Based on the original Statistical Inventory Reconciliation(SIR) Test Method (Quantitative), K-folds cross validation is used to increase P(D) and decrease P(FA) by adjusting K, which are related to bias and standard deviation. There is a trade-off between bias and variance, with very flexible models (overfit) having low bias and high variance, and relatively rigid models(underfit) having high bias and low variance. When K is larger, we have lower bias and larger standard deviation. Also, K-folds cross validation is very useful, when data size is small.
The industry partner, Cantest is establishing a new leak detection procedure for analyzing data sources in aboveground storage tanks and statistical learning models to monitor AST shell dynamics and product activity over time. This is an important problem as identifying leak detection is usually associated with various environmental data and records collected from sensitive sensors attached to the ASTs. Current testing procedure for leak detection uses simple statistical rules and thresholds to detect anomalies. These methods are failing for preventing AST related environmental incidents.