Recent Papers on Physically Based Simulation Modelling
Content Table
- Cellular-automata-based ecological and ecohydraulics modelling
- A novel application of a multi-objective evolutionary algorithm in open channel flow modelling
- Advances in data-driven analyses and modelling using EPR-MOGA
- Data-driven modelling: some past experiences and new approaches
- Data-driven modelling: some past experiences and new approaches
- Numerical analysis of coupled hydrosystems based on an object-oriented compartment approach
- Evaluation of spatially variable control parameters in a complex catchment modelling system: a genetic algorithm application
- A Bayesian estimation of parameter-induced uncertainty in a nearshore alongshore current model
- Calibration of nearshore process modelsATitlemdash;application of a hybrid genetic algorithm
- Optimal sampling in a noisy genetic algorithm for risk-based remediation design
Cellular-automata-based ecological and ecohydraulics modelling
Journal of Hydroinformatics Vol 11 No 3–4 pp 252–265 © IWA Publishing 2009 doi:10.2166/hydro.2009.026
Qiuwen Chen, Fei Ye and Weifeng Li
Research Centre for Eco-Environmental Sciences, Chinese Academy of Science, Shuangqing Road 18, Haidian District, Beijing 100085, China Tel.:/Fax: +86 10 6284 9311 E-mail: li.wf@rcees.ac.cn
Abstract
Spatially lumped models may fail to take into account the effects of spatial heterogeneity and local interactions. These properties sometimes are crucial to the dynamics and evolutions of ecosystems. This paper started from the fundamental aspects of CA and focused on the development and application of the approach to ecological and ecohydraulics modelling. Application cases include modelling of prey–predator dynamics by stochastic CA and simulation of riparian vegetation successions in a regulated river by rule-based CA. The results indicated that spatially explicit paradigms such as cellular automata (CA) have a strong capability to bridge the local processes and global patterns.
A novel application of a multi-objective evolutionary algorithm in open channel flow modelling
Journal of Hydroinformatics Vol 11 No 1 pp 31–50 © IWA Publishing 2009 doi:10.2166/hydro.2009.033
S. Sharifi, M. Sterling and D. W. Knight
Department of Civil Engineering, University of Birmingham, Birmingham, B15 2TT, UK Tel.: +44 121 414 5152 E-mail: sxs650@bham.ac.uk
Abstract
The Shiono and Knight method (SKM) is a simple depth-averaged flow model, based on the RANS equations which can be used to estimate the lateral distributions of depth-averaged velocity and boundary shear stress for flows in straight prismatic channels with the minimum of computational effort. However, in order to apply the SKM, detailed knowledge relating to the lateral variation of the friction factor (f), dimensionless eddy viscosity (l) and a sink term representing the effects of secondary flow (G) are required. In this paper a multi-objective evolutionary algorithm is used to study the lateral variation and value of these parameters for simple trapezoidal channels over a wide range of aspect ratios through the model calibration process. Based on the available experimental data, four objectives are selected and the NSGA-II algorithm is applied to several datasets. The best answer for each set is then selected based on a proposed methodology. Rules relating f, l and G to the wetted parameter ratio (Pb/Pw) for a variety of situations have been developed which provide practical guidance for the engineer on choosing the appropriate parameters in the SKM model.
