A watershed water quality prediction model based on attention mechanism and Bi-LSTM

Environmental Science and Pollution Research - Accurate prediction of water quality contributes to the intelligent management and control of watershed ecology. Water Quality data has time series...     

水质评价中模糊-集对分析法的改进

针对水环境的不确定性和模糊性,基于集对分析法和模糊理论相结合的评价模型已被广泛运用到水质评价当中。传统的评价模型在确定水质等级的复合算法中虽引入了权值的概念,但仍会出现指标浓度重复计算、信息丢失和指标权值的影响得不到体现等缺陷,具有局限性。针对这些缺陷,将基于加权后的内梅罗指数法思想应用于传统模糊-集对分析法中的复合算法上,考虑到加权内梅罗指数法兼顾极值的特性及计权性,通过取平均值和极值替代累加的方法来实现减小指标浓度重复计算的影响,同时加强复合算法中权值所占比重使得权值的作用得到充分体现。实验表明,改进的复合算法使得评价结果更为精确、客观。     

An efficient supply management in water flow network using graph spectral techniques

An efficient way of distributing water in urban cities is one of the major global challenges of our time. A Water Distribution Network (WDN) is a vital infrastructure, intended to provide fresh water to households within a city or designated boundary. Given the WDN’s complexity, effective numerical techniques are required to aid in the development of an ideal monitoring system. Flow meters and gate valves should be placed in low-connected areas of the WDN with water flow reaching several regions of the network. This study proposes a general strategy for assisting water utilities in making decisions for effective water supply. The aim of the research is to use weighted spectral clustering algorithms to outline water districts while addressing hydraulic restrictions via weighted adjacency and Laplacian matrices of the weighted network. This work aims to identify influenced nodes in the network based on Eigen centrality and effectively distribute water across those nodes. This project also looks at how to measure the network’s connection strength to avoid water leaks. The best clusters are found using Eigenvalues and Eigenvectors of weighted adjacency matrices and Laplacian matrices of the water network in the proposed graph spectral framework. In order to establish the optimum water network division approach, topological and graph metrics were used to compare multiple spectral clustering techniques. The proposed graph spectral approach is tested using a genuine water distribution network serving an urban area of Coimbatore city in India and offering a method for partitioning complex networks that employ the spectral graph partitioning algorithm.

     

基于时域权重矩阵的模糊综合水质评价法及其应用

水质评价作为合理开发和保护水资源的一项重要工作,越来越受到人们重视。传统的模糊综合水质评价方法因未考虑到水质指标权重的季节变化而可能影响评价结果,故对水质指标权重向量进行了改进,提出了时域权重矩阵的概念,并将实测权重向量与时域权重矩阵相结合,进行组合赋权,得到综合权重向量。应用改进的模糊综合评价方法对某引水明渠水质进行综合评价,结果相比于传统模糊综合水质评价法更符合实际。     

Concrete slump prediction modeling with a fine-tuned convolutional neural network: hybridizing sea lion and dragonfly algorithms

Environmental Science and Pollution Research - High-strength concrete (HSC) is defined as concrete that meets a special combination of uniformity and performance requirements, which cannot be...     

A convolutional neural network approach for detection of E. coli bacteria in water

Environmental Science and Pollution Research - The detection of Escherichia coli bacteria is essential to prevent health diseases. According to the laboratory-based methods, 12–48 h is...     

Assessment and rationalization of water quality monitoring network: a multivariate statistical approach to the Kabbini River (India)

The establishment of an efficient surface water quality monitoring (WQM) network is a critical component in the assessment, restoration and protection of river water quality. A periodic evaluation of monitoring network is mandatory to ensure effective data collection and possible redesigning of existing network in a river catchment. In this study, the efficacy and appropriateness of existing water quality monitoring network in the Kabbini River basin of Kerala, India is presented. Significant multivariate statistical techniques like principal component analysis (PCA) and principal factor analysis (PFA) have been employed to evaluate the efficiency of the surface water quality monitoring network with monitoring stations as the evaluated variables for the interpretation of complex data matrix of the river basin. The main objective is to identify significant monitoring stations that must essentially be included in assessing annual and seasonal variations of river water quality. Moreover, the significance of seasonal redesign of the monitoring network was also investigated to capture valuable information on water quality from the network. Results identified few monitoring stations as insignificant in explaining the annual variance of the dataset. Moreover, the seasonal redesign of the monitoring network through a multivariate statistical framework was found to capture valuable information from the system, thus making the network more efficient. Cluster analysis (CA) classified the sampling sites into different groups based on similarity in water quality characteristics. The PCA/PFA identified significant latent factors standing for different pollution sources such as organic pollution, industrial pollution, diffuse pollution and faecal contamination. Thus, the present study illustrates that various multivariate statistical techniques can be effectively employed in sustainable management of water resources. Highlights ? The effectiveness of existing river water quality monitoring network is assessed ? Significance of seasonal redesign of the monitoring network is demonstrated ? Rationalization of water quality parameters is performed in a statistical framework     

