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Calibration Of Hydrologic Models Using Distributed Surrogate Model Optimization Techniques
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Book Synopsis Calibration of Hydrologic Models Using Distributed Surrogate Model Optimization Techniques by : Mahtab Kamali
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.
Book Synopsis Efficient Multi-objective Surrogate Optimization of Computationally Expensive Models with Application to Watershed Model Calibration by : Taimoor Akhtar
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.
Book Synopsis Evaluating and Developing Parameter Optimization and Uncertainty Analysis Methods for a Computationally Intensive Distributed Hydrological Model by : Xuesong Zhang
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.
Book Synopsis Calibration of Watershed Models by : Qingyun Duan
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.
Book Synopsis Hydrologic Data Assimilation by : Caleb Matthew DeChant
Download or read book Hydrologic Data Assimilation written by Caleb Matthew DeChant and published by . This book was released on 2010 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis is a combination of two separate studies which examine hydrologic data assimilation techniques: 1) to determine the applicability of assimilation of remotely sensed data in operational models and 2) to compare the effectiveness of assimilation and other calibration techniques. The first study examines the ability of Data Assimilation of remotely sensed microwave radiance data to improve snow water equivalent prediction, and ultimately operational streamflow forecasts. Operational streamflow forecasts in the National Weather Service River Forecast Center are produced with a coupled SNOW17 (snow model) and SACramento Soil Moisture Accounting (SAC-SMA) model. A comparison of two assimilation techniques, the Ensemble Kalman Filter (EnKF) and the Particle Filter (PF), is made using a coupled SNOW17 and the Microwave Emission Model for Layered Snowpack model to assimilate microwave radiance data. Microwave radiance data, in the form of brightness temperature (TB), is gathered from the Advanced Microwave Scanning Radiometer-Earth Observing System at the 36.5GHz channel. SWE prediction is validated in a synthetic experiment. The distribution of snowmelt from an experiment with real data is then used to run the SAC-SMA model. Several scenarios on state or joint state-parameter updating with TB data assimilation to SNOW-17 and SAC-SMA models were analyzed, and the results show potential benefit for operational streamflow forecasting. The second study compares the effectiveness of different calibration techniques in hydrologic modeling. Currently, the most commonly used methods for hydrologic model calibration are global optimization techniques. While these techniques have become very efficient and effective in optimizing the complicated parameter space of hydrologic models, the uncertainty with respect to parameters is ignored. This has led to recent research looking into Bayesian Inference through Monte Carlo methods to analyze the ability to calibrate models and represent the uncertainty in relation to the parameters. Research has recently been performed in filtering and Markov Chain Monte Carlo (MCMC) techniques for optimization of hydrologic models. At this point, a comparison of the effectiveness of global optimization, filtering and MCMC techniques has yet to be reported in the hydrologic modeling community. This study compares global optimization, MCMC, the PF, the Particle Smoother, the EnKF and the Ensemble Kalman Smoother for the purpose of parameter estimation in both the HyMod and SAC-SMA hydrologic models.
Book Synopsis Sensitivity-based Guided Automatic Calibration of Hydrological Models by : Mohammad Semnani
Download or read book Sensitivity-based Guided Automatic Calibration of Hydrological Models written by Mohammad Semnani and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new method for efficient calibration of complex hydrological models that combines Dynamically Dimensioned Search (DDS) global optimization algorithm with Global Sensitivity Analysis (GSA) methods is introduced. This approach, which is called sensitivity-informed DDS, utilizes sensitivity indices to increase the probability of perturbation for the most sensitive parameters, while giving low chance to least sensitive ones. This feature improves the efficiency and effectiveness of optimization by finding good quality solutions in a shorter time. Three different implementations of sensitivity-informed DDS are considered. The first approach is named as GSA↔DDS, in which GSA toolboxes (Morris or Sobol) are performed initially and throughout the optimization process to constantly update the sensitivity information. The second approach is called GSA→DDS. In this method, the GSA methods are only performed initially to include the results of GSA within optimization process. The final implementation is called VARS→DDS. In this method, to enhance the efficiency of sensitivity analysis and optimization, VARS toolbox is performed outside the optimization to provide the sensitivity information. The performances of GSA↔DDS, GSA→DDS and VARS→DDS are compared with original DDS by solving various optimization problems (test functions and model calibration case studies). According to the results, when calibrating complex hydrological models with enough computational budget, VARS→DDS is significantly more efficient and effective than original DDS. However, the results also show that GSA→DDS and GSA↔DDS methods do not substantially improve the convergence rate and the final best solution compared to DDS. Thus, VARS→DDS is the recommended approach for sensitivity-informed DDS in calibration of distributed and semi-distributed models, when enough computational resources are available.
