{"title":"利用统计和人工神经网络技术分析水质数据","authors":"Joydeep Dutta, Sudip Basack, Ghritartha Goswami","doi":"10.1007/s11270-024-07458-x","DOIUrl":null,"url":null,"abstract":"<div><p>An effort has been made to develop statistical and ANN models for estimation of sodium concentration in pre-monsoon and post-monsoon seasons using routinely monitored water quality parameters of ground water wells in Jaipur district, Rajasthan (India). The Best Subset procedure based on R<sup>2</sup> (coefficient of determination) and F (Fishers test) values was used in model dissemination. It was found that electrical conductivity, hardness, chloride, and sulphate could be used as surrogate parameters for the prediction of sodium. The model values of Na when compared with actual values (validation) showed a reasonably good matching. Further it was noticed that there was not a single model which could be used to predict the Na levels. It is primarily attributed to the fact that sodium concentration not only varies from site to site but also varies from season to season. Secondly, Principal component analysis was used to predict the dominating water quality constituents and it was revealed that four principal components are accounted for the total chemical variability in the ground water quality for pre-monsoon season and three principal components for post-monsoon season, respectively. The common factors conductivity, fluoride, nitrate, alkalinity, and phosphate have perceptible influence on the quality of groundwater of Jaipur district, Rajasthan. Finally, Back Propagation, three layered ANN models for both pre-monsoon and post-monsoon season was developed for estimation of sodium using the steepest descent optimization technique. ANN models were developed considering a fixed number of iterations as 1000 and the developed models were verified on the data not considered in calibration. 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引用次数: 0
摘要
本研究利用印度拉贾斯坦邦斋浦尔地区地下水井的常规监测水质参数，开发了统计和 ANN 模型，用于估算季风前和季风后季节的钠浓度。基于 R2（判定系数）和 F（菲舍尔检验）值的最佳子集程序被用于模型推广。结果发现，电导率、硬度、氯化物和硫酸盐可用作预测钠含量的替代参数。钠的模型值与实际值（验证）相比，显示出相当好的匹配性。此外，我们还注意到，没有一个单一的模型可用于预测 Na 含量。这主要是由于钠浓度不仅因地点而异，而且因季节而异。其次，采用主成分分析法预测主要的水质成分，结果表明，季风前和季风后地下水水质的总化学变异分别由四个主成分和三个主成分构成。共同因子电导率、氟化物、硝酸盐、碱度和磷酸盐对拉贾斯坦邦斋浦尔地区的地下水水质有明显影响。最后，利用最陡下降优化技术为季风前和季风后季节开发了反向传播三层 ANN 模型，用于估算钠含量。在开发 ANN 模型时，将迭代次数固定为 1000 次，并根据校准时未考虑的数据对所开发的模型进行了验证。通过相关性分析确定了不同模型结构所考虑的输入变量。根据统计性能评估标准，如均方根误差 (RMSE)、相关系数 (CC) 和效率系数 (CE)，结果表明基于 ANN 的模型性能令人满意。
Analysis of Water Quality Data Using Statistical and Artificial Neural Network Techniques
An effort has been made to develop statistical and ANN models for estimation of sodium concentration in pre-monsoon and post-monsoon seasons using routinely monitored water quality parameters of ground water wells in Jaipur district, Rajasthan (India). The Best Subset procedure based on R2 (coefficient of determination) and F (Fishers test) values was used in model dissemination. It was found that electrical conductivity, hardness, chloride, and sulphate could be used as surrogate parameters for the prediction of sodium. The model values of Na when compared with actual values (validation) showed a reasonably good matching. Further it was noticed that there was not a single model which could be used to predict the Na levels. It is primarily attributed to the fact that sodium concentration not only varies from site to site but also varies from season to season. Secondly, Principal component analysis was used to predict the dominating water quality constituents and it was revealed that four principal components are accounted for the total chemical variability in the ground water quality for pre-monsoon season and three principal components for post-monsoon season, respectively. The common factors conductivity, fluoride, nitrate, alkalinity, and phosphate have perceptible influence on the quality of groundwater of Jaipur district, Rajasthan. Finally, Back Propagation, three layered ANN models for both pre-monsoon and post-monsoon season was developed for estimation of sodium using the steepest descent optimization technique. ANN models were developed considering a fixed number of iterations as 1000 and the developed models were verified on the data not considered in calibration. The input variables considered for different model structures were identified through correlation analysis. Based on the statistical performance evaluation criteria such as root mean square error (RMSE), correlation coefficient (CC), and coefficient of efficiency (CE), the results indicated satisfactory performance of ANN based model.
期刊介绍：
Water, Air, & Soil Pollution is an international, interdisciplinary journal on all aspects of pollution and solutions to pollution in the biosphere. This includes chemical, physical and biological processes affecting flora, fauna, water, air and soil in relation to environmental pollution. Because of its scope, the subject areas are diverse and include all aspects of pollution sources, transport, deposition, accumulation, acid precipitation, atmospheric pollution, metals, aquatic pollution including marine pollution and ground water, waste water, pesticides, soil pollution, sewage, sediment pollution, forestry pollution, effects of pollutants on humans, vegetation, fish, aquatic species, micro-organisms, and animals, environmental and molecular toxicology applied to pollution research, biosensors, global and climate change, ecological implications of pollution and pollution models. Water, Air, & Soil Pollution also publishes manuscripts on novel methods used in the study of environmental pollutants, environmental toxicology, environmental biology, novel environmental engineering related to pollution, biodiversity as influenced by pollution, novel environmental biotechnology as applied to pollution (e.g. bioremediation), environmental modelling and biorestoration of polluted environments.
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Water, Air, & Soil Pollution publishes research papers; review articles; mini-reviews; and book reviews.
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