A Study of Artificial Intelligence Techniques for the Estimation of the Arsenic Variation in the Regional Groundwater System
Date Issued
2011
Date
2011
Author(s)
Kao, Li-Shan
Abstract
Artificial intelligence is extensively applied to hydrological systems and is successfully implemented in the quantitative estimation of water quality. However, artificial intelligence techniques are seldom employed in the prediction of groundwater quality. The features of the groundwater pollution include imperceptibility, complex affective factors and limited data. It is not easy to employee traditional models for estimating the water quality in groundwater systems. Arsenic (As) proves to be a main factor of black-foot disease and threatens the health of residents. Constructing a reliable model for estimating arsenic concentration in groundwater is essential. Therefore, the aim of this study is to construct an artificial neural network (ANN) model for estimating arsenic concentration in groundwater systems.
From 1992 to 2005, the government takes into account the serious arsenic pollution that occurred in the coastal area of the Yun-Lin County in Taiwan and set up 28 monitoring wells for investigating the pollution in groundwater. The collected water quality data were used when constructing models in this study. However, due to limited budget and/or human factors, some arsenic concentration data from these wells were missing, which affects the realization of the pollution in groundwater. The first subject of this study is to construct a spatial model for estimating missing data by applying ANN. During the process of model construction, inaccuracy and over-fitting commonly occur in sparse data. To overcome these problems, the principal component analysis, the cross-validation and the modified performance function are employed when constructing the model. These methods have the ability to effectively alleviate the over-fitting problem and improve model accuracy. On the other hand, searching and identifying the optimal ANN structure is quite time and labor consuming. Genetic algorithm is used to identify the effective input factors and the suitable number of neurons in hidden layer.
Another subject of this study is to build a water quality assessment model for arsenic concentration by analyzing the relationship between arsenic concentration and other water quality factors in groundwater. This subject has two scenarios: one is for the single well model; and the other is for the regional model. Results indicate that the affective factors of arsenic concentration significantly vary from the north to the south in the coastal area of the Yun-Lin County. Overall, the single well model performs better than the regional model, despite that the regional model can be extensively applied over the study area. Finally, the results of the spatial and water quality models are applied to displaying the distribution map of arsenic pollution so that groundwater managers can easily realize the temporal and spatial variation in arsenic concentration during 1992 and 2005. The information of arsenic variation can reduce the risk of drinking contaminated groundwater for local residents and effectively enhance the control and management of arsenic pollution in groundwater.
Subjects
Arsenic
Groundwater quality
Artificial neural network (ANN)
Back-propagation neural networks (BPNN)
Modified performance function (MPF)
Principal component analysis (PCA)
Genetic algorithm (GA)
SDGs
Type
thesis
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