Mixed D-vine copula-based conditional quantile model for stochastic monthly streamflow simulation

Copula functions have been widely used in stochastic simulation and prediction of streamflow.However,existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate block for all months.To address this limitation,this study developed a mixed D-vine copula-based conditional quantile model that can capture temporal correlations.This model can generate streamflow by selecting different historical streamflow variables as the conditions for different months and by exploiting the conditional quantile functions of streamflows in different months with mixed D-vine copulas.The up-to-down sequential method,which couples the maximum weight approach with the Akaike information criteria and the maximum likelihood approach,was used to determine the structures of multivariate Dvine copulas.The developed model was used in a case study to synthesize the monthly streamflow at the Tangnaihai hydrological station,the inflow control station of the Longyangxia Reservoir in the Yellow River Basin.The results showed that the developed model outperformed the commonly used bivariate copula model in terms of the performance in simulating the seasonality and interannual variability of streamflow.This model provides useful information for water-related natural hazard risk assessment and integrated water resources management and utilization.

Analysis of the dynamic characteristics and stochastic simulation on variations of beach volumes

This paper analyzes the dynamic characteristics of the variations of the beach volumes for three level zonesof the Yanjing Beach in the Shuidong Bay of the western Guangdong Province by using the methods of dynamic systemanalysis and the multi-dimensional spectral estimation. The results show that the variations of the beach volume arecharaCterized by the multiband oscillations with a dominant semimonth period. Upwards the low tide level, the beachtends to be stable. The estimates of the partial coherences and the partial phases indicate that the variations of thebeach volumes are mainly the results of the direct actions of the waves which are influenced by the tidal level changesand driven by the wind stress. The simulation results of the beach volume series for different beach heart zones bythreshold mixed regressive models indicate that the influence of the tide on the variations of the beach volumes is weakened and the direct actions of the wave energy and the wind stress are apparently enhanced with the increase of thebeach height.(This project was supported by the National Natural Science Foundation of China.)

Controlling the uncertainty in reservoir stochastic simulation

Unexpected noise in reservoir stochastic simulation realization may be too high to make the realization useful, especially when there is a lack of hard data. Through discussing the uncertainties, we present two ways to control the uncertainty ratio that is brought by the algorithm of stochastic simulation. By reasonably reducing the random value of the stochastic simulation result, the unexpected values introduced by the residual that associates with random series can be controlled. Another way when the data disperse unevenly is to control the stochastic simulation order by grouping the points that need to be simulated to make those points which can be simulated by more neighborhood hard data calculated first. Both methods do not go against the core stochastic simulation algorithm.

Efficient Stochastic Simulation Algorithm for Chemically Reacting Systems Based on Support Vector Regression

The stochastic simulation algorithm （SSA） accurately depicts spatially homogeneous wellstirred chemically reacting systems with small populations of chemical species and properly represents noise, but it is often abandoned when modeling larger systems because of its computational complexity. In this work, a twin support vector regression based stochastic simulations algorithm （TS^3A） is proposed by combining the twin support vector regression and SSA, the former is a well-known robust regression method in machine learning. Numerical results indicate that this proposed algorithm can be applied to a wide range of chemically reacting systems and obtain significant improvements on efficiency and accuracy with fewer simulating runs over the existing methods.

L-leap:accelerating the stochastic simulation of chemically reacting systems

Presented here is an L-leap method for accelerating stochastic simulation of well-stirred chemically reacting systems, in which the number of reactions occurring in a reaction channel with the largest propensity function is calculated from the leap condition and the number of reactions occurring in the other reaction channels are generated by using binomial random variables during a leap. The L-leap method can better satisfy the leap condition. Numerical simulation results indicate that the L-leap method can obtain better performance than established methods.

Stochastic simulation of growth curves of Acidithiobacillus ferrooxidans

To reveal the low growth rate of Acidithiobacillus ferrooxidans, a stochastic growth model was proposed to analyze growth curves of these bacteria in a batch culture. An algorithm was applied to simulate the bacteria population during lag and exponential phase. The results show that the model moderately fits the experimental data. Further, the mean growth constant （K） of growth curves is obtained by fitting the logarithm of the simulating population data versus the generation numbers with the different initial population number （N0） and initial mean activity of population （A0）. When No is 300 and 700 respectively, the discrepancy of K value is only 0.91%, however, A0 is 0.34 and 0.38 respectively, the discrepancy of K value is 19.53%. It suggests that the effect of A0 on the lag phase exceeds No, though both parameters could shorten the lag phase by increasing their values.

