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Prediction of mine water quality by physical parameters

机译:通过物理参数预测矿井水质

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Present paper is an attempt to predict the chemical parameters like sulphate, chlorine, chemical oxygen demand, total dissolved solids and total suspended solids in mine water using artificial neural network (ANN) by incorporating the pH, temperature and hardness. The prediction by ANN is also compared with Multivariate Regression Analysis (MVRA). For prediction of chemical parameters of mine water, 30 data set were taken for the training of the network while testing and validation of network was done by 10 data set with 923 epochs. The predicted results of chemical parameters of mine water by ANN are very satisfactory and acceptable as compared to MVRA, and seem to be a good alternative for pollutants prediction.
机译:本文试图通过结合pH,温度和硬度,使用人工神经网络(ANN)预测矿井水中的硫酸盐,氯,化学需氧量,总溶解固体和总悬浮固体等化学参数。 ANN的预测也与多元回归分析(MVRA)进行了比较。为了预测矿井水的化学参数,采用了30个数据集进行网络训练,同时通过10个数据集(共923个历元)对网络进行测试和验证。与MVRA相比,ANN对矿井水化学参数的预测结果非常令人满意,可以接受,并且似乎是污染物预测的良好选择。

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