Artificial Neural Networks for Assessing Arsenic Concentrations and Characteristics of Groundwater Quality in the Coastal Area of Yun-Lin, Taiwan
Date Issued
2012
Date
2012
Author(s)
Lin, Cheng-Hua
Abstract
In the past, Blackfoot disease commonly occurred along the southwestern coast of Taiwan. A number of investigations revealed this epidemic disease was highly related to arsenic (As) concentration in groundwater, which is the main source of drinking water to local residents. Although local residents do not directly drink groundwater any more in the Yun-Lin County of Taiwan, groundwater is still a main water source in this area because surface water suffers from limited sources. A large quantity of groundwater has been extracted from the aquifer for supplying water to the public, fish ponds and crop lands, which has resulted in the accumulation of arsenic in crops and fish. Products highly-contaminated with arsenic has threatened the health of residents. Therefore, it’s essential to construct a reliable model for estimating As concentration in groundwater.
The aims of this study are to assess the characteristics of groundwater quality, extract the factors affecting As concentration, investigate the sources releasing As, and estimate As concentration in groundwater. The Water Resources Agency (WRA) have set up 28 monitoring wells for investigating groundwater pollution, and water quality data collected by the WRA were used in this study. The first subject of this study is to import all the data collected form 28 wells to the Self-Organizing Feature Map (SOM) network. The SOM was applied to classifying all the water quality data into a topology map for finding the hidden relations among data and the spatial patterns between water quality variables and As concentration. Then the clustering results were adopted as the centroids of the Radial Basis Function Neural Networks (RBFNN) for accurately estimating As concentration based on water quality variables. In addition, the Back Propagation Neural Network (BPNN) was built to compare with the proposed model that integrates the SOM and RBF. The results demonstrate that the performance of the proposed model is better than the BPNN. When comparing the clustering results, adding As concentration to the SOM could make the clustering results more obvious and therefore achieves much accurate estimation. Moreover, the results demonstrate the characteristics of groundwater quality in coastal areas correlate with salinization and arsenic pollution factors. According to the clustering results, we surmise that the occurrence of high arsenic concentration in parts of wells is mainly because groundwater is in the reduction phase, especially at higher pH and Alk values and lower dissolved oxygen levels and SO42- concentration. Finally, the Geographic Information System (GIS) is applied to the results of groundwater quality models for displaying the spatial distribution map of As pollution so that we can realize the temporal and spatial variation in arsenic concentration in the study area.
Subjects
Arsenic
Groundwater quality
Artificial neural network
Self-Organizing Feature Map
Radial Basis Function Neural Networks
As
ANN
SOM
RBFNN
Type
thesis
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