Download or read book Efficient Multi objective Surrogate Optimization of Computationally Expensive Models with Application to Watershed Model Calibration written by Taimoor Akhtar and published by . This book was released on 2015 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis introduces efficient algorithms for multi-objective optimization of computationally expensive simulation optimization problems. Implementation of efficient algorithms and their advantage of use for calibration of complex and deterministic watershed simulation models is also analyzed. GOMORS, a novel parallel multi-objective optimization algorithm involving surrogate modeling via Radial Basis Function approximation, is introduced in Chapter 2. GOMORS is an iterative search algorithm where a multiobjective search utilizing evolution, local search, multi method search and non-dominated sorting is done on the surrogate function to select numerous points for simultaneous expensive evaluations in each algorithm iteration. A novel procedure, "multi-rule selection", is introduced that simultaneously selects evaluation points (which can be computed in parallel) within an algorithm iteration through different metrics. Results are compared against ParEGO and the widely used NSGA-II on numerous test problems including a hypothetical groundwater PDE problem. The results indicate that GOMORS outperforms ParEGO and NSGA-II within a budget of 400 function evaluations. The superiority of performance of GOMORS is more evident for problems involving a large number of decision variables (15-25 decision variables). The second contribution (Chapter 3) to the thesis is a comparative analysis of algorithms for multi-objective calibration of complex watershed models. Since complex watershed models can be computationally expensive, we analyze and compare performance of various algorithms within a limited evaluation budget of 1000 evaluations. The primary aim of the analysis is to assess effectiveness of algorithms in identifying "meaningful trade-offs" for multi-objective watershed model calibration problems within a limited evaluation budget. A new metric, referred as the Distributed Cardinality index, is introduced for quantifying the relative effectiveness of different algorithms in identifying "meaningful tradeoffs". Our results indicate that GOMORS (the algorithm introduced in Chapter 2), outperforms various other algorithms, including ParEGO and AMALGAM, in computing good and meaningful trade-off solutions, within a limited simulation evaluation budget. The third and final contribution (see Chapter 4) to the thesis is MOPLS, a Multi-Objective Parallel Local Stochastic Search algorithm for efficient optimization of computationally expensive problems. MOPLS is an iterative algorithm which incorporates simultaneous local candidate search on response surface models within a synchronous parallel framework to select numerous evaluation points in each iteration. MOPLS was applied to various test problems and multi-objective watershed calibration problems with 4, 8 and 16 synchronous parallel processes and results were compared against GOMORS, ParEGO and AMALGAM. The results indicate that within a limited evaluation budget, MOPLS outperforms ParEGO and AMALGAM for computationally expensive watershed calibration problems, when comparison is made in function evaluations. When parallel speedup is taken into consideration and comparison is made in wall clock time, the results indicate that overall performance of MOPLS is better than GOMORS, ParEGO and AMALGAM.

