## Abstract

For computational purposes, a stochastic groundwater management model with minimizing value z* is often approximately solved by randomly sampling n realizations of the model's stochastic parameters, and then solving the resulting `approximating problem' for (x*_{n}, z*_{n}). It can be shown that, in expectation, z*_{n} is a lower bound on z* and that this lower bound monotonically improves as n increases. Using this result, an approach based on Monte Carlo sampling and solution of multiple instances of the n-scenario approximating problem is applied to construct confidence intervals on the optimality gap for any candidate solution x, such as x = x*_{n}. A sampling procedure based on common random numbers ensures non-negative estimates of the optimality gap and provides significant variance reduction over naive sampling on a test problem involving the location of pumping wells for contaminant plume containment.

Original language | English (US) |
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Title of host publication | Computational Methods in Contamination and Remediation of Water Resources |

Editors | V.N. Burganos, G.P. Karatzas, A.C. Payatakes, C.A. Brebbia, W.G. Gray, G.F. Pinder |

Publisher | Computational Mechanics Publ |

Pages | 67-74 |

Number of pages | 8 |

Volume | 1 |

State | Published - Jan 1 1998 |

Event | Proceedings of the 1998 12th International Conference on Computational Methods in Water Resources, CMWR XII'98. Part 1 (of 2) - Crete, Greece Duration: Jun 1 1998 → Jun 1 1998 |

### Other

Other | Proceedings of the 1998 12th International Conference on Computational Methods in Water Resources, CMWR XII'98. Part 1 (of 2) |
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City | Crete, Greece |

Period | 6/1/98 → 6/1/98 |

## ASJC Scopus subject areas

- General Engineering