scholarly journals The challenge of forecasting high streamflows 1–3 months in advance with lagged climate indices in southeast Australia

2014 ◽  
Vol 14 (2) ◽  
pp. 219-233 ◽  
Author(s):  
J. C. Bennett ◽  
Q. J. Wang ◽  
P. Pokhrel ◽  
D. E. Robertson
Keyword(s):  
Large Scale ◽  
Bayesian Model Averaging ◽  
Emergency Services ◽  
Joint Probability ◽  
Model Averaging ◽  
Climate Indices ◽  
Warning Systems ◽  
Forecast Period ◽  
Considerable Benefit ◽  
Probability Modelling

Abstract. Skilful forecasts of high streamflows a month or more in advance are likely to be of considerable benefit to emergency services and the broader community. This is particularly true for mesoscale catchments (< 2000 km2) with little or no seasonal snowmelt, where real-time warning systems are only able to give short notice of impending floods. In this study, we generate forecasts of high streamflows for the coming 1-month and coming 3-month periods using large-scale ocean–atmosphere climate indices and catchment wetness as predictors. Forecasts are generated with a combination of Bayesian joint probability modelling and Bayesian model averaging. High streamflows are defined as maximum single-day streamflows and maximum 5-day streamflows that occur during each 1-month or 3-month forecast period. Skill is clearly evident in the 1-month forecasts of high streamflows. Surprisingly, in several catchments positive skill is also evident in forecasts of large threshold events (exceedance probabilities of 25%) over the next month. Little skill is evident in forecasts of high streamflows for the 3-month period. We show that including lagged climate indices as predictors adds little skill to the forecasts, and thus catchment wetness is by far the most important predictor. Accordingly, we recommend that forecasts may be improved by using accurate estimates of catchment wetness.

scholarly journals The challenge of forecasting high streamflows in medium sized catchments 1–3 months in advance

2013 ◽  
Vol 1 (4) ◽  
pp. 3129-3168
Author(s):  
J. C. Bennett ◽  
Q. J. Wang ◽  
P. Pokhrel ◽  
D. E. Robertson
Keyword(s):  
Large Scale ◽  
Bayesian Model Averaging ◽  
Emergency Services ◽  
Joint Probability ◽  
Model Averaging ◽  
Climate Indices ◽  
Warning Systems ◽  
Forecast Period ◽  
Probability Modeling ◽  
Considerable Benefit

Abstract. Skilful forecasts of high streamflows a month or more in advance are likely to be of considerable benefit to emergency services and the broader community. This is particularly true for small-medium sized catchments (< 2000km2), where real-time warning systems are only able to give short notice of impending floods. In this study, we generate forecasts of high streamflows for the coming 1 month and coming 3 month periods using large-scale ocean/atmosphere climate indices and catchment wetness as predictors. Forecasts are generated with a combination of Bayesian joint probability modeling and Bayesian model averaging. High streamflows are defined as maximum single-day streamflows and maximum 5 day streamflows that occur during each 1 month or 3 month forecast period. Skill is clearly evident in the 1 month forecasts of high streamflows. Surprisingly, in several catchments positive skill is also evident in forecasts of large threshold events (exceedance probabilities of 25%) over the next month. Little skill is evident in forecasts of high streamflows for the 3 month period. We show that including climate indices as predictors adds little skill to the forecasts, and thus catchment wetness is by far the most important predictor. Accordingly, we recommend that forecasts may be improved by using accurate estimates of catchment wetness.