Download or read book Applications of Multi objective Mixed integer and Hybrid Global Optimization Algorithms for Computationally Expensive Groundwater Problems written by Ying Wan and published by . This book was released on 2015 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research focuses on the development and implementation of e cient optimization algorithms that can solve a range of computationally expensive groundwater simulationoptimization problems. Because groundwater model evaluations are expensive, it is important to find accurate solutions with relatively few function evaluations. As a result, all the algorithms tested in this research are evaluated on a limited computation budget. The first contribution to the thesis is a comparative evaluation of a novel multi-objective optimization algorithm, GOMORS, to three other popular multi-objective optimization methods on applications to groundwater management problems within a limited number of objective function evaluations. GOMORS involves surrogate modeling via Radial Basis Function approximation and evolutionary strategies. The primary aim of the analysis is to assess the effectiveness of multi-objective algorithms in groundwater remediation management through multi-objective optimization within a limited evaluation budget. Three sets of dual objectives are evaluated. The objectives include minimization of cost, pollution mass remaining/pollution concentration, and cleanup time. Our results indicate that the overall performance of GOMORS is better than three other algorithms, AMALGAM, BORG and NSGA-II, in identifying good trade-off solutions. Furthermore, GOMORS incorporates modest parallelization to make it even more e cient. The next contribution is application of SO-MI, a surrogate model-based algorithm designed for computationally expensive nonlinear and multimodal mixed-integer black-box optimization problems, to solve groundwater remediation design problems (NL-MIP). SO-MI utilizes surrogate models to guide the search thus save the expensive function evaluation budget, and is able to find accurate solutions with relatively few function evaluations. We present numerical results to show the effectiveness and e ciency of SO-MI in comparison to Genetic Algorithm and NOMAD, which are two popular mixed-integer optimization algorithms. The results indicate that SO-MI is statistically better than GA and NOMAD in both study cases. Chapter 4 describes DYCORS-PEST, a novel method developed for high dimensional, computationally expensive, multimodal calibration problems when the computation budget is limited. This method integrates a local optimizer PEST into a global optimization framework DYCORS. The novelty of DYCORS-PEST is that it uses a memetic approach to improve the accuracy of the solution in which DYCORS selects the point at which the search switches to use of the local method PEST and when it switches back to the global phase. Since PEST is a very e cient and widely used local search algorithm for groundwater model calibration, incorporating PEST into DYCORS-PEST is a good enhancement for PEST and easy for PEST users to learn. DYCORS-PEST achieves the goal of solving the computationally expensive black-box problem by forming a response surface of the expensive function, thus reducing the number of required expensive function evaluations for finding accurate solutions. The key feature of the global search method in DYCORS-PEST is that the number of decision variables being perturbed is dynamically adjusted in each iteration in order to be more effective for higher dimensional problems. Application of DYCORS-PEST to two 28parameter groundwater calibration problems indicate this new method outperforms PEST by a large margin for high dimensional, computationally expensive, groundwater calibration problems.

Download or read book Evaluating and Developing Parameter Optimization and Uncertainty Analysis Methods for a Computationally Intensive Distributed Hydrological Model written by Xuesong Zhang and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This study focuses on developing and evaluating efficient and effective parameter calibration and uncertainty methods for hydrologic modeling. Five single objective optimization algorithms and six multi-objective optimization algorithms were tested for automatic parameter calibration of the SWAT model. A new multi-objective optimization method (Multi-objective Particle Swarm and Optimization & Genetic Algorithms) that combines the strengths of different optimization algorithms was proposed. Based on the evaluation of the performances of different algorithms on three test cases, the new method consistently performed better than or close to the other algorithms. In order to save efforts of running the computationally intensive SWAT model, support vector machine (SVM) was used as a surrogate to approximate the behavior of SWAT. It was illustrated that combining SVM with Particle Swarm and Optimization can save efforts for parameter calibration of SWAT. Further, SVM was used as a surrogate to implement parameter uncertainty analysis fo SWAT. The results show that SVM helped save more than 50% of runs of the computationally intensive SWAT model The effect of model structure on the uncertainty estimation of streamflow simulation was examined through applying SWAT and Neural Network models. The 95% uncertainty intervals estimated by SWAT only include 20% of the observed data, while Neural Networks include more than 70%. This indicates the model structure is an important source of uncertainty of hydrologic modeling and needs to be evaluated carefully. Further exploitation of the effect of different treatments of the uncertainties of model structures on hydrologic modeling was conducted through applying four types of Bayesian Neural Networks. By considering uncertainty associated with model structure, the Bayesian Neural Networks can provide more reasonable quantification of the uncertainty of streamflow simulation. This study stresses the need for improving understanding and quantifying methods of different uncertainty sources for effective estimation of uncertainty of hydrologic simulation.

