Inversion of dispersion coeffcient in water quality model using optimal perturbation algorithm

As a primary parameter in the water quality model for shallow bays, the dispersion coefficient is traditionally determined with a trial-and-error method, which is time-consuming and requires much experience. In this paper, based on the measured data of chemical oxygen demand （COD）, the dispersion coefficient is calculated using an inversion method. In the process, the regularization method is applied to treat the ill-posedness, and an operator identity perturbation method is used to obtain the solu- tion. Using the model with an inverted dispersion coefficient, the distributions of COD, inorganic nitrogen （IN）, and inorganic phosphorus （IP） in Bohai Bay are predicted and compared with the measured data. The results indicate that the method is feasible and the inverted dispersion coefficient can be used to predict other pollutant distribution. This method may also be further extended to the inversion of other parameters in the water quality model.

Underground water quality model inversion of genetic algorithm

The underground water quality model with non-linear inversion problem is ill-posed, and boils down to solving the minimum of nonlinear function. Genetic algorithms are adopted in a number of individuals of groups by iterative search to find the optimal solution of the problem, the encoding strings as its operational objective, and achieving the iterative calculations by the genetic operators. It is an effective method of inverse problems of groundwater, with incomparable advantages and practical significances.

Development and application of a two-dimensional water quality model for the Daqinghe River Mouth of the Dianchi Lake

Water quality models are important tools to support the optimization of aquatic ecosystem rehabilitation programs and assess their efficiency. Basing on the flow conditions of the Daqinghe River Mouth of the Dianchi Lake, China, a two-dimensional water quality model was developed in the research. The hydrodynamics module was numerically solved by the alternating direction iteration （ADI） method. The parameters of the water quality module were obtained through the in situ experiments and the laboratory analyses that were conducted from 2006 to 2007. The model was calibrated and verified by the observation data in 2007. Among the four modelled key variables, i.e., water level, COD （in CODcr）, NH4＋-N and PO43-P the minimum value of the coefficient of determination （COD） was 0.69, indicating the model performed reasonably well. The developed model was then applied to simulate the water quality changes at a downstream cross-section assuming that the designed restoration programs were implemented. According to the simulated results, the restoration programs could cut down the loads of COD and PO43-P about 15%. Such a load reduction, unfortunately, would have very little effect on the NH4^＋-N removal. Moreover, the water quality at the outlet cross-section would be still in class V （3838-02）, indicating more measures should be taken to further reduce the loads. The study demonstrated the capability of water quality models to support aquatic ecosystem restorations.

PARAMETER ESTIMATION OF MULTI-CONSTITUENT WATER QUALITY MODEL FOR THE LIANGXI RIVER BY MARQUARDT METHOD

A multi-constituent water quality model is presented,Which relates carbonaceous biochemical oxygen demand (CBOD),amonia (NH3-N), nitrite(NO2-N), nitrate(NO3-N) and dissolvedoxygen(DO). The parameters are solved by Marquardt Method (i. e.,Dampled Least Square Method) while initial values inoptimization are produced by Monte-Carlo Method. The Potential ofthe method as a parameter estimation aid is demonstrated for theapplication to the Liangyi Rver, JiangSu Province of China and by aspecial comparison with Gauss Method.

Identification of Water Quality Model Parameter Based on Finite Difference and Monte Carlo

Identification results of water quality model parameter directly affect the accuracy of water quality numerical simulation. To overcome the difficulty of parameter identification caused by the measurement’s uncertainty, a new method which is the coupling of Finite Difference Method and Markov Chain Monte Carlo is developed to identify the parameters of water quality model in this paper. Taking a certain long distance open channel as an example, the effects to the results of parameters identification with different noise are discussed under steady and un-steady non-uniform flow scenarios. And also this proposed method is compared with finite difference method and Nelder Mead Simplex. The results show that it can give better results by the new method. It has good noise resistance and provides a new way to identify water quality model parameters.

Space-Temporal Variation in Underground Water Some Quality Parameters in Klodzko Water Intake Area Using Statistical and Geostatistical Methods （SW Part of Poland）

