Nuclear Power Plants (NPP) undergo fault and sensitivity analysis with scenario modelling to predict catastrophic events, specifically releases of Cesium 137 (Cs-137). The purpose of this thesis is to find which of 108 input-features from Modular Accident Analysis Program (MAAP)simulation code are important, when there is large release of Cs-137 emissions. The features are tested all together and in their groupings. To find important features, the Machine learning (ML) model Random Forest (RF) has a built-in attribute which identifies important features. The results of RF model classification are corroborated with Support Vector Machines (SVM), K-Nearest Neighbor (KNN) and use k-folds cross validation to improve and validate the results, resulting in a near 90% accuracy for the three ML models. RF is successful at identifying important features related to Cs-137 emissions, by using the classification model to first identify top features, to further train the models at identifying important input-features. The discovered input-features are important both within their individual groups, but also when including all features simultaneously. The large number of features included did not disrupt RF much, but the skewed dataset with few classified extreme events caused the accuracy to be lower at near 90%.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:du-47049 |
| Date | January 2023 |
| Creators | Hedly, Josefin, De Young, Mikaela |
| Publisher | Högskolan Dalarna, Institutionen för information och teknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0014 seconds