o:9614 Fine-Tuned Nonlinear Autoregressive Recurrent Neural Network Model for Dam Displacement Time Series Prediction en 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. NAR-LSTM, transfer learning, STL decomposition, DTW, time series prediction, dam displacemen 1552101 2504-4990 1552099 10.3390/make8040090 2026-05-20T11:11:02.945Z 44 no 46 Vukašin Ćirović Jaroslav Cerni Water Institute, Beograd, Serbia 0009-0006-1980-5986 Vesna Ranković Department for Applied Mechanics and Automatic Control, Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia person 0000-0002-5445-9971 Nikola Milivojević Jaroslav Černi Water Institute, Belgrade, Serbia person 0000-0001-5330-1219 Vladimir Milivojević Jaroslav Cerni Water Institute, Beograd, Serbia person 0009-0005-9113-9896 Brankica Majkić-Dursun Jaroslav Cerni Water Institute, Beograd, Serbia person 0000-0003-1518-5557 application/pdf 2710597 https://unilib.phaidrabg.rs/o:9614 no yes 16 70 92000001 71A04 Machine Learning and Knowledge Extraction 8 4 90 MDPI, Basel, Switzerland 2026 Received: 21 February 2026 Revised: 31 March 2026 Accepted: 2 April 2026 Published: 5 April 2026