2023 Volume 79 Issue 22 Article ID: 22-22049
This study demonstrates that our machine learning-based detection technique performs good classification of multiple types of normal and anomaly values that usually occur due to sensor problems in time series data. Water level data in our agricultural field measurement usually includes typical-pattern (i.e., normal), flood-based, spike noise, and slide-shifted data. We introduced a self-organizing map (SOM) to classify these four-type items at the same time. Then, the four classifications were visualized on the 2D map using three types of clustering methods (K-means, Ward, and Vote). The accuracy evaluation for the classification was performed by Accuracy and f1 score based on the rule of binary classification. The accuracy evaluation shows that the Vote method had better scores compared with the other methods. Additionally, for the Vote method, true values for the four classifications were mostly plotted on the same classification region, determined by clustering methods.
