{"id":2,"date":"2016-05-10T03:48:21","date_gmt":"2016-05-10T03:48:21","guid":{"rendered":"http:\/\/wp452m.a10-52-158-154.qa.plesk.ru\/wordpress\/?page_id=2"},"modified":"2016-06-09T14:42:27","modified_gmt":"2016-06-09T14:42:27","slug":"sample-page","status":"publish","type":"page","link":"http:\/\/www.uncertain-future.org.uk\/?page_id=2","title":{"rendered":"Contents"},"content":{"rendered":"

Preface<\/strong><\/p>\n

Chapter 1<\/strong><\/p>\n

1.1\u00a0 The purpose of this book<\/p>\n

1.2 The aims of environmental modelling<\/p>\n

1.3 Seven reasons\u00a0not<\/u>\u00a0to use uncertainty analysis<\/p>\n

1.4 The nature of the modelling process<\/p>\n

1.4.1\u00a0\u00a0From perceptual to procedural models<\/p>\n

1.4.2\u00a0\u00a0Parameters, variables and boundary conditions<\/p>\n

1.5 The scale problem and the concept of incommensurabilty<\/p>\n

1.6 The Model Space<\/p>\n

1.7 Ensembles of models<\/p>\n

1.8\u00a0Modelling for formulating understanding<\/p>\n

1.9\u00a0Modelling for practical applications<\/p>\n

1.9.1\u00a0\u00a0Simulation with no historical data available<\/p>\n

1.9.2\u00a0\u00a0Simulation with historical data available<\/p>\n

1.9.3\u00a0\u00a0Forecasting the near future<\/p>\n

1.10\u00a0Guidelines for Effective Modelling<\/p>\n

1.11\u00a0The Meanings of Uncertainty<\/p>\n

1.12\u00a0Deciding on an uncertainty estimation method<\/p>\n

1.13\u00a0Uncertaint y in model predictions and decision making<\/p>\n

1.14\u00a0A Review of Chapter 1.<\/p>\n

Chapter 2:\u00a0 A Philosophical Diversion<\/strong><\/p>\n

2.1\u00a0Why worry about philosophy?<\/p>\n

2.2\u00a0Pragmatic realism<\/p>\n

2.3\u00a0Other philosophical concepts of realism<\/p>\n

2.4\u00a0Models as instrumentalist tools<\/p>\n

2.5\u00a0The model validation issue<\/p>\n

2.6\u00a0The model falsification issue<\/p>\n

2.7\u00a0The model confirmation issue: Bayesian approaches<\/p>\n

2.8\u00a0The information content of observations as evidence for the confirmation of models.<\/p>\n

2.9\u00a0Explanatory depth and expecting the unexpected<\/p>\n

2.10\u00a0Uncertainty, Ignorance, and Factors of Safety<\/p>\n

2.11\u00a0Review of Chapter 2<\/p>\n

Chapter 3:\u00a0 Simulation with No Historical Data Available<\/strong><\/p>\n

3.1\u00a0Sensitivity, Scenarios and Forward Uncertainty Analysis<\/p>\n

3.2\u00a0Making decisions about prior information<\/p>\n

3.2.1\u00a0\u00a0Prior distributions of parameters<\/p>\n

3.2.2\u00a0\u00a0Belief Networks<\/p>\n

3.3\u00a0Sampling the Model Space<\/p>\n

3.3.1\u00a0\u00a0Analytical propagation of probabilistic uncertainty<\/p>\n

3.3.2\u00a0\u00a0Discrete samples or Random Monte Carlo Search?<\/p>\n

3.3.3\u00a0\u00a0Pseudo-random numbers and the realisation effect<\/p>\n

3.3.4\u00a0\u00a0Guided Monte Carlo Search<\/p>\n

3.3.5\u00a0\u00a0Copula Sampling<\/p>\n

3.3.6\u00a0\u00a0Case Study: Copula Sampling in mapping groundwater quality<\/p>\n

3.4\u00a0Fuzzy representations of uncertainty<\/p>\n

3.4.1\u00a0\u00a0Case Study: Forward Uncertainty Analysis using fuzzy variables<\/p>\n

3.5\u00a0Sensitivity Analysis<\/p>\n

3.5.1\u00a0\u00a0Point sensitivity analysis<\/p>\n

3.5.2\u00a0\u00a0Global sensitivity analysis: Sobol\u2019 Generalised Sensitivity Analysis<\/p>\n

3.5.3\u00a0\u00a0Case Study:\u00a0 Application of Sobol\u2019 GSA to a hydrologic models<\/p>\n

3.5.4\u00a0\u00a0Global sensitivity analysis:\u00a0 HSY Generalised Sensitivity Analysis<\/p>\n

