Abstract
A mixed approach based on the combination of deterministic physically based models and probabilistic data-driven models for flood forecasting is presented. The approach uses a Bayesian network built upon the results of a deterministic rainfall–runoff model for real-time decision support. The data set for the calibration and validation of the Bayesian model is obtained through a Monte Carlo simulation technique, combining a stochastic rainfall generator and a deterministic rainfall–runoff model. The methodology allows making probabilistic discharge forecasts in real time using an uncertain quantitative precipitation forecast. The validation experiments made show that the data-driven model can approximate the probability distribution of future discharge that would be obtained with the physically based model applying ensemble prediction techniques, but in a much shorter time.
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References
Beven KJ, Binley A (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrological Processes 6: 279–298.
Brath A, Rosso R (1993) Adaptive calibration of a conceptual model for flash flood forecasting. Water Resources Research 29(8): 2561–2572.
Cooper GF (1990) The computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence 42: 393–405.
Cuena J, Molina M (1999) A multi-agent system for emergency management in floods, in Multiple Approaches to Intelligent Systems, Iman I, Kodratoff Y, El Dessouki A, Ali M (eds) Lecture Notes in Computer Science Vol. 1611, Springer, pp. 460–469.
Dagum P, Luby M (1993) Approximating probabilistic inference in Bayesian belief networks is NP-hard. Artificial Intelligence 60(1): 141–153.
Day GN (1985) Extended streamflow forecasting using NWSRFS. Journal of Water Resources Planning and Management 111: 157–170.
Friedman N, Nachman, I, Dana P (1999) Learning Bayesian network structure from massive datasets: The Sparse Candidate algorithm. Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pp. 206–215.
Garrote L, Molina M (2004) A framework for making probabilistic forecasts using deterministic rainfall-runoff models, in Hydrological Risk: Recent Advances in Peak River Flow Modelling, Prediction and Real-time Forecasting, Brath A, Montanari A, Toth E, (eds), Bios, pp. 239–246
Heckerman D (1991) Probabilistic Similarity Networks. MIT Press, Cambridge, Massachusetts.
Herskovitz EH, Cooper GF (1990) Kutató: An entropy-driven system for the construction of probabilistic expert systems from data. Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, pp. 54–62.
Lauritzen SL, Spiegelhalter DJ (1988) Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society B 50(2): 157–224.
Mockus V (1972) Estimation of direct runoff from storm rainfall, in National Engineering Handbook. Part 630: Hydrology, NRCS, pp. 10.1–10.24.
Molina M, Fuentetaja R, Garrote L (2005) Hydrologic models for emergency decision support using Bayesian networks, in Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Godo L (ed) Lecture Notes in Computer Science 3571: 88–99.
Neapolitan RE (1990) Probabilistic Reasoning in Expert Systems. Theory and Algorithms, Wiley.
Pearl J (1988) Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann.
Shwe M, Cooper G (1991) An empirical analysis of likelihood-weighting simulation on a large, multiply connected medical belief network. Computer and Biomedical Research 24(5): 453–475.
Snider D (1973) Hydrographs, in National Engineering Handbook. Part 630: Hydrology, NRCS, pp. 16.1–16.23.
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Garrote, L., Molina, M., Mediero, L. (2009). Learning Bayesian Networks from Deterministic Rainfall–Runoff Models and Monte Carlo Simulation. In: Abrahart, R.J., See, L.M., Solomatine, D.P. (eds) Practical Hydroinformatics. Water Science and Technology Library, vol 68. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79881-1_27
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DOI: https://doi.org/10.1007/978-3-540-79881-1_27
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