Book Synopsis Calibration Approaches for Distributed Hydrologic Models in Poorly Gaged Basins by :
Download or read book Calibration Approaches for Distributed Hydrologic Models in Poorly Gaged Basins written by and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Developments in Informal Multi-Criteria Calibration and Uncertainty Estimation in Hydrological Modelling by : Mahyar Shafii Hassanabadi
Download or read book Developments in Informal Multi-Criteria Calibration and Uncertainty Estimation in Hydrological Modelling written by Mahyar Shafii Hassanabadi and published by . This book was released on 2014 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hydrologic modelling has benefited from significant developments over the past two decades, which has led to the development of distributed hydrologic models. Parameter adjustment, or model calibration, is extremely important in the application of these hydrologic models. Multi-criteria calibration schemes and several formal and informal predictive uncertainty estimation methodologies are among the approaches to improve the results of model calibration. Moreover, literature indicates a general agreement between formal and informal approaches with respect to the predictive uncertainty estimation in single-criterion calibration cases. This research extends the comparison between these techniques to multi-criteria calibration cases, and furthermore, proposes new ideas to improve informal multi-criteria calibration and uncertainty estimation in hydrological modelling. GLUE is selected as a candidate informal methodology due to its extreme popularity among hydrological modellers, i.e., based on the number of applications in the past two decades. However, it is hypothesized that improvements can be applied to other certain types of informal uncertainty estimation as well. The first contribution of this research is an in-depth comparison between GLUE and Bayesian inference in the multi-criteria context. Such a comparison is novel because past literature has focused on comparisons for only single criterion calibration studies. Unlike the previous research, the results show that there can be considerable differences in hydrograph prediction intervals generated by traditional GLUE and Bayesian inference in multi-criteria cases. Bayesian inference performs more satisfactorily than GLUE along most of the comparative measures. However, results also reveal that a standard Bayesian formulation (i.e., aggregating all uncertainties into a single additive error term) may not demonstrate perfect reliability in the prediction mode. Furthermore, in cases with a limited computational budget, non-converged MCMC sampling proves to be an appropriate alternative to GLUE since it is reasonably consistent with a fully-converged Bayesian approach, even though the fully-converged MCMC requires a substantially larger number of model evaluations. Another contribution of this research is to improve the uncertainty bounds of the traditional GLUE approach by the exploration of alternative behavioural solution identification strategies. Multiple behavioural solution identification strategies from the literature are evaluated, new objective strategies are developed, and multi-criteria decision-making concepts are utilized to select the best strategy. The results indicate that the subjectivity involved in behavioural solution identification strategies impacts the uncertainty of model outcome. More importantly, a robust implementation of GLUE proves to require comparing multiple behavioural solution identification strategies and choosing the best one based on the modeller's priorities. Moreover, it appears that the proposed objective strategies are among the best options in most of the case studies investigated in this research. Thus, it is recommended that these new strategies be considered among the set of behavioural solution identification strategies in future GLUE applications. Lastly, this research also develops a full optimization-based calibration framework that is capable of utilizing both standard goodness-of-fit measures and many hydrological signatures simultaneously. These signatures can improve the calibration results by constraining the model outcome hydrologically. However, the literature shows that to simultaneously apply a large number of hydrological signatures in model calibration is challenging. Therefore, the proposed research adopts optimization concepts to accommodate many criteria (including 13 hydrologic signature-based objectives and two standard statistical goodness-of-fit measures). In the proposed framework, hydrological consistency is quantified (based on a set of signature-based measures and their desired level of acceptability) and utilized as a criterion in multiple calibration formulations. The results show that these formulations perform better than the traditional approaches to locate hydrologically consistent parameter sets in the search space. Different hydrologic models, most of which are conceptual rainfall-runoff models, are used throughout the thesis to evaluate the performance of the developed strategies. However, the developments explored in this research are typically simulation-model-independent and can be applied to calibration and uncertainty estimation of any environmental model. However, further testing of these methods is warranted for more computationally intensive simulation models, such as fully distributed hydrologic models.