This paper presents selected results of research connected with the development of a （3D） geostatistical hydrogeochemical model of the Klodzko Drainage Basin, dedicated to the spatial and time variation in the selected quality parameters of underground water in the Klodzko water intake area （SW part of Poland） [1-6]. The research covers the period 1977-2012. Spatial analyses of the variation in different quality parameters, between others, Fe [gFe/m3], Mn [gMn/m3], ammonium ion [gNH4＋/m3] contents and oxidation capacity [gO2/m3], were carried out on the basis of the chemical determinations of the quality parameters of underground water samples taken from the wells in the water intake area [2-4]. Spatial and time variation in the parameters was analyzed on the basis of archival data （period 1977-1999） for 22 （pump and siphon） wells, later data obtained （November 2011） from tests of water taken from 14 existing wells and the latest data （January 2012） acquired from 3 new piezometers, which were made in other locations in the relevant area. Thematic databases, containing original data on coordinates X, Y （latitude, longitude） and Z （terrain elevation and time-years） and on regionalized variables, i.e. the underground water quality parameters in the Klodzko water intake area determined for different analytical configurations （22 wells, 14 wells, 14 wells ＋ 3 piezometers）, were created [2]. Both archival data （acquired in the years 1977-1999） and the latest data （collected in 2011-2012） were analyzed. These data were subjected to spatial analyses [2-6] using statistical and geostatistical methods [7-12]. The evaluation of basic statistics of the investigated quality parameters, including their histograms of distributions, scatter diagrams between these parameters and also correlation coefficients r, were presented in this article. The directional semivariogram function and the ordinary （block） kriging procedure were used to build the 3D geostatistical model. The geostatistical parameters of the theoretical models of directional semivariograms of the studied water quality parameters, calculated along the time interval and the well depth （taking into account the terrain elevation）, were used in the ordinary （block） kriging estimation. The obtained results of estimation, allowed to determine the levels of increased values Z＊ of studied underground water quality parameters [2, 4-6]. Generally, the behaviour of the underground water quality parameters has been found to vary in space and time. Thanks to the spatial analyses of the variation in the quality parameters in the Klodzko underground water intake area some regularities （trends） in the variation in water quality have been identified.

Parameter selection and model research on remote sensing evaluation for nearshore water quality

Using remote sensing technology for water quality evaluation is an inevitable trend in marine environmental monitoring. However, fewer categories of water quality parameters can be monitored by remote sensing technology than the 35 specified in GB3097-1997 Marine Water Quality Standard. Therefore, we considered which parameters must be selected by remote sensing and how to model for water quality evaluation using the finite parameters. In this paper, focused on Leizhou Peninsula nearshore waters, we found N, P, COD, PH and DO to be the dominant parameters of water quality by analyzing measured data. Then, mathematical statistics was used to determine that the relationship among the five parameters was COD〉DO〉P〉N〉pH. Finally, five-parameter, fourparameter and three-parameter water quality evaluation models were established and compared. The results showed that COD, DO, P and N were the necessary parameters for remote sensing evaluation of the Leizhou Peninsula nearshore water quality, and the optimal comprehensive water quality evaluation model was the four- parameter model. This work may serve as a reference for monitoring the quality of other marine waters by remote sensing.

The sensitivity of the array resistivity log to mud filtrate invasion and its primary five-parameter inversion for improved oil water recognition

In order to improve reservoir fluid recognition, the sensitivity of array resistivity response to the difference of the invasion properties in both oil-bearing layers and water layers is analyzed. Then the primary inversion is carried out based on the array resistivity log. The mud invasion process is numerically simulated based on the oil-water flow equation and water convection diffusion equation. The results show that the radial resistivity of a fresh mud-invaded oil-bearing layer presents complex distribution characteristics, such as nonlinear increase, increasing to decreasing and low resistivity annulus, and the resistive invasion profile of a water layer is monotonic. Under specific conditions, array resistivity log can reflect these changes and the array induction log is more sensitive. Nevertheless, due to the effect of factors like large invasion depth, reservoir physical and oil-bearing properties, the measured apparent resistivity may differ greatly from the actual mud filtrate invasion profile in an oil-bearing layer. We proposed a five-parameter formation model to simulate the complex resistivity distribution of fresh mud-invaded formation. Then, based on the principle of non-linear least squares, the measured array resistivity log is used for inversion with the Marquardt method. It is demonstrated that the inverted resistivity is typically non-monotonic in oil-bearing layers and is monotonic in water layers. Processing of some field data shows that this is helpful in achieving efficient reservoir fluid recognition.

Conceptual Modelling and Data Based Techniques to Understand Urban Water Use and Wastewater Production

A systems approach is used to describe the generation and variation of wastewater in an urban area. This is a multivariable system and its combined response at the outlet of this system, which is usually the entrance ofa wastewater treatment plant, depends on a number of environmental （precipitation and temperature） as well as social （size of the urban area, population changes, water consumption per capita） variables. There is a large number of available models and tools for describing the urban water system, however, the interactions between the individual components are rarely considered within the same modelling framework. In this paper a parsimonious methodology is proposed in order to understand and estimate the wastewater generation and its characteristics in an urban area using any information provided by the available data. The model incorporates both the flows of stormwater discharge and wastewater production that arrive to the wastewater treatment plant. A state dependent variable is introduced to simulate the consumptive uses in the urban area. Data availability and system＇s complexity affect the ability to achieve enhanced model performance, however, in the presented case study, preliminary results from the application of the presented model in the Greater Athens Area illustrate the potential of the conceptual modelling approach.

