  The main goal of machine learning (ML) is to study and improve mathematical
models which can be trained with data provided by the environment to infer the
future and to make decisions without necessarily having complete knowledge of
all influencing elements. In this work, we describe how ML can be a powerful
tool in studying climate modeling. Tree ring growth was used as an
implementation in different aspects, for example, studying the history of
buildings and environment. By growing and via the time, a new layer of wood to
beneath its bark by the tree. After years of growing, time series can be
applied via a sequence of tree ring widths. The purpose of this paper is to use
ML algorithms and Extreme Value Theory in order to analyse a set of tree ring
widths data from nine trees growing in Nottinghamshire. Initially, we start by
exploring the data through a variety of descriptive statistical approaches.
Transforming data is important at this stage to find out any problem in
modelling algorithm. We then use algorithm tuning and ensemble methods to
improve the k-nearest neighbors (KNN) algorithm. A comparison between the
developed method in this study ad other methods are applied. Also, extreme
value of the dataset will be more investigated. The results of the analysis
study show that the ML algorithms in the Random Forest method would give
accurate results in the analysis of tree ring widths data from nine trees
growing in Nottinghamshire with the lowest Root Mean Square Error value. Also,
we notice that as the assumed ARMA model parameters increased, the probability
of selecting the true model also increased. In terms of the Extreme Value
Theory, the Weibull distribution would be a good choice to model tree ring
data.