Advances in data-driven analyses and modelling using EPR-MOGA
Journal of Hydroinformatics Vol 11 No 3–4 pp 225–236 © IWA Publishing 2009 doi:10.2166/hydro.2009.017
O. Giustolisi and D. A. Savic
Department of Civil and Environmental Engineering, Technical University of Bari, Engineering Faculty of Taranto, via Turismo n. 8, Taranto 74100, Italy E-mail: o.giustolisi@poliba.it
Centre for Water Systems, School of Engineering, Computer Science and Mathematics, University of Exeter, Harrison Building, North Park Road, Exeter EX4 4QF, UK
Abstract
Evolutionary Polynomial Regression (EPR) is a recently developed hybrid regression method that combines the best features of conventional numerical regression techniques with the genetic programming/symbolic regression technique. The original version of EPR works with formulae based on true or pseudo-polynomial expressions using a single-objective genetic algorithm. Therefore, to obtain a set of formulae with a variable number of pseudo-polynomial coefficients, the sequential search is performed in the formulae space. This article presents an improved EPR strategy that uses a multi-objective genetic algorithm instead. We demonstrate that multi-objective approach is a more feasible instrument for data analysis and model selection. Moreover, we show that EPR can also allow for simple uncertainty analysis (since it returns polynomial structures that are linear with respect to the estimated coefficients). The methodology is tested and the results are reported in a case study relating groundwater level predictions to total monthly rainfall.
Data-driven modelling: some past experiences and new approaches
Journal of Hydroinformatics Vol 10 No 1 pp 3–22 © IWA Publishing 2008 doi:10.2166/hydro.2008.015
Dimitri P. Solomatine and Avi Ostfeld
UNESCO-IHE Institute for Water Education, PO Box 3015, 2601 DA, Delft, The Netherlands d.solomatine@unesco-ihe.org
Faculty of Civil and Environmental Engineering, Technion-Israel Institute of Technology, Haifa, 32000, Israel
Abstract
Physically based (process) models based on mathematical descriptions of water motion are widely used in river basin management. During the last decade the so-called data-driven models are becoming more and more common. These models rely upon the methods of computational intelligence and machine learning, and thus assume the presence of a considerable amount of data describing the modelled system's physics (i.e. hydraulic and/or hydrologic phenomena). This paper is a preface to the special issue on Data Driven Modelling and Evolutionary Optimization for River Basin Management, and presents a brief overview of the most popular techniques and some of the experiences of the authors in data-driven modelling relevant to river basin management. It also identifies the current trends and common pitfalls, provides some examples of successful applications and mentions the research challenges.
Data-driven modelling: some past experiences and new approaches
Journal of Hydroinformatics Vol 10 No 1 pp 3–22 © IWA Publishing 2008 doi:10.2166/hydro.2008.015
Dimitri P. Solomatine and Avi Ostfeld
UNESCO-IHE Institute for Water Education, PO Box 3015, 2601 DA, Delft, The Netherlands d.solomatine@unesco-ihe.org
Faculty of Civil and Environmental Engineering, Technion-Israel Institute of Technology, Haifa, 32000, Israel
Abstract
Physically based (process) models based on mathematical descriptions of water motion are widely used in river basin management. During the last decade the so-called data-driven models are becoming more and more common. These models rely upon the methods of computational intelligence and machine learning, and thus assume the presence of a considerable amount of data describing the modelled system's physics (i.e. hydraulic and/or hydrologic phenomena). This paper is a preface to the special issue on Data Driven Modelling and Evolutionary Optimization for River Basin Management, and presents a brief overview of the most popular techniques and some of the experiences of the authors in data-driven modelling relevant to river basin management. It also identifies the current trends and common pitfalls, provides some examples of successful applications and mentions the research challenges.
