Application of artificial neural networks for water quality prediction
Application of artificial neural networks for water quality prediction
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DOI：
10.1007/s00521-012-0940-3
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发表时间：
2013-05-01
影响因子：
6
通讯作者：
El-Shafie, Amr H.
中科院分区：
文献类型：
--
作者：
Najah, A.;El-Shafie, A.;El-Shafie, Amr H.
“水质”一词用于描述水的状况，包括水的化学、物理和生物特性。在任何水生系统的分析中，水质参数建模都是一个非常重要的方面。地表水水质预测是适当管理河流流域所必需的，以便采取适当措施将污染控制在允许的范围内。对未来现象的准确预测是优化水资源管理的生命线。人工神经网络是一种具有灵活数学结构的新技术，与其他经典建模技术相比，它能够识别输入和输出数据之间的复杂非线性关系。柔佛河流域位于马来西亚柔佛州，由于人类活动和沿河开发，柔佛河流域正在严重退化。因此，实施和采用一个能够为更好地实施水资源管理提供有力工具的水质预测模型是非常重要的。研究中采用了线性回归模型(LRM)、多层感知器神经网络和径向基函数神经网络(RBF-NN)等建模方法。结果表明，使用神经网络，尤其是RBF-NN模型可以比线性回归模型更准确地描述水质参数的行为。此外，我们观察到，RBF比MLP更快地找到解，在处理大量非线性、非参数数据方面是最准确和最可靠的工具。
The term "water quality'' is used to describe the condition of water, including its chemical, physical, and biological characteristics. Modeling water quality parameters is a very important aspect in the analysis of any aquatic systems. Prediction of surface water quality is required for proper management of the river basin so that adequate measure can be taken to keep pollution within permissible limits. Accurate prediction of future phenomena is the life blood of optimal water resources management. The artificial neural network is a new technique with a flexible mathematical structure that is capable of identifying complex non-linear relationships between input and output data when compared to other classical modeling techniques. Johor River Basin located in Johor state, Malaysia, which is significantly degrading due to human activities and development along the river. Accordingly, it is very important to implement and adopt a water quality prediction model that can provide a powerful tool to implement better water resource management. Several modeling methods have been applied in this research including: linear regression models (LRM), multilayer perceptron neural networks and radial basis function neural networks (RBF-NN). The results showed that the use of neural networks and more specifically RBF-NN models can describe the behavior of water quality parameters more accurately than linear regression models. In addition, we observed that the RBF finds a solution faster than the MLP and is the most accurate and most reliable tool in terms of processing large amounts of non-linear, non-parametric data.