Data
climate-model-simulation-crashes

climate-model-simulation-crashes

active ARFF Publicly available Visibility: public Uploaded 19-12-2017 by Jann Goschenhofer
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Author: D. Lucas, R. Klein, J. Tannahill, D. Ivanova, S. Brandon, D. Domyancic, Y. Zhang. Source: [UCI](https://archive.ics.uci.edu/ml/datasets/climate+model+simulation+crashes) Please Cite: Lucas, D. D., Klein, R., Tannahill, J., Ivanova, D., Brandon, S., Domyancic, D., and Zhang, Y.: Failure analysis of parameter-induced simulation crashes in climate models, Geosci. Model Dev. Discuss., 6, 585-623, [Web Link](http://www.geosci-model-dev-discuss.net/6/585/2013/gmdd-6-585-2013.html), 2013. __Major changes w.r.t. version 1: deactivated first two variables as they describe the batch of the experiments and should not be used for prediction. Also transformed the target from numeric to factor type.__ ### Source D. Lucas (ddlucas .at. alum.mit.edu), Lawrence Livermore National Laboratory; R. Klein (rklein .at. astron.berkeley.edu), Lawrence Livermore National Laboratory & U.C. Berkeley; J. Tannahill (tannahill1 .at. llnl.gov), Lawrence Livermore National Laboratory; D. Ivanova (ivanova2 .at. llnl.gov), Lawrence Livermore National Laboratory; S. Brandon (brandon1 .at. llnl.gov), Lawrence Livermore National Laboratory; D. Domyancic (domyancic1 .at. llnl.gov), Lawrence Livermore National Laboratory; Y. Zhang (zhang24 .at. llnl.gov), Lawrence Livermore National Laboratory . This data was constructed using LLNL's UQ Pipeline, was created under the auspices of the US Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, was funded by LLNL's Uncertainty Quantification Strategic Initiative Laboratory Directed Research and Development Project under tracking code 10-SI-013, and is released under UCRL number LLNL-MISC-633994. ### Data Set Information This dataset contains records of simulation crashes encountered during climate model uncertainty quantification (UQ) ensembles. Ensemble members were constructed using a Latin hypercube method in LLNL's UQ Pipeline software system to sample the uncertainties of 18 model parameters within the Parallel Ocean Program (POP2) component of the Community Climate System Model (CCSM4). Three separate Latin hypercube ensembles were conducted, each containing 180 ensemble members. 46 out of the 540 simulations failed for numerical reasons at combinations of parameter values. The goal is to use classification to predict simulation outcomes (fail or succeed) from input parameter values, and to use sensitivity analysis and feature selection to determine the causes of simulation crashes. Further details about the data and methods are given in the publication 'Failure Analysis of Parameter-Induced Simulation Crashes in Climate Models,' Geoscientific Model Development [(Web Link)](doi:10.5194/gmdd-6-585-2013). ### Attribute Information The goal is to predict climate model simulation outcomes (column 19, fail or succeed) given scaled values of climate model input parameters (columns 1-18). - Columns 3-20: values of 18 climate model parameters scaled in the interval [0, 1] - Column 21: simulation outcome (0 = failure, 1 = success) Relevant Papers: Lucas, D. D., Klein, R., Tannahill, J., Ivanova, D., Brandon, S., Domyancic, D., and Zhang, Y.: Failure analysis of parameter-induced simulation crashes in climate models, Geosci. Model Dev. Discuss., 6, 585-623, [Web Link](http://www.geosci-model-dev-discuss.net/6/585/2013/gmdd-6-585-2013.html), 2013.

21 features

outcome (target)nominal2 unique values
0 missing
Study (ignore)numeric3 unique values
0 missing
Run (ignore)numeric180 unique values
0 missing
vconst_corrnumeric540 unique values
0 missing
vconst_2numeric540 unique values
0 missing
vconst_3numeric540 unique values
0 missing
vconst_4numeric540 unique values
0 missing
vconst_5numeric540 unique values
0 missing
vconst_7numeric540 unique values
0 missing
ah_corrnumeric540 unique values
0 missing
ah_bolusnumeric540 unique values
0 missing
slm_corrnumeric540 unique values
0 missing
efficiency_factornumeric540 unique values
0 missing
tidal_mix_maxnumeric540 unique values
0 missing
vertical_decay_scalenumeric540 unique values
0 missing
convect_corrnumeric540 unique values
0 missing
bckgrnd_vdc1numeric540 unique values
0 missing
bckgrnd_vdc_bannumeric540 unique values
0 missing
bckgrnd_vdc_eqnumeric540 unique values
0 missing
bckgrnd_vdc_psimnumeric540 unique values
0 missing
Prandtlnumeric540 unique values
0 missing

62 properties

540
Number of instances (rows) of the dataset.
21
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
20
Number of numeric attributes.
1
Number of nominal attributes.
-0
First quartile of skewness among attributes of the numeric type.
0.5
Mean of means among attributes of the numeric type.
0.29
First quartile of standard deviation of attributes of the numeric type.
0.84
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
0.42
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
-1.2
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.04
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
0.5
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
91.48
Percentage of instances belonging to the most frequent class.
0.29
Mean standard deviation of attributes of the numeric type.
-0
Second quartile (Median) of skewness among attributes of the numeric type.
494
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
4.76
Percentage of binary attributes.
0.29
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.2
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
-1.2
Maximum kurtosis among attributes of the numeric type.
0.5
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
-1.2
Third quartile of kurtosis among attributes of the numeric type.
0.5
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
95.24
Percentage of numeric attributes.
0.5
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
4.76
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
-0
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
0
Third quartile of skewness among attributes of the numeric type.
0
Maximum skewness among attributes of the numeric type.
0.29
Minimum standard deviation of attributes of the numeric type.
-1.2
First quartile of kurtosis among attributes of the numeric type.
0.29
Third quartile of standard deviation of attributes of the numeric type.
0.29
Maximum standard deviation of attributes of the numeric type.
8.52
Percentage of instances belonging to the least frequent class.
46
Number of instances belonging to the least frequent class.
0.5
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
-1.2
Mean kurtosis among attributes of the numeric type.

5 tasks

6508 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: outcome
0 runs - estimation_procedure: 33% Holdout set - target_feature: outcome
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: outcome
0 runs - estimation_procedure: 50 times Clustering
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