Jaroslaw J. Napiorkowski

Hydrodynamic derivation of storage parameters of the Muskingum model

Abstract The St. Venant equations for unsteady flow in open channels and the Muskingum method are written both in their conventional forms and in the state-space formulation. The hydrodynamic equation of motion is solved by the method of state trajectory variation and the result for the first-order variation in the state-space variables is used as a basis of linking the parameters of the Muskingum model with the hydraulic parameters of the open channel reach. The results are applicable to any shape of cross-section and to any type of friction law.

Differential Evolution algorithm with Separated Groups for multi-dimensional optimization problems

The classical Differential Evolution (DE) algorithm, one of population-based Evolutionary Computation methods, proved to be a successful approach for relatively simple problems, but does not perform well for difficult multi-dimensional non-convex functions. A number of significant modifications of DE have been proposed in recent years, including very few approaches referring to the idea of distributed Evolutionary Algorithms. The present paper presents a new algorithm to improve optimization performance, namely DE with Separated Groups (DE-SG), which distributes population into small groups, defines rules of exchange of information and individuals between the groups and uses two different strategies to keep balance between exploration and exploitation capabilities. The performance of DE-SG is compared to that of eight algorithms belonging to the class of Evolutionary Strategies (Covariance Matrix Adaptation ES), Particle Swarm Optimization (Comprehensive Learning PSO and Efficient Population Utilization Strategy PSO), Differential Evolution (Distributed DE with explorative-exploitative population families, Self-adaptive DE, DE with global and local neighbours and Grouping Differential Evolution) and multi-algorithms (AMALGAM). The comparison is carried out for a set of 10-, 30- and 50-dimensional rotated test problems of varying difficulty, including 10- and 30-dimensional composition functions from CEC2005. Although slow for simple functions, the proposed DE-SG algorithm achieves a great success rate for more difficult 30- and 50-dimensional problems.

The properties of the kernels of the Volterra series describing flow deviations from a steady state in an open channel

Abstract The deviation of the flow from a steady state in an open channel is described by a nonlinear state equation. This model is used to derive analytically the kernels of the Volterra series. The properties and the structure of the two first kernels are examined. The condition of convergence of the Volterra series depending on the magnitude of the inflow increase is also discussed.

Comparison of evolutionary computation techniques for noise injected neural network training to estimate longitudinal dispersion coefficients in rivers

This study presents the comparison of various evolutionary computation (EC) optimization techniques applied to train the noise-injected multi-layer perceptron neural networks used for estimation of longitudinal dispersion coefficient in rivers. The special attention is paid to recently developed variants of Differential Evolution (DE) algorithm. The most commonly used gradient-based optimization methods have two significant drawbacks: they cannot cope with non-differentiable problems and quickly converge to local optima. These problems can be avoided by the application of EC techniques. Although a great amount of various EC algorithms have been proposed in recent years, only some of them have been applied to neural network training - usually with no comparison to other methods. We restrict our comparison to the regression problem with limited data and noise injection technique used to avoid premature convergence and to improve robustness of the model. The optimization methods tested in the present paper are: Distributed DE with Explorative-Exploitative Population Families, Self-Adaptive DE, DE with Global and Local Neighbors, Grouping DE, JADE, Comprehensive Learning Particle Swarm Optimization, Efficient Population Utilization Strategy Particle Swarm Optimization and Covariance Matrix Adaptation - Evolution Strategy.

How novel is the novel black hole optimization approach

Due to abundance of novel optimization algorithms in recent years, the problem of large similarities among methods that are named differently is becoming troublesome and general. The question arises if the novel source of inspiration is sufficient to breed an optimization algorithm with a novel name, even if its search properties are almost the same as, or are even a simplified variant of, the search properties of an older and well-known method. In this paper it is rigidly shown that the recently proposed heuristic approach called the black hole optimization is in fact a simplified version of Particle Swarm Optimization with inertia weight. Additionally, because a large number of metaheuristics developed during the last decade is claimed to be nature-inspired, a short discussion on inspirations of optimization algorithms is presented.

Are artificial neural network techniques relevant for the estimation of longitudinal dispersion coefficient in rivers? / Les techniques de réseaux de neurones artificiels sont-elles pertinentes pour estimer le coefficient de dispersion longitudinale en rivières?

Abstract Abstract Accurate application of the longitudinal dispersion model requires that specially designed experimental studies are performed in the river reach under consideration. Such studies are usually very expensive, so in order to quantify the longitudinal dispersion coefficient, as an alternative approach, various researchers have proposed numerous empirical formulae based on hydraulic and morphometric characteristics. The results are presented of the application of artificial neural networks as a parameter estimation technique. Five different cases were considered with the network trained for different arrangements of input nodes, such as channel depth, channel width, cross-sectionally averaged water velocity, shear velocity and sinuosity index. In the case where the sinuosity index is included as an input node, the results turned out to be better than those presented by other authors.