Download or read book Calibration of Hydrologic Models Using Distributed Surrogate Model Optimization Techniques written by Mahtab Kamali and published by . This book was released on 2009 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents a new approach to calibration of hydrologic models using distributed computing framework. Distributed hydrologic models are known to be very computationally intensive and difficult to calibrate. To cope with the high computational cost of the process a Surrogate Model Optimization (SMO) technique that is built for distributed computing facilities is proposed. The proposed method along with two analogous SMO methods are employed to calibrate WATCLASS hydrologic model. This model has been developed in University of Waterloo and is now a part of Environment Canada MESH (Environment Canada community environmental modeling system called Modèlisation Environmentale Communautaire (MEC) for Surface Hydrology (SH)) systems. SMO has the advantage of being less sensitive to "curse of dimensionality" and very efficient for large scale and computationally expensive models. In this technique, a mathematical model is constructed based on a small set of simulated data from the original expensive model. SMO technique follows an iterative strategy which in each iteration the surrogate model map the region of optimum more precisely. A new comprehensive method based on a smooth regression model is proposed for calibration of WATCLASS. This method has at least two advantages over the previously proposed methods: a)it does not require a large number of training data, b) it does not have many model parameters and therefore its construction and validation process is not demanding. To evaluate the performance of the proposed SMO method, it has been applied to five well-known test functions and the results are compared to two other analogous SMO methods. Since the performance of all SMOs are promising, two instances of WATCLASS modeling Smoky River watershed are calibrated using these three adopted SMOs and the resultant Nash-Sutcliffe numbers are reported.

Download or read book Developing Parsimonious and Efficient Algorithms for Water Resources Optimization Problems written by Masoud Asadzadeh Esfahani and published by . This book was released on 2012 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the current water resources scientific literature, a wide variety of engineering design problems are solved in a simulation-optimization framework. These problems can have single or multiple objective functions and their decision variables can have discrete or continuous values. The majority of current literature in the field of water resources systems optimization report using heuristic global optimization algorithms, including evolutionary algorithms, with great success. These algorithms have multiple parameters that control their behavior both in terms of computational efficiency and the ability to find near globally optimal solutions. Values of these parameters are generally obtained by trial and error and are case study dependent. On the other hand, water resources simulation-optimization problems often have computationally intensive simulation models that can require seconds to hours for a single simulation. Furthermore, analysts may have limited computational budget to solve these problems, as such, the analyst may not be able to spend some of the computational budget to fine-tune the algorithm settings and parameter values. So, in general, algorithm parsimony in the number of parameters is an important factor in the applicability and performance of optimization algorithms for solving computationally intensive problems. A major contribution of this thesis is the development of a highly efficient, single objective, parsimonious optimization algorithm for solving problems with discrete decision variables. The algorithm is called Hybrid Discrete Dynamically Dimensioned Search, HD-DDS, and is designed based on Dynamically Dimensioned Search (DDS) that was developed by Tolson and Shoemaker (2007) for solving single objective hydrologic model calibration problems with continuous decision variables. The motivation for developing HD-DDS comes from the parsimony and high performance of original version of DDS. Similar to DDS, HD-DDS has a single parameter with a robust default value. HD-DDS is successfully applied to several benchmark water distribution system design problems where decision variables are pipe sizes among the available pipe size options. Results show that HD-DDS exhibits superior performance in specific comparisons to state-of-the-art optimization algorithms. The parsimony and efficiency of the original and discrete versions of DDS and their successful application to single objective water resources optimization problems with discrete and continuous decision variables motivated the development of a multi-objective optimization algorithm based on DDS. This algorithm is called Pareto Archived Dynamically Dimensioned Search (PA-DDS). The algorithm parsimony is a major factor in the design of PA-DDS. PA-DDS has a single parameter from its search engine DDS. In each iteration, PA-DDS selects one archived non-dominated solution and perturbs it to search for new solutions. The solution perturbation scheme of PA-DDS is similar to the original and discrete versions of DDS depending on whether the decision variable is discrete or continuous. So, PA-DDS can handle both types of decision variables. PA-DDS is applied to several benchmark mathematical problems, water distribution system design problems, and water resources model calibration problems with great success. It is shown that hypervolume contribution, HVC1, as defined in Knowles et al. (2003) is the superior selection metric for PA-DDS when solving multi-objective optimization problems with Pareto fronts that have a general (unknown) shape. However, one of the main contributions of this thesis is the development of a selection metric specifically designed for solving multi-objective optimization problems with a known or expected convex Pareto front such as water resources model calibration problems. The selection metric is called convex hull contribution (CHC) and makes the optimization algorithm sample solely from a subset of archived solutions that form the convex approximation of the Pareto front. Although CHC is generally applicable to any stochastic search optimization algorithm, it is applied to PA-DDS for solving six water resources calibration case studies with two or three objective functions.

