HWR 642 Merging Data with Models (3 units)
HWR 642
MERGING DATA WITH MODELS
(3 Units)
SYLLABUS1
Spring 2007
Tue–Thurs 9:30am-10:45am
Hoshin Gupta, Professor
Koray Yilmaz, Ph.D Candidate (Teaching Assistant)
Department of Hydrology & Water Resources
The University of Arizona, Tucson, Arizona 85721
SYLLABUS (17 WEEKS TOTAL, 15 ACTIVE WEEKS):
WEEK 1_ORGANIZATIONAL MEETING
• Course objectives, format and expectations (Thursday Jan 11)
WEEK 2_NO CLASS
WEEK 3_INTRODUCTION TO MERGING DATA WITH MODELS
• Lecture No 1: Review of Fundamental Concepts
• Lecture No 2: Review continued, Tools needed for this class, & Discussion
of Student Term Projects
WEEK 4_HISTORICAL & PHILOSOPHICAL PERSPECTIVE
• Quiz No 1: (Review of fundamental concepts)
• Lecture No 3: Historical perspective (Different views of calibration),
Problems, and responses to those problems, Philosophical approaches
(Optimization/Parsimony, Constraining/Inclusion & Diagnosis/Power)
• Lecture No 4: Historical & Philosophical Perspective continued
1
January 29, 2007
HWR 642 Course Outline & Syllabus – Page 1
HWR 642 Merging Data with Models (3 units)
WEEK 5_OVERVIEW OF ESTIMATION THEORY
• Quiz No 2: (Parsimony, Inclusion & Power)
• Lecture No 5: Classical estimation theories and relationship among them
(Regression, Maximum Likelihood, Maximum Entropy, Maximum Bayesian)
• Lecture No 6: Introduction to Matlab Codes for the Class (Simulation, 1DSensitivity Analysis, 2D-Response Surface Analysis, Optimization by
Downhill Simplex Method)
• Assignment No 1: (Simulation, 1D-Sensitivity Analysis, 2D-Response
Surface Analysis, Optimization by Downhill Simplex Method)
WEEK 6_GUEST PRESENTATIONS
• Presentation on Regional Modeling using ‘ABCD’ model by Guillermo
Martinez (MS student)
• Presentation on Distributed Modeling for Flash-flood Forecasting in SemiArid Watersheds by Soni Yatheendradas (PhD student)
WEEK 7_SINGLE CRITERION GLOBAL OPTIMIZATION
• Quiz No 3: (Estimation theory)
• Lecture No 7: Motivation for global optimization, Effectiveness &
Efficiency in the context of optimization
• Lecture No 8: The Shuffled Complex Evolution (SCE) global optimization
method
• Assignment No 2: (Application of SCE to calibration of a hydrologic model)
WEEK 8_GUEST PRESENTATIONS
• Presentation on Hierarchical Multi-Criteria Calibration of the US National
Weather Service’s Distributed Flood-Forecast Model by Koray Yilmaz (PhD
student)
• Presentation on Regularization method for Calibration of the US National
Weather Service’s Distributed Flood-Forecast Model by Prafulla Pokhrel
(MS student)
WEEK 9_GUEST / STUDENT PRESENTATIONS
• Presentation on Bayesian Approach to Model Structure Identification by
Natasha Bulygina (PhD student)
• Class Student Presentations for Term Project – Model Simulations
WEEK 10_NO CLASS (SPRING BREAK)
HWR 642 Course Outline & Syllabus – Page 2
HWR 642 Merging Data with Models (3 units)
WEEK 11_MULTIPLE-CRITERIA OPTIMIZATION APPROACH
• Quiz No 4: (Global optimization, SCE, effectiveness & Efficiency)
• Lecture No 9: Motivation for multi-criteria approach, Theory/formulation
of the multiple-criteria problem, Definition of criteria, Pareto optimality
of solutions
• Lecture No 10: Multi-criteria solution strategies: Constrained singleoptimization, Surrogate-worth trade-off, compromise programming,
Multiple-criteria optimization by Multiple-Objective Complex Evolution
(MOCOM) global optimization approach
WEEK 12_MULTIPLE-CRITERIA OPTIMIZATION (CONT.)
• Quiz No 5: (Multiple-criteria trade-offs, Pareto optimality)
• Lecture No 11: Applications of the Multi-Criteria approach
• Lecture No 12: Further Discussion of Matlab Codes for the Class (MC
Optimization using Random Search, MCO using SCE, MCO using MOSCEM)
• Assignment No 3: (Using Random Search & SCE/Weighting Method to
generate 2-criteria Pareto optimal solutions for calibration of a hydrologic
model)
WEEK 13_METROPOLIS METHODS
• Quiz No 6: (Multiple-Criteria approach)
• Lecture No 13: Metropolis sampling, Shuffled Complex Evolution Metropolis
(SCEM) uncertainty estimation approach
• Lecture No 15: Generalized Likelihood Uncertainty Estimation (GLUE)
uncertainty estimation approach [by Koray Yilmaz]
WEEK 14_METROPOLIS METHODS (CONT.)
• Quiz No 7: (Metropolis sampling, SCEM)
• Lecture No 14: Multiple-Objective Shuffled Complex Evolution Metropolis
(MOSCEM) uncertainty estimation approach
• Lecture No 16: The relationship between Data and Information (Role of
Info in model identification, Types of information, Factors influencing
information content, Effect of data amount), Information in the context
of hydrologic modeling
• Assignment No 4: (Using MOSCEM to generate 2-criteria Pareto optimal
solutions for calibration of a hydrologic model)
HWR 642 Course Outline & Syllabus – Page 3
HWR 642 Merging Data with Models (3 units)
WEEK 15_ADVANCED TOPICS (UNCERTAINTY / DIAGNOSIS)
• Quiz No 8: (Data & Information)
• Lecture No 17: Sources & Treatment of Uncertainty
• Lecture No 18: Diagnostic approach to model identification
• Assignment No 5: (Estimating output uncertainty for a hydrologic model)
WEEK 16_REVIEW & STUDENT PROJECT PRESENTATIONS
• Lecture No 19: Review
• Class Student Presentations for Term Project – Final
WEEK 17_STUDENT PROJECT PRESENTATIONS
• Class Student Presentations for Term Project – Final
WEEK 18_EXAM WEEK – NO CLASS
HWR 642 Course Outline & Syllabus – Page 4