Book Synopsis A Data-driven Approach Using Surrogate Models and Non-deterministic Optimization Techniques for Calibration of Soil Parameters and Sensitivity Analysis by : Gullnaz Shahzadi
Download or read book A Data-driven Approach Using Surrogate Models and Non-deterministic Optimization Techniques for Calibration of Soil Parameters and Sensitivity Analysis written by Gullnaz Shahzadi and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Structural Health Monitoring (SHM) of Civil Structures by : Gangbing Song
Download or read book Structural Health Monitoring (SHM) of Civil Structures written by Gangbing Song and published by MDPI. This book was released on 2018-04-20 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a printed edition of the Special Issue "Structural Health Monitoring (SHM) of Civil Structures" that was published in Applied Sciences
Book Synopsis The Calibration and Uncertainty Evaluation of Spatially Distributed Hydrological by : JongKwan Kim
Download or read book The Calibration and Uncertainty Evaluation of Spatially Distributed Hydrological written by JongKwan Kim and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last decade, spatially distributed hydrological models have rapidly advanced with the widespread availability of remotely sensed and geomatics information. Particularly, the areas of calibration and evaluation of spatially distributed hydrological models have been attempted in order to reduce the differences between models and improve realism through various techniques. Despite steady efforts, the study of calibrations and evaluations for spatially distributed hydrological models is still a largely unexplored field, in that there is no research in terms of the interactions of snow and water balance components with the traditional measurement methods as error functions. As one of the factors related to runoff, melting snow is important, especially in mountainous regions with heavy snowfall; however, no study considering both snow and water components simultaneously has investigated the procedures of calibration and evaluation for spatially distributed models. Additionally, novel approaches of error functions would be needed to reflect the characteristics of spatially distributed hydrological models in the comparison between simulated and observed values. Lastly, the shift from lumped model calibration to distributed model calibration has raised the model complexity. The number of unknown parameters can rapidly increase, depending on the degree of distribution. Therefore, a strategy is required to determine the optimal degree of model distributions for a study basin. In this study, we will attempt to address the issues raised above. This study utilizes the Research Distributed Hydrological Model (HL-RDHM) developed by Hydrologic Development Office of the National Weather Service (OHD-NWS). This model simultaneously simulates both snow and water balance components. It consists largely of two different modules, i.e., the Snow 17 as a snow component and the Sacramento Soil Moisture Accounting (SAC-SMA) as a water component, and is applied over the Durango River basin in Colorado, which is an area driven primarily by snow. As its main contribution, this research develops and tests various methods to calibrate and evaluate spatially distributed hydrological models with different, non-commensurate, variables and measurements. Additionally, this research provides guidance on the way to decide an appropriate degree of model distribution (resolution) for a specific water catchment.