Download or read book Integrating Surrogate Modeling to Improve DIRECT DE and BA Global Optimization Algorithms for Computationally Intensive Problems written by Abdulbaset Elha Saad and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Rapid advances of computer modeling and simulation tools and computing hardware have turned Model Based Design (MBD) a more viable technology. However, using a computationally intensive, "black-box" form MBD software tool to carry out design optimization leads to a number of key challenges. The non-unimodal objective function and/or non-convex feasible search region of the implicit numerical simulations in the optimization problems are beyond the capability of conventional optimization algorithms. In addition, the computationally intensive simulations used to evaluate the objective and/or constraint functions during the MBD process also make conventional stochastic global optimization algorithms unusable due to their requirement of a huge number of objective and constraint function evaluations. Surrogate model, or metamodeling-based global optimization techniques have been introduced to address these issues. Various surrogate models, including kriging, radial basis functions (RBF), multivariate adaptive regression splines (MARS), and polynomial regression (PR), are built using limited samplings on the original objective/constraint functions to reduce needed computation in the search of global optimum. In many real-world design optimization applications, computationally expensive numerical simulation models are used as objective and/or constraint functions. To solve these problems, enormous fitness function evaluations are required during the evolution based search process when advanced Global Optimization algorithms, such as DIRECT search, Differential Evolution (DE), and Bat Algorithm (BA) are used. In this work, improvements have been made to three widely used global optimization algorithms, Divided Rectangles (DIRECT), Differential Evolution (DE), and Bat Algorithm (BA) by integrating appropriate surrogate modeling methods to increase the computation efficiency of these algorithms to support MBD. The superior performance of these new algorithms in comparison with their original counterparts are shown using commonly used optimization algorithm testing benchmark problems. Integration of the surrogate modeling methods have considerably improved the search efficiency of the DIRECT, DE, and BA algorithms with significant reduction on the Number of Function Evaluations (NFEs). The newly introduced algorithms are then applied to a complex engineering design optimization problem, the design optimization of floating wind turbine platform, to test its effectiveness in real-world applications. These newly improved algorithms were able to identify better design solutions using considerably lower NFEs on the computationally expensive performance simulation model of the design. The methods of integrating surrogate modeling to improve DIRECT, DE and BA global optimization searches and the resulting algorithms proved to be effective for solving complex and computationally intensive global optimization problems, and formed a foundation for future research in this area.

Download or read book Multi Objective Optimization using Artificial Intelligence Techniques written by Seyedali Mirjalili and published by Springer. This book was released on 2019-07-24 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the most well-regarded and recent nature-inspired algorithms capable of solving optimization problems with multiple objectives. Firstly, it provides preliminaries and essential definitions in multi-objective problems and different paradigms to solve them. It then presents an in-depth explanations of the theory, literature review, and applications of several widely-used algorithms, such as Multi-objective Particle Swarm Optimizer, Multi-Objective Genetic Algorithm and Multi-objective GreyWolf Optimizer Due to the simplicity of the techniques and flexibility, readers from any field of study can employ them for solving multi-objective optimization problem. The book provides the source codes for all the proposed algorithms on a dedicated webpage.

