Robust stochastic control modeling of dam discharge to suppress overgrowth of downstream harmful algae. (27th December 2017)

• Record Type: Journal Article
• Authors: Yoshioka, Hidekazu; Yaegashi, Yuta
• Other Names: Nicolis Orietta guestEditor.; Pascual Rodrigo guestEditor.
• Is Part Of: Applied stochastic models in business and industry. Volume 34:Number 3(2018)
• Page Start: 338
• Page End: 354
• Publication Date: 2017-12-27
• Subjects: Cladophora glomerataKützing -- dynamic programming principle -- flow discharge -- Hamilton‐Jacobi‐Bellman‐Isaacs equation -- model misspecification -- multiplier robust control; Stochastic analysis -- Periodicals; Stochastic processes -- Periodicals; Business mathematics -- Periodicals; Finance -- Mathematical models -- Periodicals; Industrial management -- Mathematical models -- Periodicals; 338.00151923
• DOI: 10.1002/asmb.2301
• Languages: English
• ISSNs: 1524-1904

Abstract

Abstract : The mathematical concept of multiplier robust control is applied to a dam operation problem, which is an urgent issue on river water environment, as a new industrial application of stochastic optimal control. The goal of the problem is to find a fit‐for‐purpose and environmentally sound operation policy of the flow discharge from a dam so that overgrowth of the harmful algae Cladophora glomerata Kützing in its downstream river is effectively suppressed. A minimal stochastic differential equation for the algae growth dynamics with uncertain growth rate is first presented. The performance index to be maximized by the operator of the dam while minimized by nature is formulated within the framework of differential games. The dynamic programming principle leads to a Hamilton‐Jacobi‐Bellman‐Isaacs equation whose solution determines the worst‐case optimal operation policy of the dam, ie, the policy that the operator wants to find. Application of the model to overgrowth suppression of Cladophora glomerata Kützing just downstream of a dam in a Japanese river is then carried out. Values of the model parameters are identified with which the model successfully reproduces the observed population dynamics. A series of numerical experiments are performed to find the most effective operation policy of the dam based on a relaxation of the current policy.