Multi-objective optimization of empirical hydrological model for streamflow prediction

Summary Traditional calibration of hydrological models is performed with a single objective function. Practical experience with the calibration of hydrologic models reveals that single objective functions are often inadequate to properly measure all of the characteristics of the hydrologic system. To circumvent this problem, in recent years, a lot of studies have looked into the automatic calibration of hydrological models with multi-objective functions. In this paper, the multi-objective evolution algorithm MODE-ACM is introduced to solve the multi-objective optimization of hydrologic models. Moreover, to improve the performance of the MODE-ACM, an Enhanced Pareto Multi-Objective Differential Evolution algorithm named EPMODE is proposed in this research. The efficacy of the MODE-ACM and EPMODE are compared with two state-of-the-art algorithms NSGA-II and SPEA2 on two case studies. Five test problems are used as the first case study to generate the true Pareto front. Then this approach is tested on a typical empirical hydrological model for monthly streamflow forecasting. The results of these case studies show that the EPMODE, as well as MODE-ACM, is effective in solving multi-objective problems and has great potential as an efficient and reliable algorithm for water resources applications.

[1]  N. J. de Vos,et al.  Multiobjective training of artificial neural networks for rainfall‐runoff modeling , 2008 .

[2]  Kwok-Wing Chau,et al.  Data-driven models for monthly streamflow time series prediction , 2010, Eng. Appl. Artif. Intell..

[3]  Sanyou Zeng,et al.  An Orthogonal Multi-objective Evolutionary Algorithm for Multi-objective Optimization Problems with Constraints , 2004, Evolutionary Computation.

[4]  Ozgur Kisi,et al.  Wavelet regression model as an alternative to neural networks for monthly streamflow forecasting , 2009 .

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

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

[7]  Xuesong Zhang,et al.  On the use of multi‐algorithm, genetically adaptive multi‐objective method for multi‐site calibration of the SWAT model , 2010 .

[8]  Chuntian Cheng,et al.  A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series , 2009 .

[9]  Chuntian Cheng,et al.  A new indirect multi-step-ahead prediction model for a long-term hydrologic prediction , 2008 .

[10]  Hoshin V. Gupta,et al.  Exploring the relationship between complexity and performance in a land surface model using the multicriteria method , 2002 .

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

[12]  Saman Razavi,et al.  Long‐lead seasonal rainfall forecasting using time‐delay recurrent neural networks: a case study , 2008 .

[13]  Bellie Sivakumar,et al.  Characterization and prediction of runoff dynamics: a nonlinear dynamical view , 2002 .

[14]  Luis A. Bastidas,et al.  Calibrating a land surface model of varying complexity using multicriteria methods and the Cabauw dataset , 2002 .

[15]  Bogdan Filipic,et al.  DEMO: Differential Evolution for Multiobjective Optimization , 2005, EMO.

[16]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[17]  Jun Wang,et al.  WBMOAIS: A novel artificial immune system for multiobjective optimization , 2010, Comput. Oper. Res..

[18]  Chuntian Cheng,et al.  Long-Term Prediction of Discharges in Manwan Hydropower Using Adaptive-Network-Based Fuzzy Inference Systems Models , 2005, ICNC.

[19]  H. Madsen,et al.  Multiobjective calibration with Pareto preference ordering: An application to rainfall‐runoff model calibration , 2005 .

[20]  W. J. Conover,et al.  Practical Nonparametric Statistics , 1972 .

[21]  Bellie Sivakumar,et al.  River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches , 2002 .

[22]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[23]  Luis A. Bastidas,et al.  Multiobjective particle swarm optimization for parameter estimation in hydrology , 2006 .

[24]  Ercan Kahya,et al.  Flow forecast by SWAT model and ANN in Pracana basin, Portugal , 2009, Adv. Eng. Softw..

[25]  Enrique Alba,et al.  SMPSO: A new PSO-based metaheuristic for multi-objective optimization , 2009, 2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making(MCDM).

[26]  Jason R. Schott.  Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. , 1995 .

[27]  O. Kisi.  Neural Networks and Wavelet Conjunction Model for Intermittent Streamflow Forecasting , 2009 .

[28]  Yi Liu,et al.  A Novel Multi-Objective Shuffled Complex Differential Evolution Algorithm with Application to Hydrological Model Parameter Optimization , 2013, Water Resources Management.

[29]  Marco Laumanns,et al.  Combining Convergence and Diversity in Evolutionary Multiobjective Optimization , 2002, Evolutionary Computation.

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

[31]  David E. Goldberg,et al.  Simplifying multiobjective optimization: An automated design methodology for the nondominated sorted genetic algorithm‐II , 2003 .

[32]  Katie Price,et al.  Tradeoffs among watershed model calibration targets for parameter estimation , 2012 .

[33]  Patrick M. Reed,et al.  Striking the Balance: Long-Term Groundwater Monitoring Design for Conflicting Objectives , 2004 .

[34]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[35]  K. P. Sudheer,et al.  A neuro-fuzzy computing technique for modeling hydrological time series , 2004 .

[36]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[37]  Patrick M. Reed,et al.  How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration , 2005 .

[38]  Enrique Alba,et al.  The jMetal framework for multi-objective optimization: Design and architecture , 2010, IEEE Congress on Evolutionary Computation.

[39]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[40]  Ying Wang,et al.  Multi-objective differential evolution with adaptive Cauchy mutation for short-term multi-objective optimal hydro-thermal scheduling , 2010 .

[41]  N. Madavan.  Multiobjective optimization using a Pareto differential evolution approach , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

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

[43]  Junjie Yang,et al.  A novel strategy of pareto-optimal solution searching in multi-objective particle swarm optimization (MOPSO) , 2009, Comput. Math. Appl..

[44]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[45]  N. J. DE VOS,et al.  Multi-objective performance comparison of an artificial neural network and a conceptual rainfall—runoff model , 2007 .

[46]  Tienfuan Kerh,et al.  Neural networks forecasting of flood discharge at an unmeasured station using river upstream information , 2006, Adv. Eng. Softw..

[47]  W. J. Shuttleworth,et al.  Parameter estimation of a land surface scheme using multicriteria methods , 1999 .

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

[49]  Marco Laumanns,et al.  Performance assessment of multiobjective optimizers: an analysis and review , 2003, IEEE Trans. Evol. Comput..