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Assessment of the underground water contaminated by the leachate of waste dump of open pit coal mine based on RBF neural network

机译:基于RBF神经网络的露天煤矿废弃物渗滤液污染的地下水评估

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This paper used RBF artificial neural network to evaluate the underground water contaminated by the leachate of waste dump of open pit coal mine of Xinqiu in Fuxin. Firstly, with the advantages of neural network method in dealing with nonlinear problem, the RBF neural network model was built. Then, the normalized standard matrix was taken as training sample and the MATLAB software was used to train the training sample. Finally, the monitoring data were taken as test samples and were inputted in the RBF neural network model to evaluate the groundwater quality of study area. At the same time, the concept of degree of membership was adopted in the result making it more objective and accurate. The result shows that the ground water of this mining is seriously polluted, class of its pollution is IV-V.The method with strong classification function and reliable evaluation results is simple and effective, and can be widely applied in all kinds of water resources comprehensive evaluation.
机译:本文使用了RBF人工神经网络评估了阜新Xinqiu露天煤矿废水渗滤液污染的地下水。首先,随着神经网络方法在处理非线性问题时,建立了RBF神经网络模型。然后,将标准化的标准矩阵作为训练样本,并使用MATLAB软件培训训练样本。最后,将监测数据作为测试样本,在RBF神经网络模型中输入,以评估研究区域的地下水质量。与此同时,成员资格的概念是通过的,使其更客观准确。结果表明,这种采矿的地下水受到严重污染,其污染的阶级是IV-V。具有强大分类功能和可靠的评估结果的方法简单有效,可广泛应用于各种水资源全面应用评估。

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