Interactive Visualization for Evolutionary Optimization of Conceptual Rainfall-Streamflow Models

Calibration is an essential part of the application of conceptual rainfall-streamflow models to watershed management problems for civil engineers. However, the identification of a unique set of parameters is difficult, if not impossible, for commonly used models. Most multiobjective methods and uncertainty assessment tools require substantial numbers of function evaluations and limit the intervention of experienced modelers in the calibration process. This paper demonstrates the application of an efficient user-driven calibration-support system to conceptual rainfall-streamflow models. The system is designed to assist the hydrological modeler by means of rapid sampling of solutions, clustering, and visualization, together with interactivity to exploit the expertise of the user and/or the knowledge revealed by the clustering technique. The efficiency of the multiobjective calibration is enhanced through the use of a novel objective function based on hydrograph slope. The application of the system to the calibration of the SIXPAR conceptual rainfall-streamflow model using a synthetic time series is shown to be effective.

[1]  Sue L. Denham,et al.  Interactive visualisation for decision support and evaluation of robustness - in theory and in practice , 2005, Adv. Eng. Informatics.

[2]  Soroosh Sorooshian,et al.  Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information , 1998 .

[3]  Luis Garrote,et al.  Object-oriented software for distributed rainfall-runoff models , 1997 .

[4]  Henrik Madsen,et al.  Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. , 2000 .

[5]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[6]  M. H. Diskin,et al.  A procedure for the selection of objective functions for hydrologic simulation models , 1977 .

[7]  Keith Beven,et al.  The future of distributed models: model calibration and uncertainty prediction. , 1992 .

[8]  Slobodan P. Simonovic,et al.  Decision‐Support System for Long‐Range Stream Flow Forecasting , 1994 .

[9]  Keith Beven,et al.  Prophecy, reality and uncertainty in distributed hydrological modelling , 1993 .

[10]  Dulcy M. Abraham,et al.  Operational Response Model for Physically Attacked Water Networks Using NSGA-II , 2006 .

[11]  S. Sorooshian,et al.  A multistep automatic calibration scheme for river forecasting models , 2000 .

[12]  Robin Wardlaw,et al.  Multireservoir Systems Optimization Using Genetic Algorithms: Case Study , 2000 .

[13]  Yang Liu,et al.  A Hybrid Optimization Method of Multi-objective Genetic Algorithm (MOGA) and K-Nearest Neighbor (KNN) Classifier for Hydrological Model Calibration , 2004, IDEAL.

[14]  David E. Goldberg,et al.  Genetic Algorithms in Pipeline Optimization , 1987 .

[15]  Guido Bugmann,et al.  Prediction of the Behaviour of Masonry Wall Panels Using Evolutionary Computation and Cellular Automata , 2006, EG-ICE.

[16]  Susan L. Denham,et al.  Visualisation methods for supporting the exploration of high dimensional problem spaces in engineering design , 2003, Proceedings International Conference on Coordinated and Multiple Views in Exploratory Visualization - CMV 2003 -.

[17]  Keith Beven,et al.  Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology , 2001 .

[18]  George Kuczera,et al.  Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm , 1998 .

[19]  Micha Werner,et al.  Reduction of Monte-Carlo simulation runs for uncertainty estimation in hydrological modelling , 2003 .

[20]  Angus R. Simpson,et al.  Competent Genetic-Evolutionary Optimization of Water Distribution Systems , 2001 .

[21]  Soroosh Sorooshian,et al.  Status of Automatic Calibration for Hydrologic Models: Comparison with Multilevel Expert Calibration , 1999 .

[22]  D. Legates,et al.  Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation , 1999 .

[23]  J. Nicell,et al.  Evaluation of global optimization methods for conceptual rainfall-runoff model calibration , 1997 .

[24]  S. Sorooshian,et al.  Uniqueness and observability of conceptual rainfall‐runoff model parameters: The percolation process examined , 1983 .

[25]  Ben Shneiderman,et al.  Designing The User Interface , 2013 .

[26]  S. Sorooshian,et al.  Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .

[27]  Susan L. Denham,et al.  Visualizing Search and Solution Spaces in the Optimum Design of Biaxial Columns , 2006 .

[28]  Soroosh Sorooshian,et al.  Multi-objective global optimization for hydrologic models , 1998 .

[29]  M. Franchini.  Use of a genetic algorithm combined with a local search method for the automatic calibration of conceptual rainfall-runoff models , 1996 .

[30]  Soroosh Sorooshian,et al.  A framework for development and application of hydrological models , 2001, Hydrology and Earth System Sciences.

[31]  S. Sorooshian,et al.  Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data , 1996 .

[32]  Henrik Madsen,et al.  Comparison of different automated strategies for calibration of rainfall-runoff models , 2002 .

[33]  Alfred Inselberg,et al.  Multidimensional Lines. I: Representation , 1994, SIAM J. Appl. Math..

[34]  G. Kuczera.  Improved parameter inference in catchment models: 1. Evaluating parameter uncertainty , 1983 .

[35]  Soroosh Sorooshian,et al.  Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods , 2000 .

[36]  S. Sorooshian,et al.  Effective and efficient algorithm for multiobjective optimization of hydrologic models , 2003 .

[37]  Q. J. Wang.  The Genetic Algorithm and Its Application to Calibrating Conceptual Rainfall-Runoff Models , 1991 .

[38]  S. Liong,et al.  Peak-flow forecasting with genetic algorithm and SWMM , 1995 .

[39]  I. Douglas,et al.  Simple Genetic Algorithm with Local Tuning: Efficient Global Optimizing Technique , 1998 .

[40]  J. D. Mathews,et al.  Conceptual Building Design—Evolutionary Approach , 2003 .

[41]  Göran Lindström,et al.  Development of an automatic calibration scheme for the HBV hydrological model , 1997 .

[42]  D. P. Solomatine,et al.  Two Strategies of Adaptive Cluster Covering with Descent and Their Comparison to Other Algorithms , 1999, J. Glob. Optim..

[43]  Keith Beven,et al.  A manifesto for the equifinality thesis , 2006 .

[44]  Ian Stephen James Packham,et al.  An interactive visualisation system for engineering design using evolutionary computing , 2003 .

[45]  Quan J. Wang,et al.  Using genetic algorithms to optimise model parameters , 1997 .

[46]  J. Ndiritu,et al.  AN IMPROVED GENETIC ALGORITHM FOR CONTINUOUS AND MIXED DISCRETE-CONTINUOUS OPTIMIZATION , 1999 .

[47]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[48]  H. Houghton-Carr.  Assessment criteria for simple conceptual daily rainfall-runoff models , 1999 .

[49]  T. Gan,et al.  Automatic Calibration of Conceptual Rainfall-Runoff Models: Optimization Algorithms, Catchment Conditions, and Model Structure , 1996 .

[50]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[51]  S. Uhlenbrook,et al.  Prediction uncertainty of conceptual rainfall-runoff models caused by problems in identifying model parameters and structure , 1999 .

[52]  S. Sorooshian,et al.  Stochastic parameter estimation procedures for hydrologie rainfall‐runoff models: Correlated and heteroscedastic error cases , 1980 .