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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]

Soccer player performance is influenced by multiple unpredictable factors. During a game, score changes and pre-game expectations affect the effort exerted by players. This study used GPS wearable sensors to track players' energy expenditure in 5-min intervals, alongside recording the goal timings and the win and lose probabilities from betting sites. A mathematical model was developed that considers pre-game expectations (e.g., favorite, non-favorite), endurance, and goal difference (GD) dynamics on player effort. Particle Swarm and Nelder–Mead optimization methods were used to construct these models, both consistently converging to similar cost function values. The model outperformed baselines relying solely on mean and median power per GD. This improvement is underscored by the mean absolute error (MAE) of 396.87 ± 61.42 and root mean squared error (RMSE) of 520.69 ± 88.66 achieved by our model, as opposed to the B 1 MAE of 429.04 ± 84.87 and RMSE of 581.34 ± 185.84 , and B 2 MAE of 421.57 ± 95.96 and RMSE of 613.47 ± 300.11 observed across all players in the dataset. This research offers an enhancement to the current approaches for assessing players' responses to contextual factors, particularly GD. By utilizing wearable data and contextual factors, the proposed methods have the potential to improve decision-making and deepen the understanding of individual player characteristics. [ABSTRACT FROM AUTHOR]