Potential Projects for SAMSI Program on Environmental Models Peter Reichert.

Slides:



Advertisements
Similar presentations
Eawag: Swiss Federal Institute of Aquatic Science and Technology Analyzing possible causes of bias of hydrological models with stochastic, time-dependent.
Advertisements

Bayesian tools for analysing and reducing uncertainty Tony OHagan University of Sheffield.
Panel discussion on the future of software in support of microbial risk assessment.
Note: Lists provided by the Conference Board of Canada
STRATEGIC ASSETS AND ORGANIZATIONAL RENT Amit, R., & Schoemaker, P. J. H., SMJ, 1993 Youngsoo Kim, BADM 545 Fall 2013.
E: Track II Project level analyses Robert Lempert, RAND 1.
Stephen McCray and David Courard-Hauri, Environmental Science and Policy Program, Drake University Introduction References 1.Doran, P. T. & Zimmerman,
What is a sample? Epidemiology matters: a new introduction to methodological foundations Chapter 4.
A Heuristic Bidding Strategy for Multiple Heterogeneous Auctions Patricia Anthony & Nicholas R. Jennings Dept. of Electronics and Computer Science University.
Neural Networks Marco Loog.
Linear Models Tony Dodd January 2007An Overview of State-of-the-Art Data Modelling Overview Linear models. Parameter estimation. Linear in the.
Sanjay Goel, School of Business/Center for Information Forensics and Assurance University at Albany Proprietary Information 1 Unit Outline Quantitative.
Deciding for well being: Decision-making science in action.
STAT 4060 Design and Analysis of Surveys Exam: 60% Mid Test: 20% Mini Project: 10% Continuous assessment: 10%
Transformational Leadership
CSR Project, 3 cr. Corporate Responsibility, C-module (15 cr.) or free-choise studies Introduction to Corporate responsibility, 1,5-3 cr. (depending on.
Oversight CHAPTER SIXTEEN Student Version Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin.
Local flood forecasting for local flood risk areas Keith Beven, David Leedal, Peter Young and Paul Smith Lancaster Environment Centre.
Raga Gopalakrishnan University of Colorado at Boulder Sean D. Nixon (University of Vermont) Jason R. Marden (University of Colorado at Boulder) Stable.
“Integration and communication as central issues in Dutch negotiated agreements on industrial energy efficiency” Greening of Industry conference, Cardiff,
Stakeholder Consultation: Experiences and Learning from Bangladesh NAPA By Mozaharul Alam Research Fellow Bangladesh Centre for Advanced Studies (BCAS)
Introduction to Discrete Event Simulation Customer population Service system Served customers Waiting line Priority rule Service facilities Figure C.1.
Applications of Bayesian sensitivity and uncertainty analysis to the statistical analysis of computer simulators for carbon dynamics Marc Kennedy Clive.
Mobilizing Agri-Food Knowledge Kari Doerksen with Cami Ryan, Elias Nelson and Peter W.B. Phillips ICABR, Ravello, Italy June 2013.
Space, Relativity, and Uncertainty in Ecosystem Assessment of Everglades Restoration Scenarios Michael M. Fuller, Louis J. Gross, Scott M. Duke-Sylvester,
Eawag: Swiss Federal Institute of Aquatic Science and Technology Problems of Inference and Uncertainty Estimation in Hydrologic Modelling Peter Reichert.
Chengjie Sun,Lei Lin, Yuan Chen, Bingquan Liu Harbin Institute of Technology School of Computer Science and Technology 1 19/11/ :09 PM.
1 Andy Guo Why Study Entrepreneurship?. 2 Andy Guo Why Study Entrepreneurship? l Knowledge of process of starting a business l Basic principles applicable.
The assessment concept of innovative level of Armenia’s industry Gagik Makaryan RUEA chairman 9 September, 2013.
Environment analysis has 3 basic objectives-  Under taking of current & potential changes.  Should provide inputs for strategic decision making.  Rich.
Experiences in assessing deposition model uncertainty and the consequences for policy application Rognvald I Smith Centre for Ecology and Hydrology, Edinburgh.
Design Process … and some design inspiration. Course ReCap To make you notice interfaces, good and bad – You’ll never look at doors the same way again.
Sarat Sreepathi North Carolina State University Internet2 – SURAgrid Demo Dec 6, 2006.
AN ADAPTIVE CYBERINFRASTRUCTURE FOR THREAT MANAGEMENT IN URBAN WATER DISTRIBUTION SYSTEMS Kumar Mahinthakumar North Carolina State University DDDAS BOF,
Eawag: Swiss Federal Institute of Aquatic Science and Technology Mechanism-Based Emulation of Dynamic Simulation Models – Concept and Application in Hydrology.
The Management Challenge of Transnational Management.
Technology Management FRAMEWORK. Technology management process framework Gregory (1995) has proposed that management of technology is comprised of five.
Development of a community-based participatory network for integrated solid waste management By: Y.P. Cai, G.H. Huang, Q. Tan & G.C. Li EVSE, Faculty of.
Adaptive Integrated Framework (AIF): a new methodology for managing impacts of multiple stressors in coastal ecosystems A bit more on AIF, project components.
6. Population Codes Presented by Rhee, Je-Keun © 2008, SNU Biointelligence Lab,
Treatment of correlated systematic errors PDF4LHC August 2009 A M Cooper-Sarkar Systematic differences combining ZEUS and H1 data  In a QCD fit  In a.
Getting more value for money: working with countries and partners toward greater effectiveness and efficiency Peter Stegman, Senior Economist.
BZUPAGES.COM. Organizational Behavior Presented To: Sir Tisman Pasha Presented By: Khurram Shahzad Roll# :
Eawag: Swiss Federal Institute of Aquatic Science and Technology Analyzing input and structural uncertainty of a hydrological model with stochastic, time-dependent.
What Is Organizational Behaviour?. What Managers Do Managerial Activities Make decisions Allocate resources Direct activities of others to attain goals.
A dynamic optimal capital structure model with costly adjustment mechanisms H. Liao, Y. Tung, T. Chen Discussion by Fei Ding Hong Kong University of Science.
Eawag: Swiss Federal Institute of Aquatic Science and Technology Use of time-dependent parameters for improvement and uncertainty estimation of dynamic.
Introduction to emulators Tony O’Hagan University of Sheffield.
Eawag: Swiss Federal Institute of Aquatic Science and Technology Analyzing input and structural uncertainty of deterministic models with stochastic, time-dependent.
Renewable energies | Eco-friendly production | Innovative transport | Eco-efficient processes | Sustainable resources Multi-fidelity meta-models for reservoir.
Linear Models Tony Dodd. 21 January 2008Mathematics for Data Modelling: Linear Models Overview Linear models. Parameter estimation. Linear in the parameters.
Dynamic capabilities in young entrepreneurial ventures: Evidence from Europe Aimilia Protogerou and Yannis Caloghirou Laboratory of Industrial and Energy.
Operational Strategies “Management by objective works – if you know the objectives. Ninety percent of the time you don’t ” Peter F Drucker.
IIASA Riku Suutari, Markus Amann, Janusz Cofala, Zbigniew Klimont Wolfgang Schöpp A methodology to propagate uncertainties through the RAINS scenario calculations.
3.2 Natural Capital Human Population, Carrying Capacity, and Resource Use.
Where We Are Now. Where We Are Now Project Oversight Project Oversight Oversight’s Purposes: A set of principles and processes to guide and improve.
COmbining Probable TRAjectories — COPTRA
Applied Sport Management Skills Presentation Package
PSO -Introduction Proposed by James Kennedy & Russell Eberhart in 1995
LINEAR AND NONLINEAR MODELS
Lecture 1 Economic Analysis and Policies for Environmental Problems
MIS5101: Business Intelligence Access versus Accuracy
Where We Are Now. Where We Are Now Project Oversight Project Oversight Oversight’s Purposes: A set of principles and processes to guide and improve.
This talk will follow the topical structure suggested by the ABP:
Today (2/23/16) Learning objectives:
MODULE 2: TEAMWORK.
Chapter 1: Introduction to Engineering Economy
MODULE 2: TEAMWORK.
Get final-look Atlas/Ardizzone wind product.
Uncertainty Propagation
Presentation transcript:

