“In-Process Type” Dynamic Muskingum Model Parameter Estimation Method

This paper discusses the Muskingum model as a novel parameter estimation method. Sixty representative floods over the past four decades serve as research objects; a linear Muskingum model and Pigeon-inspired optimization (PIO) algorithm are used to obtain the parameters of each flood. The proposed “in-process type” dynamic parameter estimation (IP-DPE) method is used to establish the characteristic attributes set of 50 floods. The characteristic attributes set refers to a set of parameters that could describe the shape, magnitude, and duration of the flood before flood peak; they are the input, whereas parameters K and x of each flood are the output to establish a Neural Network model. Then we input flood characteristic attributes to obtain flood parameters when estimating flood parameters practically. Ten floods were used to test the parameter estimation and flood routing efficacy. The results show that the IP-DPE method can quickly identify parameters and facilitate accurate river flood forecasting.

[1]  Zhang Gang.  Parameter adaptive Muskingum method of flood routing , 2011 .

[2]  Zhang Daifeng.  Evaluation of sustainable use of water resources in Wenshan based on AHP-BP model , 2013 .

[3]  P. Kitanidis,et al.  Real‐time forecasting with a conceptual hydrologic model: 2. Applications and results , 1980 .

[4]  Stephen Roberts,et al.  Positional entropy during pigeon homing II: navigational interpretation of Bayesian latent state models. , 2004, Journal of theoretical biology.

[5]  K. Srinivasa Raju,et al.  Extended Muskingum method for flood routing , 2011 .

[6]  Xiang Zhou,et al.  Computer-aided molecular design with BP-ANN and global optimization algorithm , 2003 .

[7]  Chen Le.  Prediction of sulfur solubility in high sulfur gas based on genetic algorithm and LM-BP artificial neural network , 2014 .

[8]  Kenli Li,et al.  Hybrid particle swarm optimization for parameter estimation of Muskingum model , 2014, Neural Computing and Applications.

[9]  Peter K. Kitanidis,et al.  Real‐time forecasting with a conceptual hydrologic model: 1. Analysis of uncertainty , 1980 .

[10]  Xu Jie.  Application of ant colony algorithm to parameter estimation of Muskingum Routing model , 2005 .

[11]  Y. Fei,et al.  Progresses in Pigeon鄄inspired Optimization Algorithms , 2017 .

[12]  Lin Jian-yi.  Bayesian probabilistic forecasting model based on BP ANN , 2006 .

[13]  Aman Mohammad Kalteh,et al.  Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform , 2013, Comput. Geosci..

[14]  Xiaohua Yang,et al.  Optimal parameter estimation for Muskingum model based on Gray-encoded accelerating genetic algorithm , 2007 .

[15]  T. O'donnell.  A direct three-parameter Muskingum procedure incorporating lateral inflow / Methode directe de Muskingum a trois parametres avec prise en compte des apports lateraux , 1985 .

[16]  Alvaro A. Aldama.  Least-Squares Parameter Estimation for Muskingum Flood Routing , 1990 .

[17]  Yingying Gu,et al.  MTF estimation via BP neural networks and Markov model for space optical camera , 2013, J. Frankl. Inst..

[18]  Chao-kui Li,et al.  Study on Spatial Distribution of Soil Heavy Metals in Huizhou City Based on BP--ANN Modeling and GIS , 2011 .

[19]  Niu Cun-wen.  Application of multi-agent genetic algorithm to parameter estimation of Muskingum Model , 2007 .

[20]  Konstantine P. Georgakakos,et al.  Real‐Time Flash Flood Prediction , 1987 .

[21]  A. Moghaddam,et al.  Parameters Estimation for the New Four-Parameter Nonlinear Muskingum Model Using the Particle Swarm Optimization , 2016, Water Resources Management.

[22]  Zhong Deng.  TIME SERIES NEURAL NETWORK MODEL FOR HYDROLOGIC FORECASTING , 2001 .

[23]  Vijay P. Singh,et al.  Some notes on Muskingum method of flood routing , 1980 .

[24]  Chuntian Cheng,et al.  Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall–runoff model calibration , 2002 .

[25]  Patrick Matgen,et al.  Water Level Estimation and Reduction of Hydraulic Model Calibration Uncertainties Using Satellite SAR Images of Floods , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[26]  A. WHITEN.  Operant Study of Sun Altitude and Pigeon Navigation , 1972, Nature.

[27]  M. A. Gill.  Flood routing by the Muskingum method , 1978 .

[28]  Shu Dan-dan.  Parameter Estimation Method of Nonlinear Muskingum Model Based on PSO , 2007 .

[29]  Jaewan Yoon,et al.  Parameter Estimation of Linear and Nonlinear Muskingum Models , 1993 .

[30]  B. Rajagopalan,et al.  Optimal parameter estimation for Muskingum routing with ungauged lateral inflow , 1995 .

[31]  M. Hino.  Runoff Forecasts by Linear Predictive Filter , 1970 .

[32]  Haibin Duan,et al.  Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning , 2014, Int. J. Intell. Comput. Cybern..