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首页> 外文期刊>African Journal of Agricultural Research >Prediction of water quality parameter in Jajrood River basin: Application of multi layer perceptron (MLP) perceptron and radial basis function networks of artificial neural networks (ANNs)
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Prediction of water quality parameter in Jajrood River basin: Application of multi layer perceptron (MLP) perceptron and radial basis function networks of artificial neural networks (ANNs)

机译：Jajrood流域水质参数的预测：多层感知器（MLP）感知器和人工神经网络（RNN）的径向基函数网络的应用

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River water quality is a significant concern in many countries, considering agricultural and drinking consumptions. Therefore, prediction of salinity index, as the main water quality condition is a necessary tool for water resources planning and management. This paper describes the application of artificial neural networks (ANNs) models for computing the total dissolved solids (TDS) level in Jajrood River (Iran). Two ANN networks, multi-layer perceptron (MLP) and radial basis function (RBF), were identified, validated and tested for the computation of TDS concentrations. Both networks employed five input water quality variables measured in river water over a period of 40 years. The performance of the ANN models was checked through the coefficient of determination (R2) and root mean square error (RMSE). Jajrood River is one of the most important rivers which is located adjacent to Tehran city and supplies drinking water for people who live in this mega-city and recreational uses. Tehran is the most populous city and largest industrial pole in Iran, which caused the river, to be exposed to various pollutants. Matlab 2007 was selected for modeling goals in this research. Results show that MLP and RBF modeling as two methods of ANN are able to simulate water quality variables of Jajrood River with more than 90% accuracy. After modeling in MLP and RBF formatting and comparing simulation results (output) show that, the RBF result (R2?of validation is 0.9362) are more closely to reality than the MLP (R2?of validation is 0.8968). In other words, because of large number of input data, the RBF modeling performance has a better prediction than MLP modeling.
机译：考虑到农业和饮用水的消耗，河水水质在许多国家是一个重大问题。因此，作为主要水质状况的盐度指数预测是水资源规划与管理的必要工具。本文介绍了人工神经网络（ANN）模型在计算Jajrood河（伊朗）中的总溶解固体（TDS）水平中的应用。为计算TDS浓度，鉴定，验证和测试了两个ANN网络，即多层感知器（MLP）和径向基函数（RBF）。两个网络都使用了40年中在河水中测得的五个输入水质变量。通过确定系数（R2）和均方根误差（RMSE）来检查ANN模型的性能。杰伊德罗德河（Jajrood River）是最重要的河流之一，毗邻德黑兰市，为生活在这个特大城市和娱乐场所的人们提供饮用水。德黑兰是伊朗人口最多的城市，也是最大的工业极地，导致该河暴露于各种污染物。选择Matlab 2007作为本研究的建模目标。结果表明，作为神经网络的两种方法，MLP和RBF建模能够准确模拟Jajrood河的水质变量，准确率超过90％。在以MLP和RBF格式建模并比较模拟结果（输出）后，发现RBF结果（验证的R2？为0.9362）比MLP（验证的R2？为0.8968）更接近实际。换句话说，由于大量输入数据，RBF建模性能比MLP建模具有更好的预测。

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