Download or read book Engineering Design via Surrogate Modelling written by Alexander Forrester and published by John Wiley & Sons. This book was released on 2008-09-15 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Surrogate models expedite the search for promising designs by standing in for expensive design evaluations or simulations. They provide a global model of some metric of a design (such as weight, aerodynamic drag, cost, etc.), which can then be optimized efficiently. Engineering Design via Surrogate Modelling is a self-contained guide to surrogate models and their use in engineering design. The fundamentals of building, selecting, validating, searching and refining a surrogate are presented in a manner accessible to novices in the field. Figures are used liberally to explain the key concepts and clearly show the differences between the various techniques, as well as to emphasize the intuitive nature of the conceptual and mathematical reasoning behind them. More advanced and recent concepts are each presented in stand-alone chapters, allowing the reader to concentrate on material pertinent to their current design problem, and concepts are clearly demonstrated using simple design problems. This collection of advanced concepts (visualization, constraint handling, coping with noisy data, gradient-enhanced modelling, multi-fidelity analysis and multiple objectives) represents an invaluable reference manual for engineers and researchers active in the area. Engineering Design via Surrogate Modelling is complemented by a suite of Matlab codes, allowing the reader to apply all the techniques presented to their own design problems. By applying statistical modelling to engineering design, this book bridges the wide gap between the engineering and statistics communities. It will appeal to postgraduates and researchers across the academic engineering design community as well as practising design engineers. Provides an inclusive and practical guide to using surrogates in engineering design. Presents the fundamentals of building, selecting, validating, searching and refining a surrogate model. Guides the reader through the practical implementation of a surrogate-based design process using a set of case studies from real engineering design challenges. Accompanied by a companion website featuring Matlab software at http://www.wiley.com/go/forrester

Download or read book Developing Efficient Strategies for Automatic Calibration of Computationally Intensive Environmental Models written by Seyed Saman Razavi and published by . This book was released on 2013 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: Environmental simulation models have been playing a key role in civil and environmental engineering decision making processes for decades. The utility of an environmental model depends on how well the model is structured and calibrated. Model calibration is typically in an automated form where the simulation model is linked to a search mechanism (e.g., an optimization algorithm) such that the search mechanism iteratively generates many parameter sets (e.g., thousands of parameter sets) and evaluates them through running the model in an attempt to minimize differences between observed data and corresponding model outputs. The challenge rises when the environmental model is computationally intensive to run (with run-times of minutes to hours, for example) as then any automatic calibration attempt would impose a large computational burden. Such a challenge may make the model users accept sub-optimal solutions and not achieve the best model performance. The objective of this thesis is to develop innovative strategies to circumvent the computational burden associated with automatic calibration of computationally intensive environmental models.

Download or read book Calibration of Watershed Models written by Qingyun Duan and published by John Wiley & Sons. This book was released on 2003-01-10 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Published by the American Geophysical Union as part of the Water Science and Application Series, Volume 6. During the past four decades, computer-based mathematical models of watershed hydrology have been widely used for a variety of applications including hydrologic forecasting, hydrologic design, and water resources management. These models are based on general mathematical descriptions of the watershed processes that transform natural forcing (e.g., rainfall over the landscape) into response (e.g., runoff in the rivers). The user of a watershed hydrology model must specify the model parameters before the model is able to properly simulate the watershed behavior.

Download or read book Optimization of Complex Systems Theory Models Algorithms and Applications written by Hoai An Le Thi and published by Springer. This book was released on 2019-06-15 with total page 1164 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains 112 papers selected from about 250 submissions to the 6th World Congress on Global Optimization (WCGO 2019) which takes place on July 8–10, 2019 at University of Lorraine, Metz, France. The book covers both theoretical and algorithmic aspects of Nonconvex Optimization, as well as its applications to modeling and solving decision problems in various domains. It is composed of 10 parts, each of them deals with either the theory and/or methods in a branch of optimization such as Continuous optimization, DC Programming and DCA, Discrete optimization & Network optimization, Multiobjective programming, Optimization under uncertainty, or models and optimization methods in a specific application area including Data science, Economics & Finance, Energy & Water management, Engineering systems, Transportation, Logistics, Resource allocation & Production management. The researchers and practitioners working in Nonconvex Optimization and several application areas can find here many inspiring ideas and useful tools & techniques for their works.

