基于空间插值算法的阳宗海夏季水质参数空间分布规律研究

水质参数是分析湖泊生态环境的重要指标。由于基于离散采样点的传统水质参数统计法不能详细分析湖泊水质参数的空间分布规律,采用了克立格(Kriging)和反距离加权(Inverse Distance Weighted,IDW)空间插值算法对阳宗海夏季水质参数指标(pH值、总叶绿素、浊度、溶解氧、电导率和藻蓝蛋白)进行空间分布规律研究。试验结果表明:阳宗海夏季表层水体的循环周期较长、空间异质性较高,尤其是总叶绿素含量、浊度和藻蓝蛋白含量;阳宗海南岸受到人为生活扰动和污染较为严重,藻蓝蛋白和溶解氧的浓度均较高;阳宗海北岸受到人为工业污染更为明显,表现为浊度在该区域达到峰值;阳宗海夏季表层水体的电导率和pH值更多地受到了治理砷污染而大量喷施的絮凝剂影响。研究成果为阳宗海水环境评估和综合整治提供了理论依据。     

Feasibility Assessment of Data-Driven Models in Predicting Pollution Trends of Omerli Lake,Turkey

Data-driven models are commonly used in a wide range of disciplines, including environmental engineering. To analyze Omerli Lake’s historic water pollution status, this study monitors data for dissolved oxygen, 5-day biochemical oxygen demand, ammonium nitrogen, nitrite nitrogen, nitrate nitrogen, and ortho phosphate. The quality of the lake water is assessed based on measurements of dissolved oxygen. The collected data are analyzed using regression analysis and artificial neural network models. The main goal of this paper is to reveal the best applicable data-driven model in order to gain forward-looking information regarding the dissolved oxygen level of the lake using other pollution parameters. In order to ascertain eutrophic status, total phosphorus loads for each year are represented on a Vollenweider diagram. Results designate an increasing risk of eutrophication for Omerli Lake in recent years. Results of the data-driven models show that the artificial neural networks model constitutes the best relationship between the dissolved oxygen and other parameters.     

南方某水库底层溶解氧分布特征及低氧成因分析

以南方某水库为例,通过2013年4月至2014年3月的监测数据研究底层溶解氧(bottom dissolved oxygen,BDO)和底层水温(bottom temperature,BT)、p H值、电导率、浊度(TU)、TP、TN、COD、总有机碳(TOC)、Chl-a间的关系,以阐明该水库BDO的时空分布特征及低氧成因。结果表明,BDO春、夏季浓度低,秋、冬季(枯水期)浓度较高,进水口附近的DO浓度普遍高于库中、库心;低氧现象主要发生在夏季(5—8月);水越深、水温越低,BDO下降趋势显著(P0.05);浊度、TOC与BDO为正相关关系。BDO与叶绿素a呈现显著正相关,在底层浮游植物中,蓝藻门占绝对优势,BDO浓度与蓝藻的垂直迁移有关。表明水库BDO在时间上的变化趋势受温度的影响,而空间上的变化受到水动力的影响,低氧现象的发生受到水体垂直密度分层的影响。     

Eutrophication modelling of Amirkabir Reservoir (Iran) using an artificial neural network approach

