An ANN application for water quality forecasting

被引:393
作者
Palani, Sundarambal [1 ]
Liong, Shie-Yui [1 ]
Tkalich, Pavel [1 ]
机构
[1] Natl Univ Singapore, Trop Marine Sci Inst, Singapore 119223, Singapore
关键词
Singapore seawater; data driven technique; neural network; water quality; prediction; forecasting; Southeast Asia;
D O I
10.1016/j.marpolbul.2008.05.021
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Rapid urban and coastal developments often witness deterioration of regional seawater quality. As part of the management process, it is important to assess the baseline characteristics of the marine environment so that sustainable development can be pursued. In this study, artificial neural networks (ANNs) were used to predict and forecast quantitative characteristics of water bodies. The true power and advantage of this method lie in its ability to (1) represent both linear and non-linear relationships and (2) learn these relationships directly from the data being modeled. The study focuses on Singapore coastal waters. The ANN model is built for quick assessment and forecasting of selected water quality variables at any location in the domain of interest. Respective variables measured at other locations serve as the input parameters. The variables of interest are salinity, temperature, dissolved oxygen, and chlorophyll-a. A time lag up to 2 Delta t appeared to suffice to yield good simulation results. To validate the performance of the trained ANN, it was applied to an unseen data set from a station in the region. The results show the ANN's great potential to simulate water quality variables. Simulation accuracy, measured in the Nash-Sutcliffe coefficient of efficiency (R-2), ranged from 0.8 to 0.9 for the training and overfitting test data. Thus, a trained ANN model may potentially provide simulated values for desired locations at which measured data are unavailable yet required for water quality models. (C) 2008 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1586 / 1597
页数:12
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