Identifying Extreme Rainfall Events Using Functional Outliers Detection Methods

Outlier detection techniques play a vital role in exploring unusual data of extreme events that have a critical effect considerably in the modeling and forecasting of functional data. The functional methods have an effective way of identifying outliers graphically, which might not be visible through the original data plot in classical analysis. This study’s main objective is to detect the extreme rainfall events using functional outliers detection methods depending on the depth and density functions. In order to identify the unusual events of rainfall variation over long time intervals, this work conducts based on the average monthly rainfall of the Taiz region from 1998 to 2019. Data were extracted from the Tropical Rainfall Measuring Mission and the analysis has been processed by R software. The approaches applied in this study involve rainbow plots, functional highest density region box-plot as well as functional bag-plot. According to the current results, the functional density box-plot method has proven effective in detecting outlier compared to the functional depth bag-plot method. In conclusion, the results of the current study showed that the rainfall over the Taiz region during the last two decades was influenced by the extreme events of years 1999, 2004, 2005, and 2009.

A new method for fitting the complicated water level process of the lower Yellow River

With the view of effectively fitting the complicated water level process of the lower Yellow River, polynomial regression, stepwise regression, parameters by ridge estimate and so on, are logically integrated. And the progressive transformation is introduced. Then a new method is put forward. The core difference of this new method from the same kind of methods lies in that in this method the strong coupling effect of weak influencing factors which is common in a complicated water level process is considered, that many effective methods are synthetically used to reduce the fitting model error, and that the necessary progressive transformation is introduced. The advantages of many theories and methods are logically integrated in this method, and the method can be easily used. The rationality and necessity of each step in this method are ensured by sufficient theories, so this method can be widely used to effectively simulate the inherent relations in the same kind of complicated data. Furthermore, many complicated water level processes of the lower Yellow River are fitted by this method, and all the fitting precisions are markedly higher than the precision by the other existing methods. Every component term in the fitting model has clear physical meaning.