The complicated non‐linear relationships between water quality and environmental parameters involved in predicting algal blooms necessitate a new approach, using data‐driven modelling. Accordingly, a multilayer perceptron (MLP) and time delay neural network (TDNN) were used to predict the eutrophication status of two monitoring stations in the Amirkabir Reservoir in Iran. Six scenarios for each monitoring station were performed to select a significant, independent input using 12 years of monthly data. The final inputs were temperature, turbidity, phosphate (PO₄), nitrate (NO₃), nitrite (NO₂), ammonium (NH₃), dissolved oxygen (DO) and electrical conductivity (EC). Applying an MLP neural network to the upstream monitoring station with 21–38 neurons in the first and second hidden layers, the minimum mean squared errors (MSE ) in training, validating and testing were 0.083, 0.81 and 1.95 cells/100 ml, respectively. Further, when the TDNN network was used with the same neuron numbers in the hidden layer for the similar monitoring station, the minimum MSE values for model training, validating and testing were 0.06, 0.72 and 1.76 cells/100 ml, respectively. For the Beylaghan monitoring station, using the MLP neural network with 29–23 neurons in the first and second hidden layer, the minimum MSE values gained in training, validating and testing were 0.181, 0.58 and 0.95 cells/100 ml, respectively. Using the TDNN network with the same neurons in the hidden layers of the MLP neural network for the station, the minimum MSE values for training, validating and testing were 0.152, 0.43 and 0.84 cells/100mL, respectively. Thus, TDNN exhibited a high accuracy and workability, compared to the MLP. Sensitivity analysis of the Amirkabir Reservoir dataset indicated increasing the value of nitrate is the first factor, followed by turbidity and NH₃, having the greatest impacts on eutrophication prediction.     

浅水湖泊水生态数学模型研究及应用

该文针对珠三角感潮浅水湖泊水环境模拟问题,在二维水流-污染物输移耦合数学模型基础上,结合WASP(water quality analysis simulation program)水生态数学模型原理,考虑溶解氧、氨氮、硝酸盐氮、有机氮、无机磷、有机磷、碳生化需氧量和叶绿素a等8个水质变量及其相互作用的溶解氧平衡子系统、氮循环子系统、磷循环子系统和浮游植物动力学子系统,建立了浅水湖泊水生态数学模型。通过室内实验系统模拟了珠三角某典型感潮人工湖泊外江来水营养条件对蓝藻生长的影响,确定了蓝藻生长速率等关键模型参数,并模拟了湖泊不同换水方案下叶绿素a的时空分布规律,为控制湖泊水体富营养化、预防蓝藻水华爆发提供科学依据。     

Physicochemical factors affecting larval fish densities in mississippi river floodplain ponds,Louisiana (U.S.A.)

We collected water-quality data from 15 artificial floodplain ponds along the Mississippi River during May 1988 and quantified shoreline length, shoreline sinuousity, volume, and depth variation. Ponds regarded as high-quality nurseries (based upon larval fish densities) contained higher dissolved oxygen concentrations and lower total organic carbon concentrations than ponds of lower nursery quality (p < 0.02). High-quality nurseries also maintained higher conductivity and turbidity than low-quality nurseries (p < 0.05). Results indicated that dissolved oxygen and pH probably fluctuated less in high quality nurseries and therefore provided better conditions for survival and growth of larval fishes. Total organic carbon and conductivity were directly related to pond morphometry (p < 0.004) and affected dissolved oxygen and pH in ponds. Large, high-volume ponds with sinuous shorelines and variable depths tended to contain both high conductivities and low total organic carbon concentrations. Pond morphometry may have affected water quality and subsequently determined the nursery value of artificial floodplain ponds.     

A spatio‐temporal water quality assessment of the Beas and Sutlej Rivers at the Harike Wetland: A Ramsar site in Punjab,India

Lakes play a vital role in regulating water storage, flow of river water, and ultimately maintaining a balanced ecosystem. Spatial and temporal variations in physicochemical parameters of water in Harike Wetland, a Ramsar site in the northwestern state of Punjab, India, were studied. This study was conducted on a monthly basis from January to December 2015. The water quality was studied at ten locations from sites 1 to 10 upstream, central and downstream from Harike Lake for ten physicochemical parameters, including temperature, pH, electrical conductivity, turbidity, dissolved oxygen concentration biological oxygen demand, nitrate and phosphate concentrations and salinity. The findings of this study revealed that, except for temperature and pH, all parameters exhibited relatively higher values for the Sutlej River, compared with the Beas River, with sampling sites 5 to site 7 exhibiting intermediate results. The mean seasonal temperature variations ranged from 16.9 to 26.6 °C, the pH from 7.7 to 8.2, electrical conductivity from 223 to 303 μS cm^?1 and TDS concentration from 148.7 to 180.4 ppm. Correlation analysis was conducted to assess the relations between the variables. The electrical conductivity exhibited a high positive correlation with salinity and biological oxygen demand, whereas it correlated negatively with the dissolved oxygen concentration. Box and whisker plots were also plotted for the study results to better examine the data distribution.     