“Final all possible steps”approach for accelerating stochastic simulation of coupled chemical reactions

In this paper, we develop a modified accelerated stochastic simulation method for chemically reacting systems, called the ＂final all possible steps＂ （FAPS） method, which obtains the reliable statistics of all species in any time during the time course with fewer simulation times. Moreover, the FAPS method can be incorporated into the leap methods, which makes the simulation of larger systems more efficient. Numerical results indicate that the proposed methods can be applied to a wide range of chemically reacting systems with a high-precision level and obtain a significant improvement on efficiency over the existing methods.

Ground-motion simulation for the MW6.1 Ludian earthquake on 3 August 2014 using the stochastic finite-fault method

The stochastic finite-fault simulation method was applied to synthesize the horizontal ground acceleration seismograms produced by the MW6.1 Ludian earthquake on August 3,2014.For this purpose,we produced first a total of 200 kinematic source models for the Ludian event,which are characterized by the heterogeneous slip on the conjugated ruptured fault and the slip-dependent spreading of the rupture front.The results indicated that the heterogeneous slip and the spatial extent of the ruptured fault play dominant roles in the spatial distribution of ground motions in the near-fault area.The peak ground accelerations(PGAs)and 5%-damped pseudospectral accelerations(PSAs)at periods shorter than 0.5 s estimated on the resulting synthetics generally match well with the observations at stations with Joyner-Boore distances(RJB)greater than 20 km.The synthetic PGVs and PSAs at periods of 0.5 s and 0.75 s are in good agreement with predicted medians by the Yu14 model(Yu et al.,2014).However,the synthetic results are generally much lower than the predicted medians by BSSA14 model(Boore et al.,2014).Moreover,the ground motion variability caused by the randomness in the source rupture process was evaluated by these synthetics.The standard deviations of PSAs on the base-10 logarithmic scale,Sigma[log10(PSA)],are closely dependent on either the spectral period or the RJB.The Sigma[log10(PSA)]remains a constant approximately 0.55 at periods shorter than 0.1 s,and then increase continuously up to^0.13 as the period increases from 0.1 to 2.0 s.The Sigma[log10(PSA)]values at periods of 0.1‒2.0 s show the downward tendency as the RJB values increase.However,the Sigma[log10(PSA)]​values at periods shorter than 0.1 s decrease as the RJB values increase up to^50 km,and then increase with the increasing RJB.Furthermore,we found that the ground-motion variability shows the significant dependence on the azimuth.

A Modified Inhomogeneous Stochastic Simulation Algorithm to Model Reactive Boundary Conditions

The present study proposes a stochastic simulation scheme to model reactive boundaries through a position jump process which can be readily implemented into the Inhomogeneous Stochastic Simulation Algorithm by modifying the propensity of the diffusive jump over the reactive boundary. As compared to the literature, the present approach does not require any correction factors for the propensity. Also, the current expression relaxes the constraint on the compartment size allowing the problem to be solved with a coarser grid and therefore saves considerable computational cost. The modified algorithm is then applied to simulate three reaction-diffusion systems with reactive boundaries.

Stochastic Simulation and Analysis of Geological Corrosion Defects in Dam Foundation

Uncertainty in geological structural modeling, especially geological corrosion(a kind of karst cave), is a bottleneck that restricts the development and application of geological computer modeling and effect estimation. To solve this issue, a stochastic modeling method based on the random field theory is proposed in comparison with the deterministic geometric modeling method. Then the constraint random field modeling method and the random field modeling method without constrained parameters are compared and analyzed. A case study shows that the novel stochastic simulation method is an effective tool to describe the distribution characteristics of corrosion parameters and reflect the updated geological prospecting information. The influence of geological corrosion on the dam behavior can also be better analyzed by using the stochastic simulation method. At the same time, the unconfined random field ignores the sample location information and may lead to higher variability. Therefore, the constraint random field modeling method can provide a useful reference for the numerical analysis under complex geological conditions.