Download or read book TK MARS written by Hadis Anahideh and published by . This book was released on 2019 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: Surrogate optimization approaches for black-box functions focus on approximating the underlying function, using metamodeling techniques, in order to optimize computationally expensive simulation models. Historically, surrogate optimization models have been validated by deterministic (noiseless) functions with every variable being significant. As a result, many surrogate optimization models used interpolating surrogates. However, many real world experiments often times include parameters that are insignificant and uncertainties associated with the black-box function. Using traditional interpolating surrogate optimization methods can lead to surrogate models with unnecessary predictors and sensitivity to noise. Consequently, a surrogate model with flexible, non-interpolating, and parsimonious characteristics is required to overcome real-world noisy black-box functions with only a subset of important variables. One such surrogate model is, Multivariate Adaptive Regression Splines (MARS) which was initially developed by Friedman. In this study, we propose a modified version of MARS, Tree Knot MARS (TK-MARS), to improve the application of MARS within the surrogate optimization context. TK-MARS is able to identify the peaks and valleys for optimization using a classification and regression tree partitioning method. Furthermore, we develop a smart replication strategy based on hypothesis testing. The Smart-Replication approach identifies the promising points to replicate and the number of replications for each of them.

Download or read book Comparison of Stochastic Radial Basis Function and PEST for Automatic Calibration of Computationally Expensive Groundwater Models with Application to Miyun Huai Shun Aquifer written by Ying Wan and published by . This book was released on 2013 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: Groundwater numerical models have been widely used as effective tools to analyze and manage water resources. However, the accuracy and reliability of a groundwater numerical model largely depends on model parameters calibration, which is extremely computationally expensive. Therefore, it is highly desirable that efficient optimization algorithms be applied to automatic calibration problems. In this study, we compare the performance of three optimization algorithms and propose a new hybrid method. The algorithms are applied to calibration of a model for part of Beijing water supply. We first outline the three algorithms and briefly describe our hybrid method. The first algorithm referred as PEST in this paper is the Gauss-Marquardt-Levenberg (GML) method including truncated singular value decomposition, which is widely applied in the field of model parameter calibration. As the second one, CMAES_P is a "PEST compatible" implementation of CMA-ES (Covariance Matrix Adaptation Evolution Strategy) global optimization algorithm. PEST derivative-based algorithm and CMAES_P are both encapsulated in the automated parameter optimization software PEST, which has advanced predictive analysis and regularization features to minimize user-specified objective functions. The third one, called Stochastic Radial Basis Function (Stochastic RBF) method, is developed by Regis and Shoemaker (2007), which utilizes radial basis function as the response surface model to approximate the expensive objective function. Our new hybrid method combines Stochastic RBF and PEST derivative-based algorithm, which provides PEST derivative-based algorithm with the starting points found by Stochastic RBF. This paper compares the performances of the aforementioned four algorithms for automatic parameter calibration of a groundwater model on three 28-parameter cases and two synthetic test function calibration problems. We employ the following characteristics as our comparison criteria on all the cases: (1) efficiency in giving good objective function for a given number of function evaluations; (2) performance for different statistical criteria; (3) variability of solutions in multiple trials; (4) improvements if more function evaluations are performed. On the basis of 20 trials, the results indicate that Stochastic RBF is best among the three and CMAES_P is superior to PEST. In addition, our hybrid method still failed to beat Stochastic RBF in highly computationally expensive nonlinear cases. To sum up, our results show that Stochastic RBF method is a more efficient alternative to PEST for automatic parameter calibration of computationally expensive groundwater models. ii.

