Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 3 of 3
Full-Text Articles in Computer Engineering
The Exploratory Modeling Workbench An Open Source Toolkit For Exploratory Modeling, Scenario Discovery, And (Multi-Objective) Robust Decision Making, Jan H. Kwakkel
International Congress on Environmental Modelling and Software
There is a growing interest in model-based decision support under deep uncertainty, reflected in a variety of approaches and techniques being put forward in the literature. A key idea shared among these various approaches and techniques is the use of models for exploratory rather than predictive purposes. Exploratory modeling aims at exploring the implications for decision making of the various presently irresolvable uncertainties. This is achieved by conducting series of computational experiments that cover the various ways in which the various uncertainties might be resolved. This paper presents an open source library for performing exploratory modeling. This exploratory modeling workbench …
Informing Adaptive Strategies For The Colorado Basin, David Groves, Robert Lempert, Jordan Fischbach, Evan Bloom
Informing Adaptive Strategies For The Colorado Basin, David Groves, Robert Lempert, Jordan Fischbach, Evan Bloom
International Congress on Environmental Modelling and Software
The Colorado River is the single most important source of water in the southwestern United States, providing water and power for nearly 40 million people and water to irrigate more than five million acres of farmland across seven states and for 22 Native American tribes. A vast physical and institutional infrastructure exists to provide water, as well as hydropower, recreational opportunities, environmental services, and other benefits to all these users. However, increasing demand, a decade of drought, and expectations of a significantly changing future climate have put the system under significant and deeply uncertain stress. This paper employs and extends …
Water Resources Decision Support Under Deep Uncertainty: A Classification Of Model-Based Frameworks And Challenges For Scenario Discovery, J. Herman, P. Reed, H. Zeff, G. Characklis
Water Resources Decision Support Under Deep Uncertainty: A Classification Of Model-Based Frameworks And Challenges For Scenario Discovery, J. Herman, P. Reed, H. Zeff, G. Characklis
International Congress on Environmental Modelling and Software
Recent work in water systems planning has focused on exploratory “bottom-up” decision support frameworks, which aim to identify robust solutions capable of withstanding deviations from the conditions for which they were designed. Here we organize these frameworks according to their methods of (1) alternative generation, (2) sampling of states of the world, (3) quantification of robustness measures, and (4) machine learning and sensitivity analysis methods to identify influential uncertainties. We demonstrate these methods using an urban water portfolio planning problem in North Carolina, a region whose water supply faces both climate and population pressures. The task of scenario sampling poses …