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Snow water equivalent estimation using blackbox optimization

Stéphane Alarie (alarie.stephane***at***ireq.ca)
Charles Audet (Charles.Audet***at***gerad.ca)
Vincent Garnier (vincent.garnier***at***polymtl.ca)
Sébastien Le Digabel (Sebastien.Le.Digabel***at***gerad.ca)
Louis-Alexandre Leclaire (leclaire.louis-alexandre***at***ireq.ca)

Abstract: Accurate measurements of snow water equivalent (SWE) is an important factor in managing water resources for hydroelectric power generation. SWE over a catchment area may be estimated via kriging on measures obtained by snow monitoring devices positioned at strategic locations. The question studied in this paper is to find the device locations that minimize the kriging interpolation error of the SWE. This is done first by formulating a simulator blackbox that takes a set of locations as inputs and returns the interpolation error, and then to minimize this error using the mesh adaptive direct search (MADS) algorithm designed for blackbox optimization. The fact that the optimization variables represent planar coordinates is used to devise algorithmic strategies that dynamically groups subsets of variables. The methodology is applied to three water-resource systems in the province of Québec on the blackbox simulator and on a surrogate with various grouping strategies.

Keywords: Snow Water Equivalent, Mesh Adaptive Direct Search, Blackbox optimization, Groups of variables, Kriging.

Category 1: Applications -- Science and Engineering

Category 2: Convex and Nonsmooth Optimization (Nonsmooth Optimization )

Category 3: Nonlinear Optimization (Constrained Nonlinear Optimization )

Citation: S. Alarie, C. Audet, V. Garnier, S. Le Digabel, and L.A. Leclaire, Snow water equivalent estimation using blackbox optimization. Pacific Journal of Optimization, 9(1), 1-21, 2013. http://www.ybook.co.jp/online2/oppjo/vol9/p1.html

Download: [PDF]

Entry Submitted: 03/07/2011
Entry Accepted: 03/07/2011
Entry Last Modified: 01/28/2013

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