Published: (2020-06-01) -
Unsupervised Machine Learning Techniques for Detecting PLC Process Control Anomalies
by: Emmanuel Aboah Boateng, et al.
Published: (2022-03-01)