Fitting hydrological models on multiple responses using the multiobjective evolutionary annealing simplex approach

A. Efstratiadis, and D. Koutsoyiannis, Fitting hydrological models on multiple responses using the multiobjective evolutionary annealing simplex approach, Practical hydroinformatics: Computational intelligence and technological developments in water applications, edited by R.J. Abrahart, L. M. See, and D. P. Solomatine, 259–273, doi:10.1007/978-3-540-79881-1_19, Springer, 2008.

[doc_id=796]

[English]

Most complex hydrological modelling schemes, when calibrated on a single observed response (e.g. river flow at a point), provide poor predictive capability, due to the fact that the rest of variables of basin response remain practically uncontrolled. Current advances in modelling point out that it is essential to take into account multiple fitting criteria, which correspond to different observed responses or to different aspects of the same response. This can be achieved through multiobjective calibration tools, thus providing a set of solutions rather than a single global optimum. Besides, actual multiobjective optimization methods are rather inefficient, when real-world problems with many criteria and many control variables are involved. In hydrological applications there are some additional issues, due to uncertainties related to the representation of complex processes and the observation errors. The multiobjective evolutionary annealing-simplex (MEAS) method implements an innovative scheme, particularly developed for the optimization of such problems. Its features and capabilities are illustrated by solving a challenging parameter estimation problem, dealing with hydrological modelling and water resources management in a karstic basin in Greece.

Full text is only available to the NTUA network due to copyright restrictions

See also: http://dx.doi.org/10.1007/978-3-540-79881-1_19

Our works referenced by this work:

1. A. Efstratiadis, and D. Koutsoyiannis, An evolutionary annealing-simplex algorithm for global optimisation of water resource systems, Proceedings of the Fifth International Conference on Hydroinformatics, Cardiff, UK, 1423–1428, doi:10.13140/RG.2.1.1038.6162, International Water Association, 2002.
2. A. Efstratiadis, D. Koutsoyiannis, and D. Xenos, Minimising water cost in the water resource management of Athens, Urban Water Journal, 1 (1), 3–15, 2004.
3. E. Rozos, A. Efstratiadis, I. Nalbantis, and D. Koutsoyiannis, Calibration of a semi-distributed model for conjunctive simulation of surface and groundwater flows, Hydrological Sciences Journal, 49 (5), 819–842, doi:10.1623/hysj.49.5.819.55130, 2004.
4. A. Efstratiadis, E. Rozos, A. Koukouvinos, I. Nalbantis, G. Karavokiros, and D. Koutsoyiannis, An integrated model for conjunctive simulation of hydrological processes and water resources management in river basins, European Geosciences Union General Assembly 2005, Geophysical Research Abstracts, Vol. 7, Vienna, 03560, doi:10.13140/RG.2.2.27930.64960, European Geosciences Union, 2005.
5. A. Efstratiadis, and D. Koutsoyiannis, The multiobjective evolutionary annealing-simplex method and its application in calibrating hydrological models, European Geosciences Union General Assembly 2005, Geophysical Research Abstracts, Vol. 7, Vienna, 04593, doi:10.13140/RG.2.2.32963.81446, European Geosciences Union, 2005.
6. E. Rozos, and D. Koutsoyiannis, A multicell karstic aquifer model with alternative flow equations, Journal of Hydrology, 325 (1-4), 340–355, 2006.
7. A. Efstratiadis, Non-linear methods in multiobjective water resource optimization problems, with emphasis on the calibration of hydrological models, PhD thesis, 391 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, Athens, February 2008.

Our works that reference this work:

1. A. Efstratiadis, I. Nalbantis, A. Koukouvinos, E. Rozos, and D. Koutsoyiannis, HYDROGEIOS: A semi-distributed GIS-based hydrological model for modified river basins, Hydrology and Earth System Sciences, 12, 989–1006, doi:10.5194/hess-12-989-2008, 2008.
2. A. Efstratiadis, and D. Koutsoyiannis, On the practical use of multiobjective optimisation in hydrological model calibration, European Geosciences Union General Assembly 2009, Geophysical Research Abstracts, Vol. 11, Vienna, 2326, doi:10.13140/RG.2.2.10445.64480, European Geosciences Union, 2009.
3. A. Efstratiadis, and D. Koutsoyiannis, One decade of multiobjective calibration approaches in hydrological modelling: a review, Hydrological Sciences Journal, 55 (1), 58–78, 2010.
4. I. Tsoukalas, P. Kossieris, A. Efstratiadis, and C. Makropoulos, Surrogate-enhanced evolutionary annealing simplex algorithm for effective and efficient optimization of water resources problems on a budget, Environmental Modelling and Software, 77, 122–142, doi:10.1016/j.envsoft.2015.12.008, 2016.

Other works that reference this work (this list might be obsolete):

1. #Solomatine, D. L.M. See and R.J. Abrahart, Data-driven modelling: concepts, approaches and experiences, Practical hydroinformatics , ed. by R.J. Abrahart, L. M. See, and D. P. Solomatine, 33-47, Springer, doi:10.1007/978-3-540-79881-1_2, 2008.
2. Pollacco, J. A. P., and B. P. Mohanty, Uncertainties of water fluxes in SVAT models: inverting surface soil moisture and evapotranspiration retrieved from remote sensing, Vadose Zone Journal, 11(3), vzj2011.0167, 2012.
3. Dumedah, G., Formulation of the evolutionary-based data assimilation and its implementation in hydrological forecasting, Water Resources Management, 26(13), 3853-3870, 2012.
4. Dumedah, G., and P. Coulibaly, Evaluating forecasting performance for data assimilation methods: the Ensemble Kalman Filter, the Particle Filter, and the Evolutionary-based assimilation, Advances in Water Resources, 60, 47-63, 2013.
5. Gharari, S., M. Hrachowitz, F. Fenicia, H. Gao, and H. H. G. Savenije, Using expert knowledge to increase realism in environmental system models can dramatically reduce the need for calibration, Hydrology and Earth System Sciences, 18, 4839-4859, doi:10.5194/hess-18-4839-2014, 2014.
6. Ho, V.H., I. Kougias, and J.H. Kim, Reservoir operation using hybrid optimization algorithms, Global Nest Journal, 17 (1), 103-117, 2015.
7. Tigkas, D., V. Christelis, and G. Tsakiris, Comparative study of evolutionary algorithms for the automatic calibration of the Medbasin-D conceptual hydrological model, Environmental Processes, 3(3), 629–644, doi:10.1007/s40710-016-0147-1, 2016.
8. Laura, R., L. L. Matthieu, G. Federico, L. M. Nicolas, H. Frédéric, M. Céline, and R. Pierre, Impact of mesoscale spatial variability of climatic inputs and parameters on the hydrological response, Journal of Hydrology, doi:10.1016/j.jhydrol.2017.07.037, 2017.

Tagged under: Optimization