苏州市景观水体表观污染类型识别及特征指标筛选

为解决表观污染成因的问题,以苏州市区河网为主要研究对象,采集796个数据样本,对景观水体的表观污染类型进行划分界定,并通过对数据样本的统计分析,探究水体表观污染类型和水质指标之间的关系。结果表明:氧化还原电位、溶解氧、叶绿素a、高锰酸盐指数和浊度对不同污染类型的水体具有很好的区分度,可作为主要特征指标;景观水体可分为有机主导型、无机主导型、营养主导型和混合型4种表观污染类型;不同表观污染水体判别的优先次序为有机主导型、无机主导型、营养主导型、混合型。     

LM—BP神经网络在于桥水库水质预测中的应用

利用BP神经网络的改进算法（L-M）,通过对大量样本进行多次的训练学习,建立于桥水库水质预痢模型,用该模型对于桥水库高锰酸盐指数、五日生化需氧量、氨氮、溶解氧等污染指标进行了预测,预测结果表明,LM—BP神经网络模型用在于桥水库水质预测时是可行的,可以得到较为理想的的精度和可靠度。     

基于粒子群最优化下BP神经网络的短期电价预测(英文)

根据电力市场的相关历史数据准确地预测出未来的市场出清电价,对于市场中的各个参与者都具有十分重要的意义.在建立了一种粒子群优化(PSO)下的BP神经网络电价短期预测模型的基础上,采用PSO进化算法,反复抽取训练子集样本,通过对应的验证样本预测误差寻找近似最有代表性的训练子集,解决了模型的训练样本参数难以设置的问题.实验验证了该预测模型的有效性,结果表明处理好预测模型样本参数的选择问题,能够提高模型的稳定性及预测精度.     

Long-term evolution and trends of the hydrological and hydrochemical parameters in Bulgarian Black Sea waters during the period 1992-2000.

The dynamics of chemical parameters in the Bulgarian Black Sea during the 1990s reflects the complex relations in the ecosystem itself and the influence of the Danube water discharge, which is a major climatic and anthropogenic factor for the Western Black Sea. Analyses of hydrological (temperatures, salinity) and hydrochemical (dissolved oxygen, oxygen saturation, nitrite nitrogen, nitrate nitrogen) data collected during the period 1992-2000 in the 30-miles zone offshore along the Bulgarian Black Sea coast were carried out in the framework of the "DANUBS" project. In the period 1995-2000 gradually the winters were becoming warmer, the springs colder and the summers were short and hot. The long-term averages show spatially a minimum of salinity in front of the Cape Galata at 10 miles offshore, whereas in front of the Cape Emine the salinity increases gradually from the coast towards the 30-miles zone offshore. In the late 1990s very low summer values or even complete absence of inorganic nitrogen in the Bulgarian Black Sea were registered. Seasonally the oxygen varied in broad terms, however on average the surface waters were saturated or slightly oversaturated with oxygen. There was a regular decrease in oxygen concentrations with depth.     

Runoff and loads of nutrients and heavy metals from an urbanized area.

