AUTO-CALIBRATION OF DAILY AND HOURLY TANK MODEL’S PARAMETERS USING GENETIC ALGORITHM
DOI:
https://doi.org/10.11113/mjce.v23.15815Keywords:
Hydrological Tank model, Global Optimization Methods (GOMs), Genetic Algorithm (GA), Rainfall-runoff model.Abstract
The only calibration method for Hydrologic Tank model in early days is using trialand-error. This method required much time and effort for obtaining better results since a large number of parameters need to be calibrated. Therefore, various Global Optimization Methods (GOMs) have been applied to optimize Tank model parameters automatically. In this study, genetic algorithm was introduced to auto-calibrate daily and hourly Tank model parameters. The selected study area is Bedup Basin, Samarahan, Sarawak, Malaysia. Input data used for both daily and hourly model calibration are rainfall and runoff only. The accuracy of the simulation results are measured using Coefficient of Correlation (R) and Nash-Sutcliffe Coefficient (E2 ). The robustness of the model parameters obtained are further analyzed by boxplots analysis. Peak errors are also evaluated for hourly runoff simulation. Results show that GA method is able to obtain optimal values for ten parameters fast and accurate within a multidimensional parameter space that could provide the best fit between the observed and simulated runoff.References
Chen, R.S., Pi, L.C., and Hsieh, C.C. (2005). A Study on Automatic Calibration of Parameters in Tank Model. Journal of the American Water Resources Association (JAWRA).
Cooper, V.A., V.T.V. Nguyen, and J.A. Nicell (1997). Evaluation of Global Optimization Methods for Conceptual Rainfall-Runoff Model Calibration. Water Sci. Technol, 36(5):53-60.
Cooper, V.A., V.T.V. Nguyen, and J.A. Nicell (2007). Calibration of Conceptual Rainfall Runoff Models using Global Optimisation Methods with Hydrologic Process-Based Parameter Constraints. Journal of Hydrology, Elsevier.
DID (2004). Hydrological Year Book Year 2004. Department of Drainage and Irrigation Sarawak, Malaysia.
DOA (1975). Jabatan Pertanian Malaysia. Scale 1:50,000
Gen, M.S. and Cheng, R.W. (1997). Genetic Algorithm And Engineering Design. John Willey and sons, inc.
Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor.
JUPEM (1975). Jabatan Ukur dan Pemetaan Malaysia. Scale 1:50,000
Kawasaki, R. (2003). Application of Synthetic Tank Model Simulation on the Area with Poor Basic Hydrological Data Availability. Japan Society of Shimanto Policy and Integrated
River Management, Vol 2-2, pp 18-19.
Kuczera, G. (1997). Efficient subspace probabilistic parameter optimisation for catchment models. Wat. Resour. Res., 33(1), 177-185.
Kuok, K.K., Harun, S., and Shamsudin, S.M. (2007). Optimization of Tank Model’s Parameters Using Global Optimization Methods (GOMs). Proceeding of 2nd International Conference on Managing Rivers in the 21st Century: Solution Towards Sustainable River Basins (Rivers’07), 6th-8
th June 2007, Kuching, Sarawak.
Kuok, K.K., Harun, S., Shamsudin, S.M., and Chiu, P.C. (2008). Tank Model’s Parameters Optimization Using Particle Swarm Optimization (PSO) Technique and Shuffle Complex
Evolution (SCE) Technique. Proceeding of Curtin University of Technology Science and Engineering International Conference (CUTSE’08), 24th - 27th November 2008, Miri,
Sarawak.
Kuok, K.K., Harun, S., Shamsudin, S.M., and Chiu, P.C. (2009). Optimization of Tank Model’s Parameters Using Particle Swarm Optimization approach (PSO). Proceeding of International
Conference on Engineering and Education in the 21st Century (ICEE), 23rd - 25th Mac 2009, Kuching, Sarawak.
Kuok, K.K., Harun, S., and Shamsudin, S.M. (2010), Global Optimization Methods for Calibration and Optimization of the Hydrologic Tank Model’s Parameters. Canadian Journal on Civil Engineering, Vol. 1, No. 1, Feb. 2010. AM Publishers
Kuok, K.K., Chiu, P.C., and Harun, S. (2011). Multiobjective Particle Swarm Optimization for optimizing Daily Tank Model’s Parameters. Proceeding of ASEAN Australian Engineering
Congress, 25th -27th July 2011, Kuching.
Lauzon, N., Rousselle, J., Birikundavyi, S., and Trung, H.T. (2000). Real-time Daily Flow Forecasting Using Black-box Models, Diffusion Processes and Neural Networks. Can. J. Civ. Eng. 27: 671-682.
Sorooshian, S., Q. Duan, and V.K. Gupta (1993). Calibration of Rainfall-Runoff Models: Global Optimization to the Sacramento Soil Moisture Accounting Model. Water Resources Research 29(4):1185-1194.
Sugawara M and Funiyuki M. (1956). A Method of Revision of the River Discharge by Means of a Rainfall Model. Collection of Research Papers about Forecasting Hydrologic variables:
-18.
Yapo, P. O., Gupta, H. V., Sorooshian, S. (1996). Automatic calibration of conceptual rainfallrunoff models: sensitivity to calibration data. J. Hydrol., 181, 23-48.
Wang QJ.(1991). The Genetic Algorithm and Its Application to Calibrating Conceptual Rainfallrunoff Models. Water Resources Research 27(9): 2467-2471.
