Optimal sensors placement for flood forecasting modelling

Abstract Numerical models are instrumental to more effective flood forecasting and managementservices though they suffer from numerous uncertaintysources. An effective model calibration is hence essential. In this research work,a methodology of optimal sampling design has been investigated and developed forwater drainage networks. Optimal hydrometer sensors locationsalong the Amato River (South Italy)have been defined by optimizing a two-objective function that maximizes the calibrated model accuracy and minimizesthe total metering cost. This problem has been solved by using an enumerative search solution, run on the ENEA/CRESCO HPC infrastructure, evaluating the exact Pareto-front byefficient computational time.

[1]  Atulya K. Nagar,et al.  Observability Analysis of Water Distribution Systems Under Parametric and Measurement Uncertainty , 2000 .

[2]  Werner de Schaetzen,et al.  Optimal sampling design for model calibration using shortest path, genetic and entropy algorithms , 2000 .

[3]  Zoran S. Kapelan,et al.  A hybrid inverse transient model for leakage detection and roughness calibration in pipe networks , 2003 .

[4]  H. Ishibuchi,et al.  MOGA: multi-objective genetic algorithms , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[5]  Hussein A. Abbass,et al.  MBO: marriage in honey bees optimization-a Haplometrosis polygynous swarming approach , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[6]  G. Yu,et al.  Optimized pump scheduling in water distribution systems , 1994 .

[7]  A Hjelmfelt,et al.  Chemical implementation of neural networks and Turing machines. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[8]  T. Walski.  Technique for Calibrating Network Models , 1983 .

[9]  James G. Uber,et al.  Sampling Design Methods for Water Distribution Model Calibration , 1998 .

[10]  Roger W. Meier,et al.  Sampling Design for Network Model Calibration Using Genetic Algorithms , 2000 .

[11]  Gary D. Bader,et al.  The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function , 2010, Nucleic Acids Res..

[12]  Lindell Ormsbee.  Implicit Network Calibration , 1989 .

[13]  D. Savić,et al.  Multiobjective design of water distribution systems under uncertainty , 2005 .

[14]  M. D. McKay,et al.  A comparison of three methods for selecting values of input variables in the analysis of output from a computer code , 2000 .

[15]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[16]  Valério D. Pillar,et al.  On the identification of optimal plant functional types , 1999 .

[17]  Kourosh Behzadian,et al.  Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks , 2009, Environ. Model. Softw..

[18]  P. Lansley,et al.  The promise and challenge of providing assistive technology to older people. , 2001, Age and ageing.

[19]  Stratton,et al.  Outcome of transphenoidal surgery for acromegaly and its relationship to surgical experience , 1999, Clinical endocrinology.