小型景观湖水质模拟及应用研究

由于小型景观水体的体量和再生水补水措施的施行,其水质随环境和入水水质的变化和富营养化过程较大型水体快。由EFDC模型和WASP模型耦合建立小型浅水景观湖生态动力学模型,用实测水质数据进行参数率定与验证,可实现小型景观水体水质变化趋势的模拟,为水质管理提供决策支持。在此基础上,对民主湖在几种水质保障方案下1年的水质变化进行了模拟预测和对比,结果表明,作为富营养化程度表征的叶绿素a在空间和时间分布上均存在一定的规律性,夏季由于温度升高,叶绿素a浓度较高,说明藻类等浮游植物迅速增殖,是水质恶化水华暴发的高发期,而采取加大湖水水力循环和改善入口水质的控制方案,可使水质得到良好保障。     

基于聚类分析和水质标识指数的水质评价方法

为探讨一种适用于大尺度、多断面和长时间的水质评价方法,用层次聚类分析将2006~2008年海河5个监测断面的165个水质样本分为20组,并用方差分析验证了结果的可靠性。用综合水质标识指数表示水质样本的综合特征,与单因子评价法相比,该方法充分考虑了最差水质因子的影响,避免了单因子评价法以偏概全的缺陷,且分辨率高。将评价结果分解到各断面,结果表明:密云水库、官厅水库和于桥水库的水质处于II~III类水之间,可满足作为饮用水水源水的要求,三岔口断面大部分时间为IV~V类水,张家口八号桥断面全年有6个月以上水质属于劣V类。     

Evaluation of the Swat River,Northern Pakistan,water quality using multivariate statistical techniques and water quality index (WQI) model

Environmental Science and Pollution Research - This study evaluates the characteristics of water along the Swat River, Northern Pakistan. For this purpose, water samples (n = 30) were...     

巢湖水质时空分布模式研究

依据2004~2006年12个采样点11个水质指标数据,应用主成分分析(PCA)、聚类分析(CA)、判别分析(DA)和基于G IS平台的克里格插值法,对巢湖水质时空分布模式进行研究。通过统计分析将巢湖11个水质指标概括为4个主成分:第一主成分COD、BOD5和Chl-a;第二主成分电导率、NH4+-N、TN和TP;第三主成分温度和DO;第四主成分pH和SD。在空间尺度上分为2组,分别对应于东西半湖。除了DO指标,空间上西半湖周边区域的水质指标浓度显著高于东半湖;东西半湖TP和COD空间分布相似;在时间尺度上,可分为1月~3月份、12月份一组和4月~11月份一组;TP、COD、DO和SD指标第一时期浓度高于第二时期,温度、电导率和Chl-a指标则相反;溶解氧和温度两者的时间差异性呈现明显的负相关。并对采样点和采样频次进行了适当优化。     

苏南某市河流水质参数时空变异性研究

以苏南某市区320km2内的河流为研究对象,基于对高锰酸盐指数、NH3-N、TP3个主要水质参数的监测,应用地质统计学的变差函数球状模型和Kriging插值法,对河流有机污染指标、富营养化指标进行了空间插值,用以揭示其时空分布特征及变化趋势,并绘制了时空分布等值线图。结果表明,受不同区域污染物来源的差异、不同河道自身条件的差异和不同水期水生植物、入流水量、河水流动性的差异等因素的影响,研究区河流水质参数呈现出不同的时空变异特征;各水质参数污染均相当严重,尤以富营养化指标氮磷最为显著。     

QUAL2K模型在西苕溪干流梅溪段水质模拟中的应用

QUAL2K是一维稳态水质模型,由美国环保局开发并在QUAL2E模型的基础上发展起来的.采用QUAL2K模型对西苕溪干流梅溪段的水质进行了模拟和预测.针对西苕溪的具体情况,选用COD、氨氮和总磷作为模拟预测指标,用模型率定法并参考相关文献确定了COD降解系数Kc、氨氮降解系数Kn和总磷平衡系数Kp等水质参数,并对模拟结果进行了验证,结果表明预测值和实测值的相关性较好.     