Stochastic Simulation of Emission Spectra and Classical Photon Statistics of Quantum Dot Superluminescent Diodes

We present a stochastic procedure to investigate the correlation spectra of quantum dot superluminescent diodes. The classical electric field of a diode is formed by a polychromatic superposition of many independent stochastic oscillators. Assuming fields with individual carrier frequencies, Lorentzian linewidths and amplitudes we can form any relevant experimental spectrum using a least square fit. This is illustrated for Gaussian and Lorentzian spectra, Voigt profiles and box shapes. Eventually, the procedure is applied to an experimental spectrum of a quantum dot superluminescent diode which determines the first- and second-order temporal correlation functions of the emission. We find good agreement with the experimental data and a quantized treatment. Thus, a superposition of independent stochastic oscillators represents the first- and second-order correlation properties of broadband light emitted by quantum dot superluminescent diodes.

Monte-Carlo simulation of a stochastic differential equation

For solving higher dimensional diffusion equations with an inhomogeneous diffusion coefficient,Monte Carlo（MC） techniques are considered to be more effective than other algorithms, such as finite element method or finite difference method. The inhomogeneity of diffusion coefficient strongly limits the use of different numerical techniques. For better convergence, methods with higher orders have been kept forward to allow MC codes with large step size. The main focus of this work is to look for operators that can produce converging results for large step sizes. As a first step, our comparative analysis has been applied to a general stochastic problem.Subsequently, our formulization is applied to the problem of pitch angle scattering resulting from Coulomb collisions of charge particles in the toroidal devices.

Scheduling an Energy-Aware Parallel Machine System with Deteriorating and Learning Effects Considering Multiple Optimization Objectives and Stochastic Processing Time

Currently,energy conservation draws wide attention in industrial manufacturing systems.In recent years,many studies have aimed at saving energy consumption in the process of manufacturing and scheduling is regarded as an effective approach.This paper puts forwards a multi-objective stochastic parallel machine scheduling problem with the consideration of deteriorating and learning effects.In it,the real processing time of jobs is calculated by using their processing speed and normal processing time.To describe this problem in a mathematical way,amultiobjective stochastic programming model aiming at realizing makespan and energy consumption minimization is formulated.Furthermore,we develop a multi-objective multi-verse optimization combined with a stochastic simulation method to deal with it.In this approach,the multi-verse optimization is adopted to find favorable solutions from the huge solution domain,while the stochastic simulation method is employed to assess them.By conducting comparison experiments on test problems,it can be verified that the developed approach has better performance in coping with the considered problem,compared to two classic multi-objective evolutionary algorithms.

StochSD: A Full Potential CSS Language for Dynamic and Stochastic Modelling, Simulation and Statistical Analysis

It is vital that a well-defined conceptual model can be realized by a macro-model (e.g., a Continuous System Simulation (CSS) model) or a micro-model (e.g., an Agent-Based model or Discrete Event Simulation model) and still produce mutually consistent results. The Full Potential CSS concept provides the rules so that the results from macro-modelling become fully consistent with those from micro-modelling. This paper focuses on the simulation language StochSD (Stochastic System Dynamics), which is an extension of classical Continuous System Simulation that implements the Full Potential CSS concept. Thus, in addition to modelling and simulating continuous flows between compartments represented by “real” numbers, it can also handle transitions of discrete entities by integer numbers, enabling combined models to be constructed in a straight-forward way. However, transition events of discrete entities (e.g., arrivals, accidents, deaths) usually happen irregularly over time, so stochasticity often plays a crucial role in their modelling. Therefore, StochSD contains powerful random functions to model uncertainties of different kinds, together with devices to collect statistics during a simulation or from multiple replications of the same stochastic model. Also, tools for sensitivity analysis, optimisation and statistical analysis are included. In particular, StochSD includes features for stochastic modelling, post-analysis of multiple simulations, and presentation of the results in statistical form. In addition to making StochSD a Full Potential CSS language, a second purpose is to provide an open-source package intended for small and middle-sized models in education, self-studies and research. To make StochSD and its philosophy easy to comprehend and use, it is based on the System Dynamics approach, where a system is described in terms of stocks and flows. StochSD is available for Windows, macOS and Linux. On the StochSD homepage, there is extensive material for a course in Modelling and Simulation in form of PowerPoint lectures and laboratory exercises.