Numerical analysis of coupled hydrosystems based on an object-oriented compartment approach
Journal of Hydroinformatics Vol 10 No 3 pp 227–244 © IWA Publishing 2008 doi:10.2166/hydro.2008.003
Olaf Kolditz, Jens-Olaf Delfs, Claudius Bürger, Martin Beinhorn and Chan-Hee Park
Geohydrology and Hydroinformatics, Center for Applied Geoscience, University of Tübingen, Tübingen, D-72076, Germany
and Environmental Informatics, Helmholtz Center for Environmental Research - UFZ, 04318, Leipzig, Germany E-mail: olaf.kolditz@ufz.de
Geohydrology and Hydroinformatics, Center for Applied Geoscience, University of Tübingen, Tübingen, D-72076, Germany
Environmental Informatics Helmholtz, Center for Environmental Research - UFZ, 04318, Leipzig, Germany
Abstract
In this paper we present an object-oriented concept for numerical simulation of multi-field problems for coupled hydrosystem analysis. Individual (flow) processes modelled by a particular partial differential equation, i.e. overland flow by the shallow water equation, variably saturated flow by the Richards equation and saturated flow by the groundwater flow equation, are identified with their corresponding hydrologic compartments such as land surface, vadose zone and aquifers, respectively. The object-oriented framework of the compartment approach allows an uncomplicated coupling of these existing flow models. After a brief outline of the underlying mathematical models we focus on the numerical modelling and coupling of overland flow, variably saturated and groundwater flows via exchange flux terms. As each process object is associated with its own spatial discretisation mesh, temporal time-stepping scheme and appropriate numerical solution procedure. Flow processes in hydrosystems are coupled via their compartment (or process domain) boundaries without giving up the computational necessities and optimisations for the numerical solution of each individual process. However, the coupling requires a bridging of different temporal and spatial scales, which is solved here by the integration of fluxes (spatially and temporally). In closing we present three application examples: a benchmark test for overland flow on an infiltrating surface and two case studies – at the Borden site in Canada and the Beerze–Reusel drainage basin in the Netherlands.
Evaluation of spatially variable control parameters in a complex catchment modelling system: a genetic algorithm application
Journal of Hydroinformatics Vol 9 No 3 pp 163–173 © IWA Publishing 2007 doi:10.2166/hydro.2007.026
Tianjun Fang and James E. Ball
Water Research Laboratory, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
Faculty of Engineering, University of Technology Sydney, Broadway, NSW 2007, Australia james.ball@uts.edu.au
Abstract
Successful implementation of a catchment modelling system requires careful consideration of the system calibration which involves evaluation of many spatially and temporally variable control parameters. Evaluation of spatially variable control parameters has been an issue of increasing concern arising from an increased awareness of the inappropriateness of assuming catchment averaged values. Presented herein is the application of a real-value coding genetic algorithm (GA) for evaluation of spatially variable control parameters for implementation with the Storm Water Management Model (SWMM). It was found that a real-value coding GA using multiple storms calibration was a robust search technique that was capable of identifying the most promising range of values for spatially variable control parameters. As the selection of appropriate GA operators is an important aspect of the GA efficiency, a comprehensive investigation of the GA operators in a high-dimensional search space was conducted. It was found that a uniform crossover operation was superior to both one-point and two-point crossover operations over the whole range of crossover probabilities, and the optimal uniform crossover and mutation probabilities for the complex system considered were in the range of 0.75–0.90 and 0.01–0.1, respectively.
A Bayesian estimation of parameter-induced uncertainty in a nearshore alongshore current model
Journal of Hydroinformatics 7 (2006) 37-49
B. G. Ruessink
Institute for Marine and Atmospheric Research, Department of Physical Geography, Faculty of Geosciences, Utrecht University, PO Box 80.115, 3508 TC, Utrecht, The Netherlands, Tel: +31 30 253 2405, Fax: +31 30 253 1145, g.ruessink@geo.uu.nl
Abstract
In many process-based models, parameters have to be estimated from data. It is important to obtain not only the optimum value of the parameters, but also to assess the uncertainty in the parameters and, hence, in the models' output. In this paper, the Bayesian Monte Carlo technique known as Generalised Likelihood Uncertainty Estimation (GLUE) is used to evaluate the parameter-induced predictive uncertainty of a three-parameter model that predicts alongshore currents over a nearshore barred profile. GLUE performs a fully random sampling of feasible-parameters space, assigning non-zero likelihoods to those model simulations that outperform a user-defined threshold. Based on data gathered at six cross-shore position across an inner bar at Egmond aan Zee, The Netherlands, non-zero likelihoods were found for a rather wide range of parameter values, largely induced by an interdependence between two parameters that affect the width of current jets across the bar. The width of the 95% uncertainty interval was found empirically to increase linearly with the predicted magnitude of the alongshore current, from about 0.02–0.06 m/s when the current magnitude is near zero to about 0.2 m/s when it is near its maximum of about 1.1 m/s. These widths are approximately equal to a rough estimate of the errors in the data. In many cases the 95% uncertainty interval brackets the observations, although there are also various instances where this is not the case and apparently model structural errors dominate over parameter-induced errors. Model non-linearity and parameter interdependence cause the marginal parameter posterior distributions to differ remarkably from those obtained from traditional first-order approximations.