基于不同水厂水质调查的消毒副产物生成趋势及模型预测

以南方某市具有代表性的7个自来水厂为研究对象,对不同季节和不同处理工艺下的原水、出厂水和管网水的9个常规水质参数和2类含碳消毒副产物进行了检测,考察了水质指标随季节的变化规律和处理工艺对不同水质指标的影响,分析了常规水质参数与消毒副产物生成量之间的关系。结果表明：7个自来水厂出厂水均检出三卤甲烷(trihalomethanes,THMs)和卤乙酸(haloacetic acids,HAAs);THMs平均质量浓度为8.70~29.35 μg·L⁻¹,HAAs平均质量浓度为13.22~39.06 μg·L⁻¹;管网水中2类消毒副产物浓度水平较出厂水略有增加;THMs的季节变化规律为冬季>春季≈秋季>夏季,HAAs的季节变化性不强。利用IBM SPSS Statistics 20进行了Spearman秩相关系数分析,并分别以原水和出厂水水质参数来建立出厂水THMs或HAAs生成量的回归方程。结果表明：THMs质量浓度预测效果良好,可用于自来水厂水质的化学安全性预警;但对于HAAs质量浓度预测,无论采用原水还是出厂水水质参数所建立的预测方程,预测结果均不理想。     

基于灰色模型和模糊神经网络的综合水质预测模型研究

水质状态变化趋势预测研究对水资源管理和维护具有重要的现实意义。提出了一种将灰色模型和模糊神经网络相结合的水质预测模型。首先基于改进的灰色模型预测出水体中各理化因子在未来一段时间内的指标变化,然后采用T-S模糊神经网络对各单因子的预测值进行数据融合,构建水质变化综合趋势预测模型,预测出下一时间段的水质整体状态指标。实验表明,这种方式用来预测湖泊水质变化趋势具有可行性;与BP网络模型相比,基于T-S模糊神经网络系统的模型具有预测精度高、模型系统稳定等优越性。     

再生水补水人工湖水域聚类划分及水质指标时空分布特征

以天津临港生态湿地公园人工湖水质和水文的调查数据为依据,运用相关性分析和聚类分析将研究水体划分为3类水域。研究了不同聚类水域水温、DO、pH、氮磷营养元素以及Chla的时空分布特征。结果显示,DO、硝态氮、TN、TP与流速呈显著正相关,水温、SS与流速呈显著负相关。Chla与水深呈显著正相关,TN、硝态氮与水深呈显著负相关。断面宽度与所有理化指标均没有显著相关性。3种水域在监测期间N/P比(TN/TP)显示湖泊正在从氮营养元素限制型(N/P<7)向适宜藻类生长的营养类型过渡。富营养化评价显示,再生水为补水的临港人工湖在监测期间水质已处于中度富营养,主要贡献因子为COD和TP。     

An integrated multivariate statistical approach for the evaluation of spatial variations in groundwater quality near an unlined landfill

Environmental Science and Pollution Research - Groundwater is a major resource for water supply in Canada, and 43 of 68 Saskatchewan municipalities rely on groundwater or combined groundwater and...     

Comparison of artificial neural network and regression models in the prediction of urban stormwater quality.

Urban stormwater quality is influenced by many interrelated processes. However, the site-specific nature of these complex processes makes stormwater quality difficult to predict using physically based process models. This has resulted in the need for more empirical techniques. In this study, artificial neural networks (ANN) were used to model urban stormwater quality. A total of 5 different constituents were analyzed-chemical oxygen demand, lead, suspended solids, total Kjeldahl nitrogen, and total phosphorus. Input variables were selected using stepwise linear regression models, calibrated on logarithmically transformed data. Artificial neural networks models were then developed and compared with the regression models. The results from the analyses indicate that multiple linear regression models were more applicable for predicting urban stormwater quality than ANN models.     

Neuro-fuzzy and neural network systems for air quality control

In order to define efficient air quality plans, Regional Authorities need suitable tools to evaluate both the impact of emission reduction strategies on pollution indexes and the costs of such emission reductions. The air quality control can be formalized as a two-objective nonlinear mathematical problem, integrating source–receptor models and the estimate of emission reduction costs. Both aspects present several complex elements. In particular the source–receptor models cannot be implemented through deterministic modelling systems, that would bring to a computationally unfeasible mathematical problem. In this paper we suggest to identify source–receptor statistical models (neural network and neuro-fuzzy) processing the simulations of a deterministic multi-phase modelling system (GAMES). The methodology has been applied to ozone and PM10 concentrations in Northern Italy. The results show that, despite a large advantage in terms of computational costs, the selected source–receptor models are able to accurately reproduce the simulation of the 3D modelling system.