Session: DAC-14-01-Metamodel-Based Design Optimization

Paper Number: 88163

88163 - Surrogate Models and Time Series for Flow Prediction on the Red River Dam Network 

Surrogate models have been used to replace computationally expensive analysis models in engineering design problems. However, time-dependent variables and historical data are usually ignored in the surrogate modeling process. For instance, in a dam network design, using hydraulic simulations to estimate the water flow is computationally expensive, and the data is in the form of time series. So, we need time-dependent surrogate models to replace these simulations and manage this computational complexity. In this paper, we describe surrogate models to predict the amount of water flow into a reservoir. The challenge is that the flow is a time-dependent variable, and we need to incorporate time-series into surrogate models. Thus, there are three contributions: (1) using surrogate modeling to predict flow for dam network design, (2) incorporating time series analysis in surrogate models, (3) using an ensemble of surrogates to increase the accuracy of prediction. We also demonstrate how to integrate surrogate models and machine learning with time series analysis for more accurate and faster prediction. Due to the availability of data, we use the Buffalo Reservoir on the Red River Basin as an example. Based on the time series data for flow, evaporation, precipitation, and maximum and minimum temperature, three surrogate models are used to examine the impact of integrating time series into surrogate models.  These are multivariate autoregressive integrated moving average (MARIMA), a classic time series analysis method; artificial neural network (ANN), and random forest (RF) methods, two machine learning surrogate models. We use seven different time lags as features within an RF model, as an ensemble of surrogate models, and predict the flow for seven-time steps ahead. We successfully incorporate the time series data and particularly the concept of the time lag within surrogate models. We show that RF as the ensemble of surrogates provides more accurate predictions than the other two surrogate models. Although this method has been demonstrated for the Red River Basin, it could also be applied to designing anything in which time-dependent flow is an issue, for example, in biomedical applications, the management of manufacturing processes and product sales as well as any products in which fluid flow is an issue.

Presenting Author: Reza Alizadeh University of Oklahoma

Presenting Author Biography: Reza Alizadeh is a doctoral candidate in Industrial and Systems Engineering at the University of Oklahoma.

Authors:

Reza Alizadeh University of Oklahoma
Janet K. Allen Univ of Oklahoma
Farrokh Mistree University of Oklahoma