•Four ANN types are applied to streamwater temperature prediction.•Tests are performed for lowland and mountainous rivers in temperate climate zones.•The choice of the best ANN type depends on the ...way the comparison is performed.•ANN ensemble aggregation approach should be used in practical applications.•All ANN types outperform k-nearest neighbour approach.
A number of methods have been proposed for the prediction of streamwater temperature based on various meteorological and hydrological variables. The present study shows a comparison of few types of data-driven neural networks (multi-layer perceptron, product-units, adaptive-network-based fuzzy inference systems and wavelet neural networks) and nearest neighbour approach for short time streamwater temperature predictions in two natural catchments (mountainous and lowland) located in temperate climate zone, with snowy winters and hot summers. To allow wide applicability of such models, autoregressive inputs are not used and only easily available measurements are considered. Each neural network type is calibrated independently 100 times and the mean, median and standard deviation of the results are used for the comparison. Finally, the ensemble aggregation approach is tested.
The results show that simple and popular multi-layer perceptron neural networks are in most cases not outperformed by more complex and advanced models. The choice of neural network is dependent on the way the models are compared. This may be a warning for anyone who wish to promote own models, that their superiority should be verified in different ways.
The best results are obtained when mean, maximum and minimum daily air temperatures from the previous days are used as inputs, together with the current runoff and declination of the Sun from two recent days. The ensemble aggregation approach allows reducing the mean square error up to several percent, depending on the case, and noticeably diminishes differences in modelling performance obtained by various neural network types.
We show that in twisted microstructured optical fibers (MOFs) the coupling between the core and cladding modes can be obtained for helix pitch much greater than previously considered. We provide an ...analytical model describing scaling properties of the twisted MOFs, which relates coupling conditions to dimensionless ratios between the wavelength, the lattice pitch and the helix pitch of the twisted fiber. Furthermore, we verify our model using a rigorous numerical method based on the transformation optics formalism and study its limitations. The obtained results show that for appropriately designed twisted MOFs, distinct, high loss resonance peaks can be obtained in a broad wavelength range already for the fiber with 9 mm helix pitch, thus allowing for fabrication of coupling based devices using a less demanding method involving preform spinning.
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.
In recent years sampling approaches have been used more widely than optimization algorithms to find parameters of conceptual rainfall-runoff models, but the difficulty of calibration of such models ...remains in dispute. The problem of finding a set of optimal parameters for conceptual rainfall-runoff models is interpreted differently in various studies, ranging from simple to relatively complex and difficult. In many papers, it is claimed that novel calibration approaches, so-called metaheuristics, outperform the older ones when applied to this task, but contradictory opinions are also plentiful. The present study aims at calibration of two simple lumped conceptual hydrological models, HBV and GR4J, by means of a large number of metaheuristic algorithms. The tests are performed on four catchments located in regions with relatively similar climatic conditions, but on different continents. The comparison shows that, although parameters found may somehow differ, the performance criteria achieved with simple lumped models calibrated by various metaheuristics are very similar and differences are insignificant from the hydrological point of view. However, occasionally some algorithms find slightly better solutions than those found by the vast majority of methods. This means that the problem of calibration of simple lumped HBV or GR4J models may be deceptive from the optimization perspective, as the vast majority of algorithms that follow a common evolutionary principle of survival of the fittest lead to sub-optimal solutions.
Over the last two decades numerous metaheuristics have been proposed and it seems today that nobody is able to understand, evaluate, or compare them all. In principle, optimization methods, including ...the recently popular Evolutionary Computation or Swarm Intelligence-based ones, should be developed in order to solve real-world problems. Yet the vast majority of metaheuristics are tested in the source papers on artificial benchmarks only, so their usefulness for various practical applications remains unverified. As a result, choosing the proper method for a particular real-world problem is a difficult task. This paper shows that such a choice is even more complicated if one wishes, with good reason, to use metaheuristics twice, once to find the best and then to find the worst solutions for the specific numerical real-world problem. It often occurs that for either case different optimizers are to be recommended. The above finding is based on testing 30 metaheuristics on numerical real-world problems from CEC2011. First we solve 22 minimization problems as defined for CEC2011. Then we reverse the objective function for each problem and search for its maximizing solution. We also observe that algorithms that are highly ranked on average may not perform best for any given specific problem. Rather, the highest average ranking may be achieved by methods that are never among the poorest ones. In other words, occasional winners may get less attention than rare losers.
