Abstract
Risk management for natural disasters that focuses on early warning, dynamic scheduling and aftermath evaluation, has been one of the key technologies in the field of disaster prevention and mitigation. Some water-related disasters have common characteristics, which are usually generalized and then applied for establishing an integrated model to cope with these disasters. The rapid development of the artificial intelligence (AI) technique makes it possible to enhance the model with intelligent and cooperative features. Such type of model is firstly proposed in this chapter and then derived into two instances, which aim to solve problems in drought evaluation and water scheduling of reservoirs, respectively. The former is based on the radial base function neural network (RBFNN) and the later takes an improved particle swarm optimization (I-PSO) algorithm as its carrier for implementation. Simulation results demonstrate that the first model can make full use of the spatial and time data of the drought and high accuracy of evaluation and classification of the drought severity can therefore be acquired. The second model can distribute the water storage among the reservoirs timely and efficiently, which is of great significance of eliminating the damage of the seasonal droughts and floods occurred in the tributary.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hubbard, D.: The Failure of Risk Management: Why It’s Broken and How to Fix It, p. 46. John Wiley & Sons (2009)
Xuan, F.Z.: Artificial intelligence and integrated intelligent information systems. United States of America by Idea Group Publishing (February 2006)
Zadeh, L.A.: Fuzzy sets. Information and Control 8(3), 338–353 (1965)
Zhou, H.-C., Zhang, D.: Assessment Model of Drought and Flood Disasters with Variable Fuzzy Set Theory. Transaction of the Chinese Society of Agricultural Engineering (CSAE) 9, 56–61 (2009)
Moody, J., Darken, C.J.: Fast Learning in Networks of Locally Tuned Processing Units. Neural Computation 1, 281–294 (1989)
Wei, H.-K., Amari, S.-I.: Dynamics of Learning Near Singularities in Radial Basis Function Networks. Neural Networks 7, 989–1005 (2008)
Srinivasan, D., Ng, W.S., Liew, A.C.: Neural-Networked-Based Signature Recognition for Harmonic Source Identification. IEEE Transactions on Power Delivery 1, 398–405 (2006)
Lee, J., Sankar, R.: Theoretical Derivation of Minimum Mean Square Error of RBF Based Equalizer. Signal Processing 7, 1613–1625 (2007)
Rank, E.: Application of Bayesian Trained RBF Networks to Nonlinear Time-Series Modeling. Signal Processing 7, 1393–1410 (2003)
Broomhead, D.S., Lowe, D.: Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks. RSRE-MEMO-4148. Defense Research Information Center, Orpington, England (1988)
Constantinopoulos, C., Likas, A.: An Incremental Training Method for the Probabilistic RBF Networks. IEEE Transactions on Neural Networks 4, 966–974 (2006)
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43 (1995)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. IEEE Int’l Conf. on Neural Networks, vol. IV, pp. 1942–1948. IEEE Press, Piscataway (1995)
Villanova, L., et al.: Functionalization of microarray devices: Process optimization using a multiobjective PSO and multiresponse MARS modeling. In: 2010 IEEE Congress on Evolutionary Computation, CEC (2010)
Alipoor, M., Parashkoh, M.K., Haddadnia, J.: A novel biomarker discovery method on protemic data for ovarian cancer classification. In: 2010 18th Iranian Conference on Electrical Engineering, ICEE (2010)
Goken, C., Gezici, S., Arikan, O.: Optimal signaling and detector design for power-constrained binary communications systems over non-gaussian channels. IEEE Communications Letters 14(2), 100–102 (2010)
Huang, H., Tsai, C.: Particle swarm optimization algorithm for optimal configurations of an omnidirectional mobile service robot. In: Proceedings of SICE Annual Conference 2010 (2010)
Zhao, B., Guo, C.X., Cao, Y.J.: A multiagent-based particle swarm optimization approach for optimal reactive power dispatch. IEEE Transactions on Power Systems 20(2), 1070–1078 (2005)
Kanakasabapathy, P., Swarup, K.S.: Evolutionary Tristate PSO for Strategic Bidding of Pumped-Storage Hydroelectric Plant. IEEE Transactions on Systems, Man, and Cybernetics. Part C: Applications and Reviews 40(4), 460–471 (2010)