Potential Projects for SAMSI Program on Environmental Models Peter Reichert

Concrete Projects 1)Johanna Mieleitner: Application of time-dependent parameters to a simple hydrological model (model improvements, uncertainty assessment). 2)Ariel Contron-Arias: Application of time-dependent parameters to a simple epidemiological model (identification of times of changing behavior).

Discussed Project Ideas 3)Ariel Contron-Arias: Other approaches to dealing with time dependent parameters for epidemiological models. 4)Fei Liu: Linearized treatment of time-dependent param. 5)Serge Guillas: More general class of time-dep. param. 6)Robert Wolpert: Stat. characterization of rainfall uncertainty. 7)Tony O’Hagan: Dynamic emulators for hydrological model. 8)Bruce Pitman: Apply polynomial chaos quadrature to hydrological model. 9)Lenny Smith: Use sets of probabilities for characterizing uncertainty and for decision support. 10)Christine Schoemaker: Apply efficient global optimization to environmental models. 11)?: Other approaches to dealing with bias then GP or OU.

Suggestion for Future Activities Extend list of project ideas. Assess innovative potential and usefulness. Try to move more of the ideas to projects by making them more concrete. Build small project teams that work on projects. Do not build more working groups; report back from projects to the “big” methodology group for discussion and for getting inspirations. The larger “diversity” of this group can be expected to be more stimulating then smaller, more specific working groups. Plan conference contributions / contributions to closing workshop/final report based on preliminary results.