Descriptive Characteristics of Surface Water Quality in Hong Kong by a Self-Organising Map

Abstract

In this study, principal component analysis (PCA) and a self-organising map (SOM) were used to analyse a complex dataset obtained from the river water monitoring stations in the Tolo Harbor and Channel Water Control Zone (Hong Kong), covering the period of 2009–2011. PCA was initially applied to identify the principal components (PCs) among the nonlinear and complex surface water quality parameters. SOM followed PCA, and was implemented to analyze the complex relationships and behaviors of the parameters. The results reveal that PCA reduced the multidimensional parameters to four significant PCs which are combinations of the original ones. The positive and inverse relationships of the parameters were shown explicitly by pattern analysis in the component planes. It was found that PCA and SOM are efficient tools to capture and analyze the behavior of multivariable, complex, and nonlinear related surface water quality data.

Assessment of the Eutrophication-Related Environmental Parameters in Two Mediterranean Lakes by Integrating Statistical Techniques and Self-Organizing Maps

“…The SOM is consisted of an input layer and an output layer that are connected with computational weights [ 43 , 44 ]. The output layer consists of neurons that are arranged in a hexagonal or rectangular grid and are fully interconnected [ 11 ].…”

“…The Euclidean distance ( D i ) mentioned above is described by the following equation, and calculates the distance measure between the input vector and the i weight vector [ 44 , 45 ]: where S is the number of output neurons, R is the dimension of the input vectors, p ij represents the j element of the input vector, and w ij symbolizes the j element of the i weight vector. The term BMU is defined, according to Lee & Scholz [ 15 ], as the neuron with the weight vector closest to the input variable x , as given by the equation: where| |symbolizes the distance measure, x the input vector, m the weight vector, and c the subscription of the weight vector for the winning neuron.…”

Recognizing the Relationship between Spatial Patterns in Water Quality and Land-Use/Cover Types: A Case Study of the Jinghe Oasis in Xinjiang, China

“…Accordingly, the k-means algorithm is applied to keep each cluster compact and separate the clusters from each other. The Davies-Bouldin clustering index was used to determine the optimal number of clusters for the dataset [16,28]. The lower the Davies-Bouldin index value is, the better the clusters are differentiated.…”

Application of a Self-Organizing Map of Isotopic and Chemical Data for the Identification of Groundwater Recharge Sources in Nasunogahara Alluvial Fan, Japan

“…Each component plane shows the value of one component of the reference vector in each of the 72 nodes. Visualization of the data using component planes is helpful for finding interrelationships among the different parameters [21]. For example, the component plane patterns of Na + and Cl − were visibly similar; both had larger values in the lowerleft and smaller values in the upper-left part of the plane (Figure 10).…”

“…Therefore, a self-organizing map (SOM) approach, a neural network-based pattern analysis with unsupervised learning, has recently been applied to map changes in groundwater levels and chemistry in space and time (e.g., [17][18][19][20]). An SOM, which can cluster a set of hydrochemical data into two or more independent groups, is superior to other statistical tools because it can: (1) deal with system nonlinearities, (2) be developed from data without requiring mechanistic knowledge of the system, (3) handle noisy or irregular data and be easily and quickly updated, and (4) be used to interpret and visualize information of multiple variables or parameters [16,21].…”