Artificial neural networks (ANNs) become widely used for runoff forecasting in numerous studies. Usually classical gradient-based methods are applied in ANN training and a single ANN model is used. ...To improve the modelling performance, in some papers ensemble aggregation approaches are used whilst in others, novel training methods are proposed. In this study, the usefulness of both concepts is analysed. First, the applicability of a large number of population-based metaheuristics to ANN training for runoff forecasting is tested on data collected from four catchments, namely upper Annapolis (Nova Scotia, Canada), Biala Tarnowska (Poland), upper Allier (France) and Axe Creek (Victoria, Australia). Then, the importance of the search for novel training methods is compared with the importance of the use of a very simple ANN ensemble aggregation approach. It is shown that although some metaheuristics may slightly outperform the classical gradient-based Levenberg-Marquardt algorithm for a specific catchment, none performs better for the majority of the tested ones. One may also point out a few metaheuristics that do not suit ANN training at all. On the other hand, application of even the simplest ensemble aggregation approach clearly improves the results when the ensemble members are trained by any suitable algorithms.
EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR E. Toth
We report a detailed study of vector modulation instability (VMI) in highly birefringent fibers with circularly polarized modes in the normal dispersion regime. We show that because of suppression of ...coherent terms, the VMI in circularly birefringent fibers is governed by one set of coupled-mode nonlinear Schrödinger equations regardless of the fiber birefringence. In consequence, the VMI sidebands are polarized linearly and orthogonally to the pump up to the birefringence level of 10 -5 , similarly like in isotropic fibers. For greater birefringence the polarization states of the sidebands become elliptical with opposite handedness while the azimuth angle deviates from orthogonality to the pump. We also point on the dependence of the critical power beyond which the VMI cannot exist upon ellipticity angle θ of the eigenmodes. We show that the critical power gradually increases with the ellipticity angle and for θ > 17.6° the VMI gain is not limited, in contrast to linearly birefringent fibers. Our findings were confirmed experimentally by observation of the isotropic-like VMI in the spun side-hole fiber with nearly circularly polarized eigenmodes, in spite of relatively high birefringence of the order of 2 × 10 -6 .
In this study, we show that transformation optics formalism can be used to rigorously model a wide range of twisted anisotropic fibers, which could only be analyzed using perturbative methods. If the ...material anisotropy of fibers has an intrinsic origin or is induced by axially or helically symmetric physical factors, then they can be transformed into a form usable in rigorous two-dimensional (2D) modeling. We demonstrate the effectiveness of the proposed approach in 2D modeling of the propagation characteristics of first-order eigenmodes in twisted and spun fibers with high linear birefringence. We derive the equivalent electric permittivity tensors for such fibers in the helical coordinate system and study the evolution of the first-order modes toward vortex modes with increasing twist rate. The obtained results confirm that the proposed method can reveal phenomena that cannot be predicted by analytical approaches.
We study the effect of the core ellipticity and core-induced thermal stress on the conversion of LP11 modes to vortex modes in gradually twisted highly birefringent PANDA fibers using an improved ...perturbation-based modeling method. We show that these two technologically unavoidable factors have a significant impact on the conversion process, which manifests itself in shortening the conversion length, altering the assignment between the input LP
modes and output vortex modes, and modifying the vortex mode structure. In particular, we demonstrate that for certain fiber geometries, it is possible to obtain output vortex modes with parallel and antiparallel spins and orbital angular momenta. The simulation results obtained using the modified method are in good agreement with recently published experimental data. Furthermore, the proposed method provides reliable guidelines for choosing fiber parameters that ensure a short conversion length and the desired polarization structure of the output vortex modes.
We present a new method for the efficient modeling of the conversion of LP modes to vortex modes in gradually twisted highly birefringent fibers, employing the coupled-mode approach in helicoidal ...coordinates. The method is applicable to a class of highly birefringent fibers with cylindrical cores and stress-applying elements. We analyzed the effects of refractive index contrast, birefringence, and twist rate profile on the quality of the converted vortex beams, including the intensity and polarization distributions, as well as on the crosstalk between different eigenmodes at the output of the twisted fibers. The obtained results prove the possibility of a broadband quasi-adiabatic generation of vortex beams of high purity in gradually twisted highly birefringent fibers a few centimeters long and provide hints for optimization of the conversion process.
PDF
