Document Type
Presentation
Publication Date
8-1-2014
Abstract
We have developed a web-based, interactive, watershed planning system called WRESTORE (Watershed Restoration Using Spatio-Temporal Optimization of Resources) (http://wrestore.iupui.edu) that allows stake-holder communities to participate in a democratic, collaborative form of optimization process for designing best management practices (BMPs) on their landscape, while also optimizing based on subjective, qualitative landowners’ criteria beyond the usual socio-economic, physical, and ecological criteria. This system utilizes multiple advanced computational approaches including the SWAT (Soil and Water Assessment Tool) hydrologic model for watershed simulations, interactive genetic algorithms and reinforcement-based machine learning algorithms for search and optimization, and deep learning artificial neural networks for user modeling, within an encompassing human-computer interaction framework. A substantial user study of the WRESTORE system was conducted recently involving multiple real stakeholders varying from consultants, government officials, watershed alliance members, etc., with the objective of gaining insight about WRESTORE’s usability and utility. In particular focus was the user modeling component that develops a computational model of a user’s preferences and criteria, based on real-time user-provided ratings for a subset of possible designs (similar to the idea of user profiling commonly done for Information Filtering Systems). The user model constructed based on the real user’s personalized feedbacks can then be used to influence the automated search for and optimization of BMP alternatives in WRESTORE. In this paper, we describe the overall WRESTORE system architecture, the methods developed for user modeling for interactive optimization, and the experimental set-up as well as results with real user studies. These results clearly demonstrate that development of user models for such personalized, interactive optimization is both feasible and valuable for developing community-based computational water sustainability solutions.
Comments
Session R70, Hydrology and Socio-Economic Aspects