Stochastic optimization of a mixed moving average process for controlling non-Markovian streamflow environments

TL;DR

This paper develops a stochastic control framework for managing streamflow variability using a jump-driven mixed moving average process, providing explicit solutions and bounds, and demonstrating practical application to river data.

Contribution

It introduces a novel control approach for non-Markovian streamflow models, utilizing a Markovian lift and quadratic solutions for explicit variance control.

Findings

Closed-form control solution with variance bounds

Effective discretization via Markovian lift

Successful application to real river data

Abstract

We investigated a cost-constrained static ergodic control problem of the variance of measure-valued affine processes and its application in streamflow management. The controlled system is a jump-driven mixed moving average process that generates realistic subexponential autocorrelation functions, and the static nature of the control originates from a realistic observability assumption in the system. The Markovian lift was effectively used to discretize the system into a finite-dimensional process, which is easier to analyze. The resolution of the problem is based on backward Kolmogorov equations and a quadratic solution ansatz. The control problem has a closed-form solution, and the variance has both strict upper and lower bounds, indicating that the variance cannot take an arbitrary value even when it is subject to a high control cost. The correspondence between the discretized system based on the Markovian lift and the original infinite-dimensional one is discussed. Then, a convergent Markovian lift is presented to approximate the infinite-dimensional system. Finally, the control problem was applied to real cases using available data for a river reach. An extended problem subject to an additional constraint on maintaining the flow variability was also analyzed without significantly degrading the tractability of the proposed framework.