Department of Civil Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, Jharkhand, India.
Department of Civil Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, Jharkhand, India..
J Contam Hydrol. 2020 Nov;235:103718. doi: 10.1016/j.jconhyd.2020.103718. Epub 2020 Sep 23.
Globally, groundwater heavy metal (HM) pollution is a serious concern, threatening drinking water safety as well as human and animal health. Therefore, evaluation of groundwater HM pollution is essential to prevent accompanying hazardous ecological impacts. In this aspect, the effectiveness of various groundwater HM pollution evaluation approaches should be examined for their level of trustworthiness. In this study, 226 groundwater samples from Arang of Chhattisgarh state, India, were collected and analyzed. Measured concentration for various HMs were further used to calculate six groundwater pollution indices, such as the HM pollution index (HPI), HM evaluation index (HEI), contamination index (CI), entropy-weight based HM contamination index (EHCI), Heavy metal index (HMI), and principal component analysis-based metal index (PMI). Groundwater in the study area was mainly contaminated by elevated Cd, Fe, and Pb concentrations due to natural and anthropogenic pollution. Moreover, this study explored the performance of deep learning (DL)-based predictive models via comparative study. Two hidden layers with 26 and 19 neurons in the first and second hidden layers, respectively, were optimised along with rectified linear unit activation function. A mini-batch gradient descent was also applied to ensure smooth convergence of the training dataset into the model. Results demonstrated that the DL-PMI scored lowest errors, 0.022 for mean square error (MSE), 0.140 for mean absolute error (MAE), and 0.148 for root mean square error (RMSE), in the model validation than the other DL-based groundwater HM pollution model. Prediction performances of all pollution indices were also verified using artificial neural network (ANN)-based models, which also highlighted the lowest validation error for ANN-PMI (MSE = 3.93, MAE = 1.38, and RMSE = 1.98). Furthermore, the prediction accuracies of PMI using both ANN and DL models scored the highest R value of 0.95 and 0.99, respectively. Therefore it is suggested that groundwater HM pollution using PMI as the best indexing approach in the present study area. Moreover, compared to benchmark, ANN, the DL performed better; hence, it could be concluded that the proposed DL model may be suitable approach in the field of computational chemistry by handling overfitting problems.
全球范围内，地下水重金属（HM）污染是一个严重的问题，威胁着饮用水安全以及人类和动物的健康。因此，评估地下水 HM 污染对于防止伴随而来的生态危害至关重要。在这方面，需要检查各种地下水 HM 污染评估方法的有效性，以确定其可信度。在本研究中，从印度恰蒂斯加尔邦的阿兰格采集了 226 个地下水样本并进行了分析。进一步使用所测的各种 HM 的浓度来计算六个地下水污染指数，如重金属污染指数（HPI）、重金属评价指数（HEI）、污染指数（CI）、基于熵权的重金属污染指数（EHCI）、重金属指数（HMI）和基于主成分分析的金属指数（PMI）。研究区地下水主要受到 Cd、Fe 和 Pb 浓度升高的污染，这是由于自然和人为污染造成的。此外，本研究还通过比较研究探索了基于深度学习（DL）的预测模型的性能。优化了具有 26 个和 19 个神经元的两个隐藏层，分别在第一个和第二个隐藏层中，激活函数为修正线性单元。还应用了小批量梯度下降，以确保训练数据集顺利收敛到模型中。结果表明，与其他基于 DL 的地下水 HM 污染模型相比，DL-PMI 在模型验证中得分最低，平均平方误差（MSE）为 0.022，平均绝对误差（MAE）为 0.140，均方根误差（RMSE）为 0.148。还使用基于人工神经网络（ANN）的模型验证了所有污染指数的预测性能，结果也突出了 ANN-PMI 的最低验证误差（MSE=3.93，MAE=1.38，RMSE=1.98）。此外，使用 ANN 和 DL 模型的 PMI 预测精度的 R 值最高，分别为 0.95 和 0.99。因此，建议在本研究区域使用 PMI 作为最佳索引方法评估地下水 HM 污染。此外，与基准相比，DL 表现更好；因此，可以得出结论，所提出的 DL 模型在处理过度拟合问题方面可能是计算化学领域的合适方法。