Calibration of nearshore process modelsATitlemdash;application of a hybrid genetic algorithm
Journal of Hydroinformatics 7 (2005) 135-149
B. G. Ruessink
p>Department of Physical Geography, Faculty of Geosciences, Institute for Marine and Atmospheric Research Utrecht, Utrecht University,PO Box 80.115, 3508 TC Utrecht, The Netherlands, Tel: +31 30 2532405, Fax: +31 30 2531145, E-mail: g.ruessink@geog.uu.nl
Abstract
The physically realistic functions implemented in nearshore process models are governed by parameters that usually do not represent measurable attributes of the nearshore and, therefore, need to be determined through calibration. The classical approach to calibrate nearshore process models is via manual parameter adjustments and visual comparisons of model predictions and measurements. In this paper a hybrid genetic algorithm, comprising a global population-evolution-based search strategy and a local Nelder–Mead simplex search, is used to calibrate nearshore process models in an objective and automatic manner. The effectiveness of the algorithm to find the optimum parameter setting are examined in two case studies with increasing complexity: a simple alongshore current model and a more complex cross-shore bed evolution model. Whereas the algorithm is found to be an effective method to find the optimum setting of the alongshore current model, it fails to identify the optimum parameter values in the bed evolution model, related to the strong interaction between two of the parameters in the suspended sediment transport equation. Setting one of the interdependent parameters to a constant value within its feasible space while retaining the other in the optimization procedure is found to be a feasible solution to the ill-posed optimization problem of the bed evolution model.
Optimal sampling in a noisy genetic algorithm for risk-based remediation design
Journal of Hydroinformatics 5 (2003) 11-25
Gayathri Gopalakrishnan, Barbara S.Minsker and David E.Goldberg
Civil and Environmental Engineering, University of Illinois, 3230 NCEL, MC-250, 205 N Mathews Avenue, Urbana, IL 61801, USA *Now at Geomatrix Consultants Inc., Minneapolis, MN, USA
Civil and Environmental Engineering, University of Illinois, 3230 NCEL, MC-250, 205 N Mathews Avenue, Urbana, IL 61801, USA *Now at Geomatrix Consultants Inc., Minneapolis, MN, USA
General Engineering, University of Illinois, 117 Transportation, MC-238, 104 S Mathews Avenue, Urbana, IL 61801, USA
Abstract
A groundwater management model has been developed that predicts human health risks and uses a noisy genetic algorithm to identify promising risk-based corrective action (RBCA) designs. Noisy genetic algorithms are simple genetic algorithms that operate in noisy environments. The noisy genetic algorithm uses a type of noisy fitness function (objective function) called the sampling fitness function, which utilises Monte-Carlo-type sampling to find robust designs. Unlike Monte Carlo simulation modelling, however, the noisy genetic algorithm is highly efficient and can identify robust designs with only a few samples per design. For hydroinformatic problems with complex fitness functions, however, it is important that the sampling be as efficient as possible. In this paper, methods for identifying efficient sampling strategies are investigated and their performance evaluated using a case study of a RBCA design problem. Guidelines for setting the parameter values used in these methods are also developed. Applying these guidelines to the case study resulted in highly efficient sampling strategies that found RBCA designs with 98% reliability using as few as 4 samples per design. Moreover, these designs were identified with fewer simulation runs than would likely be required to identify designs using trial-and-error Monte Carlo simulation. These findings show considerable promise for applying these methods to complex hydroinformatic problems where substantial uncertainty exists but extensive sampling cannot feasibly be done.