Download or read book An Improved Framework for Watershed Discretization and Model Calibration written by Amin Haghnegahdar and published by . This book was released on 2015 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large-scale (~103-106 km2) physically-based distributed hydrological models have been used increasingly, due to advances in computational capabilities and data availability, in a variety of water and environmental resources management, such as assessing human impacts on regional water budget. These models inevitably contain a large number of parameters used in simulation of various physical processes. Many of these parameters are not measurable or nearly impossible to measure. These parameters are typically estimated using model calibration, defined as adjusting the parameters so that model simulations can reproduce the observed data as close as possible. Due to the large number of model parameters, it is essential to use a formal automated calibration approach in distributed hydrological modelling. The St. Lawrence River Basin in North America contains the largest body of surface fresh water, the Great Lakes, and is of paramount importance for United States and Canada. The Lakes' water levels have huge impact on the society, ecosystem, and economy of North America. A proper hydrological modelling and basin-wide water budget for the Great Lakes Basin is essential for addressing some of the challenges associated with this valuable water resource, such as a persistent extreme low water levels in the lakes. Environment Canada applied its Modélisation Environnementale-Surface et Hydrologie (MESH) modelling system to the Great Lakes watershed in 2007. MESH is a coupled semi-distributed land surface-hydrological model intended for various water management purposes including improved operational streamflow forecasts. In that application, model parameters were only slightly adjusted during a brief manual calibration process. Therefore, MESH streamflow simulations were not satisfactory and there was a considerable need to improve its performance for proper evaluation of the MESH modelling system. Collaborative studies between the United States and Canada also highlighted the need for inclusion of the prediction uncertainty in modelling results, for more effective management of the Great Lakes system. One of the primary goals of this study is to build an enhanced well-calibrated MESH model over the Great Lakes Basin, particularly in the context of streamflow predictions in ungauged basins. This major contribution is achieved in two steps. First, the MESH performance in predicting streamflows is benchmarked through a rather extensive formal calibration, for the first time, in the Great Lakes Basin. It is observed that a global calibration strategy using multiple sub-basins substantially improved MESH streamflow predictions, confirming the essential role of a formal model calibration. At the next step, benchmark results are enhanced by further refining the calibration approach and adding uncertainty assessment to the MESH streamflow predictions. This enhancement was mainly achieved by modifying the calibration parameters and increasing the number of sub-basins used in calibration. A rigorous multi-criteria comparison between the two experiments confirmed that the MESH model performance is indeed improved using the revised calibration approach. The prediction uncertainty bands for the MESH streamflow predictions were also estimated in the new experiment. The most influential parameters in MESH were also identified to be soil and channel roughness parameters based on a local sensitivity test. One of the main challenges in hydrological distributed modelling is how to represent the existing spatial heterogeneity in nature. This task is normally performed via watershed discretization, defined as the process of subdividing the basin into manageable “hydrologically similar” computational units. The model performance, and how well it can be calibrated using a limited budget, largely depends on how a basin is discretized. Discretization decisions in hydrologic modelling studies are, however, often insufficiently assessed prior to model simulation and parameter. Few studies explicitly present an organized and objective methodology for assessing discretization schemes, particularly with respect to the streamflow predictions in ungauged basins. Another major goal of this research is to quantitatively assess watershed discretization schemes for distributed hydrological models, with various level of spatial data aggregation, in terms of their skill to predict flows in ungauged basins. The methodology was demonstrated using the MESH model as applied to the Nottawasaga river basin in Ontario, Canada. The schemes differed from a simple lumped scheme to more complex ones by adding spatial land cover and then spatial soil information. Results reveal that calibration budget is an important factor in model performance. In other words, when constrained by calibration budget, using a more complex scheme did not necessarily lead to improved performance in validation. The proposed methodology was also implemented using a shorter sub-period for calibration, aiming at substantial computational saving. This strategy is shown to be promising in producing consistent results and has the potential to increase computational efficiency of this comparison framework. The outcome of this very computationally intensive research, i.e., the well-calibrated MESH model for the Great Lakes and all the final parameter sets found, are now available to be used by other research groups trying to study various aspects of the Great Lakes System. In fact, the benchmark results are already used in the Great Lakes Runoff Intercomparison Project (GRIP). The proposed comparison framework can also be applied to any distributed hydrological model to evaluate alternative discretization schemes, and identify one that is preferred for a certain case.

