# Satellite-Derived Bathymetry for Selected Shallow Maltee Coastal Zones
## 3 Methods
Firstly, from \(2/3\) of the training dataset, \(N\) bootstrap samples, which are a smaller sample taken from a larger sample, are drawn while the remaining \(1/3\) of the training dataset is used to test the error of the predictions [40]. Each bootstrap sample is then used to create an unpruned tree where the best split is determined by randomly selecting \(m\) predictors as a subset of the predictor variables at each node. Unlike linear models, RFs have the advantage of accounting for non-linear interactions between variables and can also adapt to process both numerical and categorical data [41]. In comparison to prior straightforward empirical or semi-empirical models, RFs can develop more adaptable and precise models based on existing data [42]. The K-star (K*) algorithm is an Instance-Based Classifier (IBC). By selecting one transformation at random from all possible transformations, the K* algorithm utilises an entropic metric based on the probability of changing one instance into another [43]. Therefore, this algorithm varies from other instance-based learners as it makes use of an entropy-based distance function [44]. IBC employ a classification approach that involves comparing an instance to a pre-classified database of examples. The underlying assumption is that instances that are similar to each other will have corresponding classifications. The distance function and the classification function are the two principal components that constitute IBC [44]. The distance function calculates the extent of similarity between two instances, whilst the classification function describes the way in which similarities between instances are utilised to arrive at a final classification for a new instance. Essentially, the distance between instances reflects the level of complexity involved in transforming one instance into another [45]. Linear regression (LR) is the simplest form of regression. The LR algorithm attempts to fit a straight line to the dataset. However, this is only plausible when the variables of the dataset are linear [46]. An advantage that the LR algorithm has over other techniques is that it is simple to gauge and is also easy to bypass overfitting by regularisation. Nonetheless, this machine learning algorithm is not recommended for the more practical applications as it oversimplifies real world situations [46]. LR is tasked to predict a dependent variable value based on a given independent variable and finding out a linear relationship between the input and the output in the process. The regression line of the model is the line of best fit thus, reducing the error difference between the true value and the predicted value to a minimum [47]. Multilayer perceptron (MLP) is a feedforward neural network with one or more layers between the input and output layer [48]. The attributes measured for each training tuple correspond to the inputs towards the network. The inputs are transferred into the input layer and are weighted and fed together to a second neuron-like unit layer, labelled as the hidden layer. Additionally, the output of the hidden layer unit can be inputted into another hidden layer, repeating the process [49]. The MLP can mimic any continuous measurable function between the input and output vectors through the selection of appropriate training data and transfer functions [50]. Furthermore, this algorithm is capable of acquiring knowledge in a supervised manner through training that necessitates the provision of a training dataset consisting of inputs and their corresponding output vectors. The MLP is exposed to the training dataset multiple times, with the network's data being adjusted until the desired input-output correlation is attained [51]. The four techniques were trained and tested on the generated dataset. This provided an indication on the error of the predicted values in comparison to the actual depth values. Four graphs of the actual in situ values against the predicted values by each of the tested models were produced, and the correlation coefficient was computed as an error metric.