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- MakaleAçık Erişim2025Advancing sea level anomaly modeling in the black sea with LSTM Auto-Encoders: A novel approach
Yavuzdoğan, A.; Tanir Kayıkçı, E.
Rising sea levels pose significant risks to coastal communities and ecosystems. Accurate modeling of sea level changes is crucial for effective environmental management and disaster mitigation. Machine learning methods are emerging as an important asset in improving sea level predictions and understanding the impacts of climate change. Especially, Long Short-Term Memory (LSTM) models have emerged as a powerful tool for sea level anomaly modeling, but there is an increasing need for more advanced models in this area. This study enhances existing methodologies by introducing a novel approach using an LSTM Auto-Encoder model, designed to compress input data into a lower-dimensional latent space before reconstructing it, thereby capturing complex temporal dependencies and anomalies effectively. We compared LSTM Auto-Encoder model performance with that of a Stacked LSTM network, which learns complex temporal patterns through multiple layers, and a traditional damped-persistence statistical model. Our results demonstrate that the LSTM Auto-Encoder model not only outperformed these models in predicting sea level anomalies across various lead times but also exhibited superior generalization capabilities across both satellite altimeter and in-situ data. These findings highlight the potential of the LSTM Auto-Encoder model as a powerful tool in coastal management and climate change studies, underscoring the critical role of advanced machine learning techniques in enhancing our predictive abilities and informing disaster preparedness strategies.
- MakaleYalnız künye2021Cost-oriented LSTM methods for possible expansion of control charting signals
Ünlü, Ramazan
Manual quality control may result in delayed detection of a system defect, or none at all, potentially resulting in malfunctions that can lead to disruption of the system, incur extra costs, or complete downtime of the system. To mitigate such issues, more advanced statistics and machine learning-based systems such as SVM, WSVM, etc. are used to automatically detect error signals during the process. However, these methodologies are not developed for sequential dataset such as a classical CCPR dataset. As a result, in this study, we have implemented a preliminary Cost-Oriented Long-Short Term Memory (LSTM), which is designed to learn from a sequential dataset and compare it with SVM and WSVM which are traditional methods utilized in the field. Additionally, we compared the performance of methods in terms of early detection of an abnormal pattern. Based on the findings, the Cost-Oriented LSTM method outperforms SVM and WSVM in the majority of abnormal patterns in terms of both classification and early detection performance. Global accuracy score which is the average of accuracy rates in all combination of abnormal parameters and window lengths shows that LSTM gives a better accuracy score than SVM and WSVM in all seven abnormal patterns detection.