Project Research Results
Grantee Research Project Results
Computational Requirements of Statistical Learning within a Decision-Making Framework for Sustainable Technology.EPA Grant Number: R828207
Title: Computational Requirements of Statistical Learning within a Decision-Making Framework for Sustainable Technology.
Investigators: Chen, Victoria C.P. , Chang, Michael E. , Johnson, Ellis L. , Lee, Eva K.Y.
Current Investigators: Chen, Victoria C.P.
Institution: Georgia Institute of Technology
EPA Project Officer: Karn, Barbara
Project Period: July 1, 2000 through June 30, 2003 (Extended to June 30, 2005)
Project Amount: $335,000
RFA: Technology for a Sustainable Environment (1999)
Research Category: Pollution Prevention/Sustainable Development
Description:This research addresses the development of a decision-making framework (DMF) for creating more sustainable urban environments. The development of this DMF requires a novel collaboration of current research in sustainability, optimization, and statistics. The objective of the DMF is to explore hypothetical paradigms on various scales, subject to technical and societal constraints, and measure their effect on other internal and external systems. In particular, the DMF will be instrumental in evaluating databases of emerging technologies and identifying the most promising directions for sustainable solutions. Approach:
The DMF will have the ability to extract key information from environmental deterministic and statistical models that would otherwise be overlooked using current approaches. Decision-makers use models to examine systems and to predict system responses to prescribed stimulants. Those who develop deterministic and statistical models may use the DMF developed in this research to guide them in building an interface between their models and the user. Such an interface will enable the users to utilize the models more efficiently and effectively when exploring how a system responds to a potential action or set of actions. It is expected that the users have a desired optimum course of action either to maximize expected benefits or minimize expected costs while maintaining other state conditions.
The possibility of DMF prototypes in two important arenas will be investigated: (a) Water Quality -- comparison of current and emerging technologies in a wastewater treatment system; and (b) Air Quality -- evaluation of spatial and temporal actions for reducing ground-level ozone pollution. For wastewater treatment, we will consider the downstream subsystem presented by Chen and Beck (Water Sci. Tech. 15:99?112, 1997). Since the necessary modules for the included technologies have already been constructed, we are confident that a DMF for this system will be successful. By contrast, in order to construct an efficient air pollution module, the complex urban airshed encompassing the issue of ozone pollution requires additional exploratory studies via an advanced three-dimensional, photochemical air quality grid model, such as the urban airshed model (UAM, EPA-450/4-90-007A-E, 1990) or the recently released MODELS-3 (www.epa.gov/asmdnerl/models3, 1998).
The DMF will be based on a stochastic dynamic programming (SDP) approach that permits optimization of a system changing over time. Although dynamic programming is proven optimal and has been successful in many applications, it is highly computationally intensive. Development of a DMF for sustainable technology is not straightforward because most environmental problems involve continuous variables that are subject to uncertainty and require the added dimensionality of space (in addition to time). The most promising high-dimensional continuous-state SDP solution method to-date is OA/MARS (Chen et al., Oper. Res. 47:38?53, 1999), which has accurately solved higher-dimensional problems than was previously possible with prior methods.Expected Results:
A critical objective of the proposed research is to develop computationally-practical, high-dimensional, continuous-state SDP solution methods for use within a DMF for sustainable technology. Our methodology will follow along the same lines as the OA/MARS method, which employs orthogonal arrays (OAs) and multivariate adaptive regression splines (MARS, Friedman, Annals of Stat. 19:1?141, 1991). The generalized solution method utilizes statistical experimental designs through which statistical learning within the SDP is achieved. Prior applications of OA/MARS have demonstrated that memory requirements are well within the capacity of modern technology. However, the computational effort required by the learning process (i.e., MARS), although polynomial in growth, may not be practical for very large problems. It should be noted that recent test runs on a 550 MHz Pentium II have demonstrated up to a 15-fold increase in computational speed compared to the original OA/MARS runs on a Sun SPARCstation 10 model 51. The development of statistical learning for this research is separated into four categories: (1) restructuring MARS to reduce computation with minimal loss in learning; (2) investigating other flexible methods of statistical learning, such as artificial neural networks; (3) employing smaller statistical experimental designs, such as Latin hypercube designs; and (4) parallelizing the statistical learning process. A successful DMF for air quality will push the boundaries of SDP research.
Apart from the traditional communication mechanisms of conferences and publication in the open literature, we intend to work closely with modelers affiliated with the Center for Urban and Regional Ecology (CURE) at Georgia Tech. CURE is an integrated team of natural scientists, engineers, economists, city planners, and policy and social scientists from Georgia Tech, the University of Georgia, Georgia State University, and Emory University. CURE's mission is to promote options for human prosperity in a healthy environment while improving air, water, land use, and biodiversity at the scale of regional ecosystems in which cities are embedded. Noted modelers on the team include William Chameides and Armistead "Ted" Russell (air quality), M. Bruce Beck (wastewater), Aris Georgakakos (water resources), and Ronald Cummings (environmental economics). Dr. Michael Chang, co-PI on this proposal, is the CURE research coordinator. CURE will serve as advisor and facilitator for transfer of our DMF to the modeling community.
Our affiliation with CURE may also provide an opportunity to apply the DMF to a real, policy relevant, air quality issue. On March 2, 2000, the Center "kicked off" its three-year, Fall line Air Quality Study (FAQS) to identify the causes of poor air quality in the cities of Augusta, Macon, and Columbus, Georgia. In year three of the study, the FAQS research team will utilize an advanced, three-dimensional photochemical grid model, similar to that used in creating the DMF, to evaluate the effectiveness and efficiency of different control strategies. If the DMF can be implemented in the FAQS at that point, it will allow the FAQS team to evaluate more control options and combinations of options than would otherwise be possible, and provide a platform for demonstrating the real utility of the DMF. This is a fortuitous opportunity upon which we will seek to capitalize.Publications and Presentations:
Publications have been submitted on this project: View all 39 publications for this projectJournal Articles:
Journal Articles have been submitted on this project: View all 5 journal articles for this projectSupplemental Keywords:
pollution prevention, sustainable development, risk management, clean technologies, cost benefit., RFA, Scientific Discipline, Air, Water, Sustainable Industry/Business, Applied Math & Statistics, air toxics, cleaner production/pollution prevention, Mathematics, Wastewater, Sustainable Environment, Technology for Sustainable Environment, Economics and Business, tropospheric ozone, computational simulations, cost reduction, cleaner production, waste reduction, stratospheric ozone, statistical research, wastewater reuse, wastewater treatment plants, stochastic dynamic programming, computer generated alternatives, optimization, sustainable urban environment, water quality, industrial innovations, pollution prevention, source reduction