To investigate the run-off characteristics of dissolved and particulate substances from a heavily urbanized area (basin area: 95 ha, percentage of impervious surfaces: 60%), sensors for measuring water level, water temperature, DO, pH, electric conductivity (EC), turbidity and ammonium ion were placed in the channel connecting storm sewers and natural river, together with water sampling for analyzing SS, nutrients and metals. While both turbidity and EC showed apparent "first flush", the peaks of EC were always earlier than those of turbidity. In a similar manner, dissolved nutrients and metals exhibited earlier "first flush" compared with particulate nutrients and acid-extractable metals. Significantly positive correlations between EC and dissolved substances as well as those between turbidity and particulate (acid-extractable minus dissolved) substances were usually observed, and two distinct different regressions were found between the two datasets separated before and after the concentration peaks. Using these relationships, the total loads during the respective rainfall events were calculated on the basis of EC and turbidity changes. The total loads of nitrogen, zinc, etc. were nearly proportional to the lengths of non-rainfall periods before the events, indicating that these loads derived from the atmospheric deposition.     

水轮机尾水管压力脉动的神经网络模型

为掌握万家寨水电站水轮机尾水管的水压力脉动特性,对水轮机尾水管水压力脉动进行了现场测试,获得了多个工况下的测试数据.在测试数据的基础上,应用人工神经网络中的三层前向网络模型,采用反向传播算法,建立了水轮机尾水管压力脉动特性的人工神经网络模型.与实际的测试数据进行对比分析表明,所建立的人工神经网络模型,能够准确反映水轮机的尾水管压力脉动特性,可以用于指导水电站中机组的稳定运行.     

KNT-artificial neural network model for flux prediction of ultrafiltration membrane producing drinking water.

This paper describes the prediction of flux behavior in an ultrafiltration (UF) membrane system using a Kalman neuro training (KNT) network model. The experimental data was obtained from operating a pilot plant of hollow fiber UF membrane with groundwater for 7 months. The network was trained using operating conditions such as inlet pressure, filtration duration, and feed water quality parameters including turbidity, temperature and UV254. Pre-processing of raw data allowed the normalized input data to be used in sigmoid activation functions. A neural network architecture was structured by modifying the number of hidden layers, neurons and learning iterations. The structure of KNT-neural network with 3 layers and 5 neurons allowed a good prediction of permeate flux by 0.997 of correlation coefficient during the learning phase. Also the validity of the designed model was evaluated with other experimental data not used during the training phase and nonlinear flux behavior was accurately estimated with 0.999 of correlation coefficient and a lower error of prediction in the testing phase. This good flux prediction can provide preliminary criteria in membrane design and set up the proper cleaning cycle in membrane operation. The KNT-artificial neural network is also expected to predict the variation of transmembrane pressure during filtration cycles and can be applied to automation and control of full scale treatment plants.     

Runoff load estimation of particulate and dissolved nitrogen in Lake Inba watershed using continuous monitoring data on turbidity and electric conductivity

Easy-to-measure surrogate parameters for water quality indicators are needed for real time monitoring as well as for generating data for model calibration and validation. In this study, a novel linear regression model for estimating total nitrogen (TN) based on two surrogate parameters is proposed based on evaluation of pollutant loads flowing into a eutrophic lake. Based on their runoff characteristics during wet weather, electric conductivity (EC) and turbidity were selected as surrogates for particulate nitrogen (PN) and dissolved nitrogen (DN), respectively. Strong linear relationships were established between PN and turbidity and DN and EC, and both models subsequently combined for estimation of TN. This model was evaluated by comparison of estimated and observed TN runoff loads during rainfall events. This analysis showed that turbidity and EC are viable surrogates for PN and DN, respectively, and that the linear regression model for TN concentration was successful in estimating TN runoff loads during rainfall events and also under dry weather conditions.     