Luo, Y., Wang, W., Zhou, M.: Study on optimal scheduling model and technology based on RPSO for small hydropower sustainability. In: International Conference on Sustainable Power Generation and Supply, SUPERGEN 2009 (2009)
Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 69–73. IEEE Press, Piscataway (1998)
Liang, J.J., Qin, A., Suganthan, K.P.N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295 (2006)
Li, H.R., Gao, Y.L.: Particle Swarm Optimization Algorithm with Adaptive Threshold Mutation. In: 2009 International Conference on Computational Intelligence and Security (2009)
Xie, X.F., Zhang, W.J., Yang, Z.L.: Adaptive Particle Swarm Optimization on Individual Level. In: International Conference on Signal Processing (ICSP), Beijing, China, pp. 1215–1218 (2002)
Li, X.D., Engelbrecht, A.P.: Particle swarm optimization: An introduction and its recent developments. In: Proc. Genetic Evol. Comput. Conf., pp. 3391–3414 (2007)
Shi, Y., Eberhart, R.C.: Empirical study of particle swarm optimization. In: Proc. of Congress on Computational Intelligence, Washington DC, USA, pp. 1945–1950 (1999)
Liu, H., Abraham, A., Zhang, W.: A fuzzy adaptive turbulent particle swarm optimation. International Journal of Innovative Computing and Applications 1(1), 39–47 (2007)
Pant, M., Radha, T., Singh, V.P.: A new diversity based particle swarm optimization with Gaussian mutation. J. of Mathematical Modeling, Simulation and Applications 1(1), 47–60 (2008)
Pant, M., Thangaraj, R., Abraham, A.: Particle swarm optimization using adaptive mutation. In: 19th International Conference on Database and Expert Systems Application, pp. 519–523 (2008)
Jiang, Y., Hu, T.S., Huang, C.C., Wu, X.N.: An improved particle swarm optimization algorithm. Applied Mathematics and Computation 193, 231–239 (2007)
Yang, X.M., Yuan, J.S., Yuan, J.Y., Mao, H.N.: A modified particle swarm optimizer with dynamic adaptation. Applied Mathematics and Computation 189, 1205–1213 (2007)
Ho, S.Y., Lin, H.S., Liauh, W.H., Ho, S.J.: Orthogonal particle swarm optimization and its application to task assignment problems. IEEE Trans. Syst., Man, Cybern. A, Syst., Humans 38(2), 288–298 (2008)
Liu, B., Wang, L., Jin, Y.H.: An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Trans. Syst., Man, Cybern. B, Cybern. 37(1), 18–27 (2007)
Ciuprina, G., Ioan, D., Munteanu, I.: Use of intelligent-particle swarm optimization in electromagnetic. IEEE Trans. Magn. 38(2), 1037–1040 (2002)
Niu, B., Zhu, Y.L., He, X.X., et al.: An Improved Particle Swarm Optimization Based on Bacterial Chemotaxis. In: Proc. IEEE Congress on Intelligent Control, Dalian, China, pp. 3193–3197 (2006)
Thangaraj, R., Pant, M., Abraham, A.: A new diversity guided particle swarm optimization with mutation. In: World Congress on Nature & Biologically Inspired Computing, NaBIC 2009 (2009)
Engelbrecht, A.P.: Fundamentals of Computational Swarm Intelligence. John Wiley & Sons Ltd., England (2005)
Chang, G.W., et al.: Experiences with mixed integer linear programming based approaches on short-term hydro scheduling. IEEE Transactions on Power Systems 16(4), 743–749 (2001)
Xuan, Y., Mei, Y., Xu, J.: Research of Hydropower Stations Optimal Operation Based on the Discrete Differential Dynamic Programming - Progressive Optimization Algorithm Combination Method. In: 7th ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2009 (2009)
Leite, P.T., Carneiro, A.A.F.M., Carvalho, A.C.P.L.: Hybrid Genetic Algorithm Applied to the Determination of the Optimal Operation of Hydrothermal Systems. In: Ninth Brazilian Symposium on Neural Networks, SBRN 2006 (2006)
Orero, S.O., Irving, M.R.: A genetic algorithm modelling framework and solution technique for short term optimal hydrothermal scheduling. IEEE Transactions on Power Systems 13(2), 501–518 (1998)
Hu, X.H., Eberhart, R.C.: Adaptive particle swarm optimization: detection and response to dynamic systems. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Ding, YS., Liang, X., Cheng, LJ., Wang, W., Li, RF. (2012). An Integrated Intelligent Cooperative Model for Water-Related Risk Management and Resource Scheduling. In: Lu, J., Jain, L.C., Zhang, G. (eds) Handbook on Decision Making. Intelligent Systems Reference Library, vol 33. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25755-1_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-25755-1_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25754-4
Online ISBN: 978-3-642-25755-1
eBook Packages: EngineeringEngineering (R0)