  This paper presents an automated machine learning framework designed to
assist hydrologists in detecting anomalies in time series data generated by
sensors in a research watershed in the northeastern United States critical
zone. The framework specifically focuses on identifying peak-pattern anomalies,
which may arise from sensor malfunctions or natural phenomena. However, the use
of classification methods for anomaly detection poses challenges, such as the
requirement for labeled data as ground truth and the selection of the most
suitable deep learning model for the given task and dataset. To address these
challenges, our framework generates labeled datasets by injecting synthetic
peak patterns into synthetically generated time series data and incorporates an
automated hyperparameter optimization mechanism. This mechanism generates an
optimized model instance with the best architectural and training parameters
from a pool of five selected models, namely Temporal Convolutional Network
(TCN), InceptionTime, MiniRocket, Residual Networks (ResNet), and Long
Short-Term Memory (LSTM). The selection is based on the user's preferences
regarding anomaly detection accuracy and computational cost. The framework
employs Time-series Generative Adversarial Networks (TimeGAN) as the synthetic
dataset generator. The generated model instances are evaluated using a
combination of accuracy and computational cost metrics, including training time
and memory, during the anomaly detection process. Performance evaluation of the
framework was conducted using a dataset from a watershed, demonstrating
consistent selection of the most fitting model instance that satisfies the
user's preferences.