不同基质湿地对污水净化效果的研究

基质是人工湿地系统中的重要组成部分,为了研究不同类型的基质材料对污水的净化能力,设计建设了5种常见基质材料的人工湿地进行污水处理效果分析。通过试验,得出了5种湿地基质对污水浊度、溶解氧、氨氮、硝酸盐氮、化学需氧量以及总磷的去除效果。根据试验分析结果,筛选出净化能力较强的基质作为人工湿地基质,可以提高人工湿地对污水的净化能力、延长材料的使用时间,减少湿地建设的投资成本。     

Hybrid ANN–GA model for predicting turbidity and chlorophyll-a concentrations

The transport and fate of admixtures at coastal zones are driven, or at least modulated, by currents. In particular, in tide-dominated areas due to higher near-bottom shear stress at strong currents, sediment concentration and turbidity are expected to be at maximum during spring tide, while algal growth rate likely is peaking up at slack currents during neap tide. Varying weather and atmospheric conditions might modulate the said dependencies, but the water quality pattern still is expected to follow the dominant tidal cycle. As tidal cycling could be predicted well ahead, there is a possibility to use water quality and hydrodynamic high-resolution data to learn past dependencies, and then use tidal hydrodynamic model for nowcasting and forecasting of selected water quality parameters.This paper develops data driven models for nowcasting and forecasting turbidity and chlorophyll-a using Artificial Neural Network (ANN) combined with Genetic Algorithm (GA). The use of GA aims to automate and enhance ANN designing process. The training of the ANN model is done by constructing input–output mapping, where hydrodynamic parameters act as an input for the network, while turbidity and chlorophyll-a are the corresponding outputs (desired target). Afterward, the prediction is carried out only by employing computed water surface elevation as an input for the trained ANN model. The proposed data driven model has successfully revealed complex relationships and utilized its experiential knowledge acquired from the training process for facilitating the subsequent use of the data driven model to yield an accurate prediction.     

基于SOFM和BP短期负荷预测方法

基于以自组织特征映射神经网络(Self-organizing feature map,SOFM)先聚类、神经网络再预测的模型以往多用在对疾病、天气方面的预测,由此提出了一种以SOFM与误差反向传播算法的神经网络(Back Propagation,BP)相组合应用为基本原理的短期电力负荷预测的组合模型。该模型主要基于SOFM网络的主要特性聚类,预先将训练样本集采取聚类分析,对其分为具有相似数据的若干子类,再根据每一子类构造一个BP网络模型。利用内蒙古自治区某市的实际日平均负荷数据进行仿真,证明了本文方法的有效性。     

Application of artificial neural networks for classifying lake eutrophication status

Several artificial neural network architectures were ‘trained’ on data from the Eastern Lake Survey – Phase I of the Environmental Protection Agency's National Surface Water Survey in order to investigate which physical–chemical parameters are possibly of greatest importance in determining the eutrophication status of lakes. From the 110 available lake parameters in the Survey, 60 were chosen as input to the neural networks. The traditional eutrophication classification scheme of Vollenweider was used for comparative purposes. The various artificial neural network simulations showed that, in addition to total phosphorus and inorganic nitrogen, turbidity, specific conductance, lake elevation and hydrogen ion concentration were identified as the most significant parameters affecting the classification of lakes in regard to their eutrophication status. These results suggest a conceivable association between these parameters and lake eutrophication, thereby indicating a need for further study on these relationships. A model simulation utilizing an unsupervised neural network did not provide much insight into the lake eutrophication status, but did show that the available physical–chemical lake data could be categorized according to physical region, thereby providing an indication that the lake data used in this study were region‐dependent.     

基于遥感的内陆水体水质监测研究进展

从遥感数据、反演方法和水质参数三方面综述了水质遥感监测的研究进展,介绍了国内外常用遥感数据,对比了分析法、经验法、半经验法、机器学习和综合法五种反演方法的优缺点,总结了叶绿素a、悬浮物、有色可溶性有机物等光敏参数和化学需氧量、生化需氧量、总磷和总氮等非光敏参数的研究进展。目前内陆水体水质遥感监测在卫星传感器的针对性、反演算法的时空局限性、水质参数光谱特征的复杂性、大气校正的精确性和特殊类型水体的水质监测等方面还存在问题;指出未来水质遥感监测应围绕新型遥感数据、通用反演模型、不同光谱特征、精确大气校正和特殊水体分类等方面开展。