Stochastic optimization of mine production scheduling with uncertain ore/metal/waste supply

Optimization of long-term mine production scheduling in open pit mines deals with the management of cash flows, typically in the order of hundreds of millions of dollars. Conventional mine scheduling utilizes optimization methods that are not capable of accounting for inherent technical uncertainties such as uncertainty in the expected ore/metal supply from the underground, acknowledged to be the most critical factor. To integrate ore/metal uncertainty into the optimization of mine production scheduling a stochastic integer programming(SIP) formulation is tested at a copper deposit. The stochastic solution maximizes the economic value of a project and minimizes deviations from production targets in the presence of ore/metal uncertainty. Unlike the conventional approach, the SIP model accounts and manages risk in ore supply, leading to a mine production schedule with a 29% higher net present value than the schedule obtained from the conventional, industry-standard optimization approach, thus contributing to improving the management and sustainable utilization of mineral resources.

An Improved, Downscaled, Fine Model for Simulation of Daily Weather States

In this study,changes in daily weather states were treated as a complex Markov chain process,based on a continuous-time watershed model（soil water assessment tool,SWAT） developed by the Agricultural Research Service at the U.S.Department of Agriculture（USDA-ARS）.A finer classification using total cloud amount for dry states was adopted,and dry days were classified into three states：clear,cloudy,and overcast（rain free）.Multistate transition models for dry-and wet-day series were constructed to comprehensively downscale the simulation of regional daily climatic states.The results show that the finer,improved,downscaled model overcame the oversimplified treatment of a two-weather state model and is free of the shortcomings of a multistate model that neglects finer classification of dry days（i.e.,finer classification was applied only to wet days）.As a result,overall simulation of weather states based on the SWAT greatly improved,and the improvement in simulating daily temperature and radiation was especially significant.

A Stochastic SVIR Model for Measles

In this article, we consider the construction of a SVIR (Susceptible, Vaccinated, Infected, Recovered) stochastic compartmental model of measles. We prove that the deterministic solution is asymptotically the average of the stochastic solution in the case of small population size. The choice of this model takes into account the random fluctuations inherent to the epidemiological characteristics of rural populations of Niger, notably a high prevalence of measles in children under 5, coupled with a very low immunization coverage.

Reducing Stochastic Discrete Models of Biochemical Networks

Biochemical systems have numerous practical applications, in particular to the study of critical intracellular processes. Frequently, biochemical kinetic models depict cellular processes as systems of chemical reactions. Many biological processes in a cell are inherently stochastic, due to the existence of some low molecular amounts. These stochastic fluctuations may have a great effect on the biochemical system’s behaviour. In such cases, stochastic models are necessary to accurately describe the system’s dynamics. Biochemical systems at the cellular level may entail many species or reactions and their mathematical models may be non-linear and with multiple scales in time. In this work, we provide a numerical technique for simplifying stochastic discrete models of well-stirred biochemical systems, which ensures that the main properties of the original system are preserved. The proposed technique employs sensitivity analysis and requires solving an optimization problem. The numerical tests on several models of practical interest show that our model reduction strategy performs very well.

Approximations of Quasi-Stationary Distributions of the Stochastic <i>SVIR</i>Model for the Measles

In this paper, we analyze the quasi-stationary distribution of the stochastic SVIR (Susceptible, Vaccinated, Infected, Recovered) model for the measles. The quasi-stationary distributions, as discussed by Danoch and Seneta, have been used in biology to describe the steady state behaviour of population models which exhibit discernible stationarity before to become extinct. The stochastic SVIR model is a stochastic SIR (Susceptible, Infected, Recovered) model with vaccination and recruitment where the disease-free equilibrium is reached, regardless of the magnitude of the basic reproduction number. But the mean time until the absorption (the disease-free) can be very long. If we assume the effective reproduction number Rp < 1 or , the quasi-stationary distribution can be closely approximated by geometric distribution. β and δ stands respectively, for the disease transmission coefficient and the natural rate.

On the direct searches for non-smooth stochastic optimization problems

Many difficult engineering problems cannot be solved by the conventional optimization techniques in practice. Direct searches that need no recourse to explicit derivatives are revived and become popular since the new century. In order to get a deep insight into this field, some notes on the direct searches for non-smooth optimization problems are made. The global convergence vs. local convergence and their influences on expected solutions for simulation-based stochastic optimization are pointed out. The sufficient and simple decrease criteria for step acceptance are analyzed, and why simple decrease is enough for globalization in direct searches is identified. The reason to introduce the positive spanning set and its usage in direct searches is explained. Other topics such as the generalization of direct searches to bound, linear and non-linear constraints are also briefly discussed.