Book Synopsis Distributed Hydrologic Modeling for Streamflow Prediction at Ungauged Basins by : Christina Bandaragoda
Download or read book Distributed Hydrologic Modeling for Streamflow Prediction at Ungauged Basins written by Christina Bandaragoda and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Hydrologic modeling and streamflow prediction of ungauged basins is an unsolved scientific problem as well as a policy-relevant science theme emerging as a major challenge to the hydrologic community. One way to address this problem is to improve hydrologic modeling capability through the use of spatial data and spatially distributed physically based models. This dissertation is composed of three papers focused on 1) the use of spatially distributed hydrologic models with spatially distributed precipitation inputs, 2) advanced multi-objective calibration techniques that estimate parameter uncertainty and use stream gauge and temperature data from multiple locations, and 3) an examination of the relationship between high-resolution soils data and streamflow recession for use in a priori parameter estimation in ungauged catchments. This research contributes to the broad quest to reduce uncertainty in predictions at ungauged basins by integrating developments of innovative modeling techniques with analyses that advance our understanding of natural systems.
Book Synopsis Distributed Hydrologic Modeling Using GIS by : Baxter E. Vieux
Download or read book Distributed Hydrologic Modeling Using GIS written by Baxter E. Vieux and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: During ten years serving with the USDA Soil Conservation Service (SCS), now known as the Natural Resources Conservation Service (NRCS), I became amazed at how millions of dollars in contract monies were spent based on simplistic hydrologic models. As project engineer in western Kansas, I was responsible for building flood control dams (authorized under Public Law 566) in the Wet Walnut River watershed. This watershed is within the Arkansas-Red River basin, as is the Illinois River basin referred to extensively in this book. After building nearly 18 of these structures, I became Assistant State Engineer in Michigan and, for a short time, State Engineer for NRCS. Again, we based our entire design and construction program on simplified relationships variously referred to as the SCS method. I recall announcing that I was going to pursue a doctoral degree and develop a new hydrologic model. One of my agency's chief engineers remarked, "Oh no, not another model!" Since then, I hope that I have not built just another model but have significantly advanced the state of hydrologic modeling for both researchers and practitioners. Using distributed hydrologic techniques described in this book, I also hope one day to forecast the response of the dams I built.
Book Synopsis Bayesian Optimization and Uncertainty Analysis of Complex Environmental Models, with Applications in Watershed Management by : Able Mashamba
Download or read book Bayesian Optimization and Uncertainty Analysis of Complex Environmental Models, with Applications in Watershed Management written by Able Mashamba and published by . This book was released on 2010 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation presents results of research in the development, testing and application of an automated calibration and uncertainty analysis framework for distributed environmental models based on Bayesian Markov chain Monte Carlo (MCMC) sampling and response surface methodology (RSM) surrogate models that use a novel random local fitting algorithm. Typical automated search methods for optimization and uncertainty assessment such as evolutionary and Nelder-Mead Simplex algorithms are inefficient and/or infeasible when applied to distributed environmental models, as exemplified by the watershed management scenario analysis case study presented as part of this dissertation. This is because the larger numbers of non-linearly interacting parameters and the more complex structures of distributed environmental models make automated calibration and uncertainty analysis more computationally demanding compared to traditional basin-averaged models. To improve efficiency and feasibility of automated calibration and uncertainty assessment of distributed models, recent research has been focusing on using the response surface methodology (RSM) to approximate objective functions such as sum of squared residuals and Bayesian inference likelihoods. This dissertation presents (i) results on a novel study of factors that affect the performance of RSM approximation during Bayesian calibration and uncertainty analysis, (ii) a new 'random local fitting' (RLF) algorithm that improves RSM approximation for large sampling domains and (iii) application of a developed automated uncertainty analysis framework that uses MCMC sampling and a spline-based radial basis approximation function enhanced by the RLF algorithm to a fully-distributed hydrologic model case study. Using the MCMC sampling and response surface approximation framework for automated parameter and predictive uncertainty assessment of a distributed environmental model is novel. While extended testing of the developed MCMC uncertainty analysis paradigm is necessary, the results presented show that the new framework is robust and efficient for the case studied and similar distributed environmental models. As distributed environmental models continue to find use in climate change studies, flood forecasting, water resource management and land use studies, results of this study will have increasing importance to automated model assessment. Potential future research from this dissertation is the investigation of how model parameter sensitivities and inter-dependencies affect the performance of response surface approximation.
