Naslov (eng)

Fine-Tuned Nonlinear Autoregressive Recurrent Neural Network Model for Dam Displacement Time Series Prediction

Autor

Ćirović, Vukašin
Ranković, Vesna
Milivojević, Nikola
Milivojević, Vladimir
Majkić-Dursun, Brankica

Publisher

Opis (eng)

Dam monitoring data are nonlinear and nonstationary time series. Most existing data-driven dam displacement models are developed independently for each measuring point, disregarding the fact that a dam is a complex structure composed of various interconnected elements that form a unified whole. Regardless of the dam type, all points on the dam are exposed to the same external environmental influences. To account for the correlation between displacement time series at different points, this paper proposes a novel finetuned deep-learning nonlinear autoregressive (NAR) model based on a Long Short-Term Memory (LSTM) network for predicting dam tangential displacement, and a new method for generating source data to train the base model. The models for three measuring points were developed and tested on experimental data collected over a period of slightly more than twelve years. Compared with the model without fine-tuning, the proposed approach achieves an average mean square error (MSE) reduction of 80.68% on the training set and 65.79% on the test set, as well as an average mean absolute error (MAE) reduction of 51.05% and 52.62%, respectively. Furthermore, the proposed model outperforms Random Forest (RF), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP) models for dam displacement prediction.

Jezik

engleski

Datum

2026

Licenca

Ovo delo je licencirano pod uslovima licence
Creative Commons CC BY 4.0 - Creative Commons Autorstvo 4.0 International License.

http://creativecommons.org/licenses/by/4.0/legalcode

Predmet

NAR-LSTM, transfer learning, STL decomposition, DTW, time series prediction, dam displacemen

Deo kolekcije (1)

o:243

Institut za vodoprivredu "Jaroslav Černi"