For the purposes of planning and operation of maritime activities, information about wave height dynamics is of great importance. In the paper, real-time prediction of significant wave heights for the following 0.5–5.5 h is provided, using information from 3 or more time points. In the first stage, predictions are made by varying the quantity of significant wave heights from previous time points and various ways of using data are discussed. Afterwards, in the best model, according to the criteria of practicality and accuracy, the influence of wind is taken into account. Predictions are made using two machine learning methods – artificial neural networks (ANN) and support vector machine (SVM). The models were built using the built-in functions of software Weka, developed by Waikato University, New Zealand

Modelling and prediction of mean monthly flow are of particular importance for long-term planning in hydrology and water resources management. Therefore, a simplified and robust modelling procedure, derived from clearly and concisely established methodology, can benefit both researchers and practitioners. The main objective of this study is to develop a robust yet simple model, capable of producing predictions of satisfactory accuracy on previously unseen data. Two chronological data allocation strategies (C1 and C2), differing in the proportions of training, calibration, and verification subsets, were evaluated to analyze their influence on model accuracy and reliability. Chain and ensemble modelling techniques were applied, resulting in several stacking regressors with different combinations of base models and final estimators. The best-performing ensemble (C2) consisted of a support vector machine, histogram gradient boosting regressor, elastic net, and two dummy regressors as base models, with an artificial neural network as the final estimator. Within the ensemble structure, dummy regressors and histogram gradient boosting regressor were used to extend the predictive range, while elastic net and support vector machine captured the overall flow bias and fundamental flow dynamics. The artificial neural network final estimator was used to integrate these components into the final flow prediction. Compared to C1, the C2 allocations strategy achieved improved generalization capability and narrower confidence intervals due to the larger training subset, indicating higher model reliability for long-term monthly flow forecasting. The study additionally emphasizes the importance of appropriate methodological workflow, careful dataset treatment, and comprehensive model evaluation using complementary statistical and hydrological analysis tools.

Design hyetographs derived with the Method of average variability (MAV) depend on two decisions usually made subjectively: the boundaries of the duration classes used to average rainfall events and the number of blocks into which the events are divided. This study proposes an objective framework for both decisions and applies it to 259 events lasting 10–120 minutes from 10 years of 5-minute rainfall data in Osijek, Croatia. The stability of each candidate MAV hyetograph was quantified by bootstrap subsampling. Admissibility required a sufficient number of rainfall events, stable peak characteristics and compliance with the prescribed discretisation constraints. Among admissible combinations, the partition of the duration range was found by exact dynamic programming (DP), implicitly considering approximately 2.1 million possible partitions. As peak dispersion increased with block count, stability was treated as a constraint, while the deviation of block durations from the recording interval was minimised. The data supported four to five classes; the selected partition, [10, 25], (25, 45], (45, 70] and (70, 120] min with 3, 7, 12 and 16 blocks, satisfied all criteria. Two subjectively defined partitions had almost three times poorer resolution and at least twice the peak dispersion, and only one of their nine classes was admissible.

The primary processing of high-resolution precipitation records (5 min and shorter) is crucial for constructing dimensionless design hyetographs and identifying design-critical precipitation scenarios for urban drainage systems. A key step in this process is separating continuous precipitation records into individual precipitation events, typically based on minimum inter-event time (MIT) and precipitation amount thresholds. This separation directly influences the subsequent analysis steps and the accuracy of the design hyetographs. Building upon this foundation, this study systematically analyses how different MIT determination methods influence the construction of dimensionless Huff hyetographs in a moderately humid continental climate. Three approaches for defining MIT were examined: a fixed MIT method (1–12 h), an autocorrelation-based method (AC), and a kernel density estimation approach (KDE). The analysis also considers the effects of minimum precipitation thresholds (P = 1, 3, and 5 mm) and precipitation duration classes (all durations and short-duration events with $T\le 2$ h), utilising a continuous 10-year series of 5 min precipitation data. The results demonstrate that the choice of MIT substantially affects the identified precipitation events, duration, total amount, and the median Huff curve’s shape, especially for precipitation types with early and late maximum intensity. Specifically, increasing MIT values produces longer and deeper events with steeper Huff curves, while precipitation thresholds mainly filter weaker events rather than impacting peak intensities. The AC method yields results similar to larger fixed MIT values (≈6–9 h), whereas the KDE method corresponds to shorter separations (≈1–3 h). To unify the assessment of design relevance, a composite design index combining Huff curve slope and short-term peak intensities was introduced. Analysis shows that short-duration convective precipitation with an early maximum is the most critical design scenario. However, late-maximum events (events in which peak intensity occurs in the fourth quartile of storm duration, Type 4) can become equally critical when longer MIT values or autocorrelation-based separation are applied. These findings underscore the importance of a transparent and methodologically consistent definition of precipitation event separation criteria when using dimensionless hyetographs in urban drainage design.