Climate Change Impact on Hydrological Extremes: Preliminary Results from the Polish-Norwegian Project

This paper presents the background, objectives, and preliminary outcomes from the first year of activities of the Polish–Norwegian project CHIHE (Climate Change Impact on Hydrological Extremes). The project aims to estimate the influence of climate changes on extreme river flows (low and high) and to evaluate the impact on the frequency of occurrence of hydrological extremes. Eight “twinned” catchments in Poland and Norway serve as case studies. We present the procedures of the catchment selection applied in Norway and Poland and a database consisting of near-natural ten Polish and eight Norwegian catchments constructed for the purpose of climate impact assessment. Climate projections for selected catchments are described and compared with observations of temperature and precipitation available for the reference period. Future changes based on those projections are analysed and assessed for two periods, the near future (2021–2050) and the far-future (2071–2100). The results indicate increases in precipitation and temperature in the periods and regions studied both in Poland and Norway.

Comparing large number of metaheuristics for artificial neural networks training to predict water temperature in a natural river

Nature-inspired metaheuristics found various applications in different fields of science, including the problem of artificial neural networks (ANN) training. However, very versatile opinions regarding the performance of metaheuristics applied to ANN training may be found in the literature.Both nature-inspired metaheuristics and ANNs are widely applied to various geophysical and environmental problems. Among them the water temperature forecasting in a natural river, especially in colder climate zones where the seasonality plays important role, is of great importance, as water temperature has strong impact on aquatic life and chemistry. As the impact of possible future climate change on water temperature is not trivial, models are needed to allow projection of streamwater temperature based on simple hydro-meteorological variables.In this paper the detailed comparison of the performance of nature-inspired optimization methods and Levenberg-Marquardt (LM) algorithm in ANNs training is performed, based on the case study of water temperature forecasting in a natural stream, namely Biala Tarnowska river in southern Poland. Over 50 variants of 22 various metaheuristics, including a large number of Differential Evolution, as well as some Particle Swarm Optimization, Evolution Strategies, multialgorithms and Direct Search methods are compared with LM algorithm on ANN training for the described case study. The impact of population size and some control parameters of particular metaheuristics on the ANN training performance are verified. It is found that despite widely claimed large improvement in nature-inspired methods during last years, the vast majority of them are still outperformed by LM algorithm on the selected problem. The only methods that, based on this case study, seem competitive to LM algorithm in terms of the final performance (but not speed) are Differential Evolution algorithms that benefit from the concept of Global and Local neighborhood-based mutation operators. The streamwater forecasting performance of the neural networks is adequate, the major prediction errors are related to the river freezing and melting processes that occur during winter in the mountainous catchment under study. The applicability of metaheuristics to neural networks training is verified.Levenberg-Marquardt and DEGL algorithms outperform other training methods.In case of Differential Evolution methods population size is crucial.Neural networks appear to be useful for water temperature predictions in rivers.

Swarm Intelligence and Evolutionary Algorithms

The popularity of metaheuristics, especially Swarm Intelligence and Evolutionary Algorithms, has increased rapidly over the last two decades. Numerous algorithms are proposed each year, and progressively more novel applications are being found. However, different metaheuristics are often compared by their performance on problems with an arbitrarily fixed number of allowed function calls. There are surprisingly few papers that explore the relationship between the relative performance of numerous metaheuristics on versatile numerical real-world problems and the number of allowed function calls.In this study the performance of 33 various metaheuristics proposed between 1960 and 2016 have been tested on 22 numerical real-world problems from different fields of science, with the maximum number of function calls varying between 5000 and 500,000. It is confirmed that the algorithms that succeed in comparisons when the computational budget is low are among the poorest performers when the computational budget is high, and vice versa. Among the tested variants, Particle Swarm Optimization algorithms and some new types of metaheuristics perform relatively better when the number of allowed function calls is low, whereas Differential Evolution and Genetic Algorithms perform better relative to other algorithms when the computational budget is large. It is difficult to find any metaheuristic that would perform adequately over all of the numbers of function calls tested. It was also found that some algorithms may become completely unreliable on specific real-world problems, even though they perform reasonably on others.

Linear flood routing model for rapid flow

The linear flood routing model presented has been derived from the linearized St. Venant equation for the case of a uniform open channel with arbitrary cross-sectional shape and friction law. In order to filter out the downstream boundary condition the kinematic wave solution is used to approximate the diffusion term in the St. Venant equation. The hydrodynamic model obtained is called the rapid flow model (RFM). It provides the exact solution for a Froude number equal to one. Such characteristics of the RFM impulse response as cumulants, amplitude and phase spectra are analysed, and then compared with those of the complete linearized St. Venant equations for different reach lengths, values of Froude number and frequencies of flood waves. The RFM can be applied for mountainous rivers that have large Froude numbers and both quick and slow rising waves.