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Understanding the dynamics of tidal rivers and estuaries is critical for reliable water management. Recently, the use of Machine Learning (ML) has increased in favor of hydrologic and hydraulic models. The advantages of ML over physically based models are most evident in modeling complex and nonlinear hydrologic processes and inverse problems. This study provides a critical review of ML approaches for forecasting, reconstruction, and establishment of stage-discharge relationships in tidal rivers and estuaries characterized by nonlinear interaction between the river and coastal processes. Gaps in this research area and the limited number of stage-discharge studies are identified and explained. The advantages and limitations of each approach are discussed from a critical perspective, and suggestions are made for future research directions. Advanced Recurrent Neural Networks (RNNs) and hybrid modeling systems combining physically based models with ML appear to be the most promising approaches for modeling complex physical processes in these environments. • A comprehensive review of machine learning techniques for predicting water level and discharge. • Focused on tidal rivers and estuaries characterized by nonlinear interactions. • Discussion on limitations and advantages of the machine learning approaches. • Presentation of hybrid approaches that performed better than just machine learning models. • Recommendations for future research directions. [ABSTRACT FROM AUTHOR]

The paper presents an effective solution for improving power system state estimation performance by applying the intersection of confidence intervals (ICI) algorithm, a state-of-the-art adaptive signal processing technique used in signal denoising. Since many power utilities worldwide still run state estimators based on the weighed least squares (WLS) algorithm using supervisory control and data acquisition (SCADA) measurements, the ICI algorithm is added to pre-process SCADA measurements without changing the structure of the WLS algorithm. Due to its adaptive window size and high sensitivity to noise in the input measurement series, the proposed ICI-based solution results in an enhancement of the state estimator output and overall performance when compared to the original algorithm. As test beds, the IEEE systems with 30 and 118 buses were used, while as an example of the real power system, the complete mathematical model of the Croatian transmission power system was simulated. Several case studies indicate that the ICI-based state estimator reduces the input measurements mean squared error by up to 30.8%, the mean absolute error by up to 20.8%, and the maximum estimation error by up to 22.6%. Furthermore, this also led to an enhanced final output of the state estimator for all tested systems in view of the state estimation accuracy and convergence. [ABSTRACT FROM AUTHOR]

On-site estimation of sea state parameters is crucial for ship navigation. Extensive research has been conducted on model-based estimation utilizing ship motion responses. Model-free approaches based on machine learning (ML) have recently gained popularity, and estimation from time-series of ship motion responses using deep learning (DL) methods has given promising results. In this study, we apply the novel, attention-based neural network (AT-NN) for estimating wave height, zero-crossing period, and relative wave direction from raw time-series data of ship pitch, heave, and roll. Despite reduced input data, it has been demonstrated that the proposed approaches by modified state-of-the-art techniques (based on convolutional neural networks (CNN) for regression, multivariate long short-term memory CNN, and sliding puzzle neural network) improved estimation MSE, MAE, and NSE by up to 86%, 66%, and 56%, respectively, compared to the best performing original methods for all sea state parameters. Furthermore, the proposed technique based on AT-NN outperformed all tested methods (original and enhanced), improving estimation MSE by 94%, MAE by 74%, and NSE by 80% when considering all sea state parameters. Finally, we proposed a novel approach for interpreting the uncertainty estimation of neural network outputs based on the Monte-Carlo dropout method to enhance the model's trustworthiness. • Enhanced 3 state-of-the-art models with improved estimation of sea state parameters. • Novel attention-based neural network enhancing 3 original and 3 improved models. • Sea state parameters' estimation improved by up to MSE 94%, MSE 74%, and NSE 80%. • Better results achieved utilizing only three ship motion time-series. • Novel way of interpreting the uncertainty estimation of neural network outputs. [ABSTRACT FROM AUTHOR]