Download or read book Rainfall Runoff Modelling written by Keith J. Beven and published by John Wiley & Sons. This book was released on 2012-01-30 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rainfall-Runoff Modelling: The Primer, Second Edition is the follow-up of this popular and authoritative text, first published in 2001. The book provides both a primer for the novice and detailed descriptions of techniques for more advanced practitioners, covering rainfall-runoff models and their practical applications. This new edition extends these aims to include additional chapters dealing with prediction in ungauged basins, predicting residence time distributions, predicting the impacts of change and the next generation of hydrological models. Giving a comprehensive summary of available techniques based on established practices and recent research the book offers a thorough and accessible overview of the area. Rainfall-Runoff Modelling: The Primer Second Edition focuses on predicting hydrographs using models based on data and on representations of hydrological process. Dealing with the history of the development of rainfall-runoff models, uncertainty in mode predictions, good and bad practice and ending with a look at how to predict future catchment hydrological responses this book provides an essential underpinning of rainfall-runoff modelling topics. Fully revised and updated version of this highly popular text Suitable for both novices in the area and for more advanced users and developers Written by a leading expert in the field Guide to internet sources for rainfall-runoff modelling software

Download or read book Multi Objective Optimization using Evolutionary Algorithms written by Kalyanmoy Deb and published by John Wiley & Sons. This book was released on 2001-07-05 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimierung mit mehreren Zielen, evolutionäre Algorithmen: Dieses Buch wendet sich vorrangig an Einsteiger, denn es werden kaum Vorkenntnisse vorausgesetzt. Geboten werden alle notwendigen Grundlagen, um die Theorie auf Probleme der Ingenieurtechnik, der Vorhersage und der Planung anzuwenden. Der Autor gibt auch einen Ausblick auf Forschungsaufgaben der Zukunft.

Download or read book Global Sensitivity Analysis written by Andrea Saltelli and published by John Wiley & Sons. This book was released on 2008-02-28 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex mathematical and computational models are used in all areas of society and technology and yet model based science is increasingly contested or refuted, especially when models are applied to controversial themes in domains such as health, the environment or the economy. More stringent standards of proofs are demanded from model-based numbers, especially when these numbers represent potential financial losses, threats to human health or the state of the environment. Quantitative sensitivity analysis is generally agreed to be one such standard. Mathematical models are good at mapping assumptions into inferences. A modeller makes assumptions about laws pertaining to the system, about its status and a plethora of other, often arcane, system variables and internal model settings. To what extent can we rely on the model-based inference when most of these assumptions are fraught with uncertainties? Global Sensitivity Analysis offers an accessible treatment of such problems via quantitative sensitivity analysis, beginning with the first principles and guiding the reader through the full range of recommended practices with a rich set of solved exercises. The text explains the motivation for sensitivity analysis, reviews the required statistical concepts, and provides a guide to potential applications. The book: Provides a self-contained treatment of the subject, allowing readers to learn and practice global sensitivity analysis without further materials. Presents ways to frame the analysis, interpret its results, and avoid potential pitfalls. Features numerous exercises and solved problems to help illustrate the applications. Is authored by leading sensitivity analysis practitioners, combining a range of disciplinary backgrounds. Postgraduate students and practitioners in a wide range of subjects, including statistics, mathematics, engineering, physics, chemistry, environmental sciences, biology, toxicology, actuarial sciences, and econometrics will find much of use here. This book will prove equally valuable to engineers working on risk analysis and to financial analysts concerned with pricing and hedging.