3.6\u00a0Model emulation techniques<\/p>\n

3.7\u00a0Uncertain Scenarios<\/p>\n

3.8\u00a0Summary of Chapter 3<\/p>\n

Box 3.1\u00a0 Simple operations with probability distributed variables<\/p>\n

Box 3.2\u00a0 Monte Carlo Sampling of a Model Space<\/p>\n

Box 3.3\u00a0\u00a0Choosing a random number generator<\/p>\n

Box 3.4\u00a0 Fuzzy representations of uncertainty<\/p>\n

Chapter 4:\u00a0\u00a0 Simulation with historical data available<\/strong><\/p>\n

4.1\u00a0Model calibration and model conditioning<\/p>\n

4.2 Weighted nonlinear regression approaches to model calibration.<\/p>\n

4.2.1\u00a0\u00a0Choosing the cost (objective) function<\/p>\n

4.2.2\u00a0\u00a0Evaluating Parameter and Prediction Uncertainties<\/p>\n

4.2.3\u00a0\u00a0Assessing the value of additional data<\/p>\n

4.3\u00a0\u00a0Formal Bayesian approaches to model conditioning<\/p>\n

4.3.1\u00a0\u00a0Formal likelihood measures<\/p>\n

4.3.2\u00a0\u00a0Markov Chain Monte Carlo Search (MC2<\/sup>)<\/p>\n

4.3.3\u00a0\u00a0Case Study:\u00a0 Assessing Uncertainties in a conceptual water balance model (Engeland et al., 2005)<\/p>\n

4.4\u00a0\u00a0Pareto Optimal Sets<\/p>\n

4.5\u00a0\u00a0Generalised Likelihood Uncertainty Estimation<\/p>\n

4.5.1\u00a0\u00a0The basis of the GLUE methodology<\/p>\n

4.5.2\u00a0\u00a0Deciding on whether a model is behavioural or not<\/p>\n

4.5.3\u00a0\u00a0Equifinality, confidence limits, tolerance limits and prediction limits<\/p>\n

4.5.4\u00a0\u00a0Equifinality and model validation<\/p>\n

4.5.5\u00a0\u00a0Equifinality and model spaces: sampling efficiency issues<\/p>\n

4.5.6\u00a0\u00a0Fuzzy Measures in Model Evaluation<\/p>\n

4.5.7\u00a0\u00a0Case Study: Hypothesis Testing Models of Stream Runoff Generation using GLUE<\/p>\n

4.5.8\u00a0\u00a0Variants on the GLUE methodology<\/p>\n

4.5.9 What to do if you find that all your models can be rejected?<\/p>\n

4.6\u00a0\u00a0Fuzzy Systems:\u00a0 Conditioning Fuzzy Rules using Data<\/p>\n

4.7\u00a0\u00a0Comparing Methods for Model Conditioning: Coherence and the Information Content of Data<\/p>\n

4.8\u00a0\u00a0Summary of Chapter 4<\/p>\n

Box 4.1\u00a0 Weighted nonlinear regression<\/p>\n

Box 4.2\u00a0 Formal Bayes Methods<\/p>\n

Box 4.3\u00a0 Markov Chain and Population Monte Carlo Methods<\/p>\n

Box 4.4\u00a0 Generalised Likelihood Uncertainty Estimation (GLUE)<\/p>\n

Chapter 5:\u00a0 Forecasting the near future<\/strong><\/p>\n

5.1\u00a0Real-time data assimilation<\/p>\n

5.2\u00a0Least squares error correction models<\/p>\n

5.3\u00a0The Kalman Filter<\/p>\n

5.3.1\u00a0\u00a0Updating a model of the residual errors<\/p>\n

5.3.2\u00a0\u00a0Updating the gain on a forecasting model<\/p>\n

5.3.3\u00a0\u00a0Case study: flood forecasting on the River Severn<\/p>\n

5.3.4\u00a0\u00a0The Extended Kalman Filter<\/p>\n

5.4 Ensemble Kalman Filter<\/p>\n

5.4.1\u00a0\u00a0Case Study:\u00a0 Application of the The Ensemble Kalman Filter to the Leaf River Basin<\/p>\n

5.4.2\u00a0\u00a0The Ensemble Kalman Smoother<\/p>\n

5.5\u00a0The Particle Filter<\/p>\n

5.5.1\u00a0\u00a0Case Study:\u00a0 Comparison of EnKF and PF methods on the River Rhine<\/p>\n