Optimizing Prediction Using Bayesian Model Averaging: Examples Using Large-Scale Educational Assessments

2018 ◽  
Vol 42 (4) ◽  
pp. 423-457 ◽  
Author(s):  
David Kaplan ◽  
Chansoon Lee
Keyword(s):  
Bayesian Model ◽  
Structural Equation ◽  
Large Scale ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Predictive Performance ◽  
Bayesian Regression ◽  
Equation Modeling ◽  
Scientific Principles ◽  
Educational Assessments

This article provides a review of Bayesian model averaging as a means of optimizing the predictive performance of common statistical models applied to large-scale educational assessments. The Bayesian framework recognizes that in addition to parameter uncertainty, there is uncertainty in the choice of models themselves. A Bayesian approach to addressing the problem of model uncertainty is the method of Bayesian model averaging. Bayesian model averaging searches the space of possible models for a set of submodels that satisfy certain scientific principles and then averages the coefficients across these submodels weighted by each model’s posterior model probability (PMP). Using the weighted coefficients for prediction has been shown to yield optimal predictive performance according to certain scoring rules. We demonstrate the utility of Bayesian model averaging for prediction in education research with three examples: Bayesian regression analysis, Bayesian logistic regression, and a recently developed approach for Bayesian structural equation modeling. In each case, the model-averaged estimates are shown to yield better prediction of the outcome of interest than any submodel based on predictive coverage and the log-score rule. Implications for the design of large-scale assessments when the goal is optimal prediction in a policy context are discussed.

Seasonal Forecasts of Australian Rainfall through Calibration and Bridging of Coupled GCM Outputs

2014 ◽  
Vol 142 (5) ◽  
pp. 1758-1770 ◽  
Author(s):  
Andrew Schepen ◽  
Q. J. Wang ◽  
David E. Robertson
Keyword(s):  
General Circulation ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
General Circulation Models ◽  
Seasonal Rainfall ◽  
Climate Indices ◽  
Seasonal Forecasts ◽  
Spatial Coverage ◽  
Validation Procedure ◽  
Systematic Biases

Abstract Coupled general circulation models (GCMs) are increasingly being used to forecast seasonal rainfall, but forecast skill is still low for many regions. GCM forecasts suffer from systematic biases, and forecast probabilities derived from ensemble members are often statistically unreliable. Hence, it is necessary to postprocess GCM forecasts to improve skill and statistical reliability. In this study, the authors compare three methods of statistically postprocessing GCM output—calibration, bridging, and a combination of calibration and bridging—as ways to treat these problems and make use of multiple GCM outputs to increase the skill of Australian seasonal rainfall forecasts. Three calibration models are established using ensemble mean rainfall from three variants of the Predictive Ocean Atmosphere Model for Australia (POAMA) version M2.4 as predictors. Six bridging models are established using POAMA forecasts of seasonal climate indices as predictors. The calibration and bridging forecasts are merged through Bayesian model averaging. Forecast attributes including skill, sharpness, and reliability are assessed through a rigorous leave-three-years-out cross-validation procedure for forecasts of 1-month lead time. While there are overlaps in skill, there are regions and seasons where the calibration or bridging forecasts are uniquely skillful. The calibration forecasts are more skillful for January–March (JFM) to June–August (JJA). The bridging forecasts are more skillful for July–September (JAS) to December–February (DJF). Merging calibration and bridging forecasts retains, and in some seasons expands, the spatial coverage of positive skill achieved by the better of the calibration forecasts and bridging forecasts individually. The statistically postprocessed forecasts show improved reliability compared to the raw forecasts.

scholarly journals Spatially-dependent Bayesian model selection for disease mapping

2016 ◽  
Vol 27 (1) ◽  
pp. 250-268 ◽  
Author(s):  
Rachel Carroll ◽  
Andrew B Lawson ◽  
Christel Faes ◽  
Russell S Kirby ◽  
Mehreteab Aregay ◽  
...  
Keyword(s):  
Model Selection ◽  
Bayesian Model ◽  
Large Scale ◽  
Bayesian Model Averaging ◽  
Disease Mapping ◽  
Model Averaging ◽  
Bayesian Model Selection ◽  
Small Scale ◽  
Study Region ◽  
Model Sets

In disease mapping where predictor effects are to be modeled, it is often the case that sets of predictors are fixed, and the aim is to choose between fixed model sets. Model selection methods, both Bayesian model selection and Bayesian model averaging, are approaches within the Bayesian paradigm for achieving this aim. In the spatial context, model selection could have a spatial component in the sense that some models may be more appropriate for certain areas of a study region than others. In this work, we examine the use of spatially referenced Bayesian model averaging and Bayesian model selection via a large-scale simulation study accompanied by a small-scale case study. Our results suggest that BMS performs well when a strong regression signature is found.