Book Synopsis Advanced Hydroinformatics by : Gerald A. Corzo Perez
Download or read book Advanced Hydroinformatics written by Gerald A. Corzo Perez and published by John Wiley & Sons. This book was released on 2023-12-19 with total page 483 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Hydroinformatics Advanced Hydroinformatics Machine Learning and Optimization for Water Resources The rapid development of machine learning brings new possibilities for hydroinformatics research and practice with its ability to handle big data sets, identify patterns and anomalies in data, and provide more accurate forecasts. Advanced Hydroinformatics: Machine Learning and Optimization for Water Resources presents both original research and practical examples that demonstrate how machine learning can advance data analytics, accuracy of modeling and forecasting, and knowledge discovery for better water management. Volume Highlights Include: Overview of the application of artificial intelligence and machine learning techniques in hydroinformatics Advances in modeling hydrological systems Different data analysis methods and models for forecasting water resources New areas of knowledge discovery and optimization based on using machine learning techniques Case studies from North America, South America, the Caribbean, Europe, and Asia The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.
Book Synopsis On the Predictive Uncertainty of a Distributed Hydrologic Model by : Huidae Cho
Download or read book On the Predictive Uncertainty of a Distributed Hydrologic Model written by Huidae Cho and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We use models to simulate the real world mainly for prediction purposes. However, since any model is a simplification of reality, there remains a great deal of uncertainty even after the calibration of model parameters. The model's identifiability of realistic model parameters becomes questionable when the watershed of interest is small, and its time of concentration is shorter than the computational time step of the model. To improve the discovery of more reliable and more realistic sets of model parameters instead of mathematical solutions, a new algorithm is needed. This algorithm should be able to identify mathematically inferior but more robust solutions as well as to take samples uniformly from high-dimensional search spaces for the purpose of uncertainty analysis. Various watershed configurations were considered to test the Soil and Water Assessment Tool (SWAT) model's identifiability of the realistic spatial distribution of land use, soil type, and precipitation data. The spatial variability in small watersheds did not significantly affect the hydrographs at the watershed outlet, and the SWAT model was not able to identify more realistic sets of spatial data. A new populationbased heuristic called the Isolated Speciation-based Particle Swarm Optimization (ISPSO) was developed to enhance the explorability and the uniformity of samples in high-dimensional problems. The algorithm was tested on seven mathematical functions and outperformed other similar algorithms in terms of computational cost, consistency, and scalability. One of the test functions was the Griewank function, whose number of minima is not well defined although the function serves as the basis for evaluating multi-modal optimization algorithms. Numerical and analytical methods were proposed to count the exact number of minima of the Griewank function within a hyperrectangle. The ISPSO algorithm was applied to the SWAT model to evaluate the performance consistency of optimal solutions and perform uncertainty analysis in the Generalized Likelihood Uncertainty Estimation (GLUE) framework without assuming a statistical structure of modeling errors. The algorithm successfully found hundreds of acceptable sets of model parameters, which were used to estimate their prediction limits. The uncertainty bounds of this approach were comparable to those of the typical GLUE approach.
Book Synopsis Adaptive and Natural Computing Algorithms by : Mikko Kolehmainen
Download or read book Adaptive and Natural Computing Algorithms written by Mikko Kolehmainen and published by Springer Science & Business Media. This book was released on 2009-10-15 with total page 645 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-proceedings of the 9th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2009, held in Kuopio, Finland, in April 2009. The 63 revised full papers presented were carefully reviewed and selected from a total of 112 submissions. The papers are organized in topical sections on neutral networks, evolutionary computation, learning, soft computing, bioinformatics as well as applications.