5.6\u00a0Variational methods<\/p>\n

5.7\u00a0Ensemble Methods in Weather Forecasting<\/p>\n

5.8\u00a0Review of Chapter 5<\/p>\n

Box 5.1\u00a0 Kalman Filter Methods for Data Assimilation<\/p>\n

Box 5.2\u00a0 Variational Methodsfor Data Assimilation<\/p>\n

Chapter 6:\u00a0 Decision making when faced with uncertainty<\/strong><\/p>\n

6.1\u00a0\u00a0Uncertainty and Risk in Decision Making<\/p>\n

6.2\u00a0\u00a0Uncertainty in Framing the Decision Context<\/p>\n

6.3\u00a0\u00a0Decision Trees, Influence Diagrams, and Belief Networks<\/p>\n

6.4\u00a0\u00a0Methods of Risk Assessment in Decision Making<\/p>\n

6.5\u00a0\u00a0Risk-Based Decision Making Methodologies<\/p>\n

6.5.1\u00a0\u00a0Assessing the preferences of the decision maker<\/p>\n

6.5.2\u00a0\u00a0Indifference between actions<\/p>\n

6.5.3\u00a0\u00a0Adding uncertainty and more information<\/p>\n

6.5.4\u00a0\u00a0Case Studies:\u00a0 Decisions for Flood Warning and Control in Lake Como, Italy and the Red River, N. Dakota<\/p>\n

6.6\u00a0The use of expert opinion in decision making<\/p>\n

6.7\u00a0Combining the opinions of experts: Bayesian Belief Networks<\/p>\n

6.7.1\u00a0\u00a0Adding empirical evidence to a Belief Network<\/p>\n

6.7.2\u00a0\u00a0A Case Study<\/p>\n

6.8\u00a0Evidential Reasoning Methods<\/p>\n

6.8.1\u00a0\u00a0Case Study: Use of Evidential Reasoning in assessing management options for Rupa Tal Lake Nepal.<\/p>\n

6.9\u00a0Decision Support Systems.<\/p>\n

6.10\u00a0Info-Gap decision theory<\/p>\n

6.10.1\u00a0Case Study:\u00a0 Info-Gap Decision Making in Designing Flood Defences<\/p>\n

6.11\u00a0The Issue of Ownership of Uncertainty in Decision Making<\/p>\n

6.12\u00a0\u00a0The NUSAP methodology<\/p>\n

6.13\u00a0\u00a0Robust Adaptive Management in the Face of Uncertainty<\/p>\n

6.14\u00a0\u00a0Uncertainty and the Precautionary Principle in Decision Making<\/p>\n

6.15\u00a0\u00a0Summary of Chapter 6<\/p>\n

Box 6.1\u00a0 Basic Risk-based Decision Theory<\/p>\n

Box 6.2\u00a0\u00a0Info-Gap decision theory<\/p>\n

Chapter 7:\u00a0 An uncertain future?<\/strong><\/p>\n

7.1\u00a0\u00a0So what should the practitioner do in the face of so many uncertainty estimation methods?<\/p>\n

7.2\u00a0 The problem of future histories – unknowability and uncertainty<\/p>\n

7.3\u00a0\u00a0But is the uncertainty problem simply a result of using poor models?<\/p>\n

7.4\u00a0\u00a0Accepting an uncertain future<\/p>\n

7.4.1\u00a0\u00a0Modelling as a learning process about places<\/p>\n

7.4.2\u00a0\u00a0Learning\u00a0about\u00a0Model Structures<\/p>\n

7.5\u00a0\u00a0Future proofing modelling systems: adaptive modelling, adaptive management<\/p>\n

7.6\u00a0\u00a0Summary of Chapter 7<\/p>\n

 <\/p>\n

Appendix:\u00a0 A (Brief) Guide to Matrix Algebra<\/strong><\/p>\n

 <\/p>\n

Appendix:\u00a0 A (Brief) Guide to Software<\/strong><\/p>\n

 <\/p>\n

Glossary<\/strong><\/p>\n

 <\/p>\n

Bibliography<\/strong><\/p>\n

 <\/p>\n

Index<\/strong><\/p>\n


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Preface Chapter 1 1.1\u00a0 The purpose of this book 1.2 The aims of environmental modelling 1.3 Seven reasons\u00a0not\u00a0to use uncertainty analysis 1.4 The nature of the modelling process 1.4.1\u00a0\u00a0From perceptual to procedural models 1.4.2\u00a0\u00a0Parameters, variables and boundary conditions 1.5 The scale problem and the concept of incommensurabilty 1.6 The Model Space 1.7 Ensembles of models … Continue reading Contents<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":4,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","meta":{"footnotes":""},"class_list":["post-2","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"http:\/\/www.uncertain-future.org.uk\/index.php?rest_route=\/wp\/v2\/pages\/2","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.uncertain-future.org.uk\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/www.uncertain-future.org.uk\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/www.uncertain-future.org.uk\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.uncertain-future.org.uk\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2"}],"version-history":[{"count":9,"href":"http:\/\/www.uncertain-future.org.uk\/index.php?rest_route=\/wp\/v2\/pages\/2\/revisions"}],"predecessor-version":[{"id":174,"href":"http:\/\/www.uncertain-future.org.uk\/index.php?rest_route=\/wp\/v2\/pages\/2\/revisions\/174"}],"up":[{"embeddable":true,"href":"http:\/\/www.uncertain-future.org.uk\/index.php?rest_route=\/wp\/v2\/pages\/4"}],"wp:attachment":[{"href":"http:\/\/www.uncertain-future.org.uk\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}