Department of Irrigation & Reclamation Engineering, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Tehran, Iran.
Department of Geography, University of California, Santa Barbara, CA, 93016-4060, USA.
Environ Monit Assess. 2020 Jul 2;192(7):482. doi: 10.1007/s10661-020-08454-4.
Water pollution is a concern in the management of water resources. This paper presents a statistical approach for data mining of patterns of water pollution in reservoirs. Genetic programming (GP), artificial neural network (ANN), and support vector machine (SVM) are applied to reservoir quality modeling. Input data for GP, ANN, and SVM were derived with the CE-QUAL-W2 numerical water quality simulation model. A case study was carried out using measured reservoir inflow and outflow, temperature, and nitrate concentration to the Amirkabir reservoir, Iran. Data mining models were evaluated with the MAE, NSE, RMSE, and R goodness-of-fit criteria. The results indicated that using the SVM model for determining nitrate pollution is time saving and more accurate in comparison with GP, ANN, and particularly CE-QUAL-W2. The SVM model reduces the runtime of nitrate concentration simulation by 581, 276, and 146 s compared with CE-QUAL-W2, GP, and ANN, respectively. The goodness-of-fit results showed that the highest values (R = 0.97, NSE = 0.92) and the lowest values (MAE = 0.034 and RMSE = 0.007) corresponded to SVM predictions, indicating higher model accuracy. This study demonstrates the potential for application of data mining tools to solute concentration simulation in reservoirs.
水污染是水资源管理中的一个关注点。本文提出了一种统计方法，用于挖掘水库水污染模式的数据挖掘。遗传编程（GP）、人工神经网络（ANN）和支持向量机（SVM）被应用于水库水质建模。GP、ANN 和 SVM 的输入数据是由 CE-QUAL-W2 数值水质模拟模型推导出来的。对伊朗 Amirkabir 水库的实测水库入流、出流、温度和硝酸盐浓度进行了案例研究。使用 MAE、NSE、RMSE 和 R 拟合优度标准对数据挖掘模型进行了评估。结果表明，与 GP、ANN 相比，SVM 模型用于确定硝酸盐污染更节省时间且更准确，特别是与 CE-QUAL-W2 相比。SVM 模型将硝酸盐浓度模拟的运行时间分别缩短了 581、276 和 146 秒。拟合优度结果表明，SVM 预测的最高值（R = 0.97，NSE = 0.92）和最低值（MAE = 0.034 和 RMSE = 0.007）对应于更高的模型精度。本研究表明，数据挖掘工具在水库溶质浓度模拟中的应用具有潜力。