Learning Binding Affinity from Augmented High Throughput Screening Data

2013 ◽  
pp. 364-385
Author(s):  
Nicos Angelopoulos ◽  
Andreas Hadjiprocopis ◽  
Malcolm D. Walkinshaw
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Machine Learning Techniques ◽  
High Dimensional ◽  
Learning Techniques

In high throughput screening a large number of molecules are tested against a single target protein to determine binding affinity of each molecule to the target. The objective of such tests within the pharmaceutical industry is to identify potential drug-like lead molecules. Current technology allows for thousands of molecules to be tested inexpensively. The analysis of linking such biological data with molecular properties is thus becoming a major goal in both academic and pharmaceutical research. This chapter details how screening data can be augmented with high-dimensional descriptor data and how machine learning techniques can be utilised to build predictive models. The pyruvate kinase protein is used as a model target throughout the chapter. Binding affinity data from a public repository provide binding information on a large set of screened molecules. The authors consider three machine learning paradigms: Bayesian model averaging, Neural Networks, and Support Vector Machines. The authors apply algorithms from the three paradigms to three subsets of the data and comment on the relative merits of each. They also used the learnt models to classify the molecules in a large in-house molecular database that holds commercially available chemical structures from a large number of suppliers. They discuss the degree of agreement in compounds selected and ranked for three algorithms. Details of the technical challenges in such large scale classification and the ability of each paradigm to cope with these are put forward. The application of machine learning techniques to binding data augmented by high-dimensional can provide a powerful tool in compound testing. The emphasis of this work is on making very few assumptions or technical choices with regard to the machine learning techniques. This is to facilitate application of such techniques by non-experts.

scholarly journals Streamflow prediction with large climate indices using several hybrid multilayer perceptrons and copula Bayesian model averaging

2021 ◽  
Vol 133 ◽  
pp. 108285
Author(s):  
Fatemeh Panahi ◽  
Mohammad Ehteram ◽  
Ali Najah Ahmed ◽  
Yuk Feng Huang ◽  
Amir Mosavi ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Multilayer Perceptrons ◽  
Climate Indices ◽  
Streamflow Prediction

Learning Binding Affinity from Augmented High Throughput Screening Data

2011 ◽  
pp. 212-234
Author(s):  
Nicos Angelopoulos ◽  
Andreas Hadjiprocopis ◽  
Malcolm D. Walkinshaw
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Machine Learning Techniques ◽  
High Dimensional ◽  
Learning Techniques

In high throughput screening a large number of molecules are tested against a single target protein to determine binding affinity of each molecule to the target. The objective of such tests within the pharmaceutical industry is to identify potential drug-like lead molecules. Current technology allows for thousands of molecules to be tested inexpensively. The analysis of linking such biological data with molecular properties is thus becoming a major goal in both academic and pharmaceutical research. This chapter details how screening data can be augmented with high-dimensional descriptor data and how machine learning techniques can be utilised to build predictive models. The pyruvate kinase protein is used as a model target throughout the chapter. Binding affinity data from a public repository provide binding information on a large set of screened molecules. The authors consider three machine learning paradigms: Bayesian model averaging, Neural Networks, and Support Vector Machines. The authors apply algorithms from the three paradigms to three subsets of the data and comment on the relative merits of each. They also used the learnt models to classify the molecules in a large in-house molecular database that holds commercially available chemical structures from a large number of suppliers. They discuss the degree of agreement in compounds selected and ranked for three algorithms. Details of the technical challenges in such large scale classification and the ability of each paradigm to cope with these are put forward. The application of machine learning techniques to binding data augmented by high-dimensional can provide a powerful tool in compound testing. The emphasis of this work is on making very few assumptions or technical choices with regard to the machine learning techniques. This is to facilitate application of such techniques by non-experts.

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