Application of homotopy parameter inversion method in Miyun Reservoir

The large-scale convergence of homotopy parametric inversion method on the water quality model parameters calculated was used,with application in parametric inversion calculation of total phosphorus of Beijing Miyun Reservoir.Through calculated and compared the error of sedimentation rate by homotopy parametric inversion method and genetic inversion calculation method,the results indicate that homotopy parametric inversion method has good stability,calculating speed,and even if the initial selection away from the objective function,the solution still has a good convergence.

基于Sentinel-2多光谱遥感影像的小浪底水质反演

多光谱遥感技术可根据遥感波段信息反演水质参数,降低监测成本,提高监测速度和质量,为大范围水环境监测提供了一种新的方法。通过分析小浪底水库的Sentinel-2多光谱影像以及采样点实测水质数据,建立了最佳光谱波段的水质参数反演模型,对小浪底水库的化学需氧量(COD)、总磷(TP)、总氮(TN)和氨氮(NH_3-N)进行了遥感反演,验证了反演模型的精确度和稳定性,并反演了各水质参数的空间分布规律。结果表明:在4种水质参数反演模型中,COD模型精确度和稳定性最高,其次是TP、TN,最低的是NH_3-N,水库出水口和部分边缘COD质量浓度较高,水库中心TN、TP和NH_3-N质量浓度高于边缘处。

基于改进麻雀搜索算法的水质模型多参数反演

水质模型参数取值对模型的模拟精度影响很大,为提高BOD-DO水质模型参数反演精度,首先在DobbinsCamp BOD-DO水质模型的基础上,以BOD和DO浓度计算值与实测值之差的加权平方和最小为目标函数,构建了Dobbins-Camp BOD-DO水质多参数反演模型;然后针对麻雀搜索算法(Sparrow Search Algorithm,SSA)求解精度低、稳定性不足和易陷入局部最优等问题,引入Sine混沌映射和对立学习、转移概率以及差分变异3个策略,分别从提高初始种群多样性、扩大搜索空间以及增强种群跳出局部最优的能力三方面对SSA算法进行改进,提出了一种多策略改进的麻雀搜索算法(Multi-strategy Improved Sparrow Search Algorithm,MISSA),并将其应用于Dobbins-Camp BOD-DO水质多参数反演模型的求解;最后通过数值实验将得到的反演结果与SSA算法、模拟退火算法、粒子群算法、遗传算法四种优化算法进行对比,并探讨了参数初值选取和观测噪声水平对反演结果的影响。结果表明:MISSA算法的计算性能明显优于对照组中的4种算法,且能显著降低初值选取对BOD-DO水质模型参数反演结果的影响,当观测数据的噪声水平不超过5%时,MISSA算法可有效提高反演结果的稳定性。该结果验证了MISSA算法在反演Dobbins-Camp BOD-DO水质模型参数的有效性,为水质模型参数求解提供有益参考。

基于机载高光谱遥感的长三角一体化示范区水质监测

本文基于机载航空高光谱影像数据对长三角生态绿色一体化发展示范区内“一河三湖”重点水域水质参数进行了反演研究。试验获取了示范区内全谱段高光谱影像数据,通过对机载遥感影像进行预处理,获得水体离水反射信息;在飞行时刻同步采集了水体样本与水体光谱,通过竞争性自适应重加权特征选择方法,选择最优波段参与反演模型的构建,并完成水质专题制图。模型验证集的决定系数R^(2)均达0.6以上,同时与水质监测站数据误差小于10%,实现了长三角一体化示范区的水质精确监测,证实了机载高光谱遥感对于水质参数的反演应用价值,为示范区发展赋能高质量。

基于Landsat 8 OLI的东圳水库水质参数反演研究

遥感技术是监测内陆水体水质的有效手段,东圳水库是莆田市水源地,为了对水质进行实时监测,了解其空间分布情况,本文基于Landsat 8 OLI遥感影像,结合124个采样点实测获得的Chl-a、浊度、COD浓度分别构建统计回归模型、BP神经网络模型、XGBoost模型,并采用R^(2)、MAE、RMSE进行精度检验。结果表明BP神经网络模型效果优于统计回归模型,R^(2)均大于0.9,但存在过拟合现象;XGBoost模型可以有效防止过拟合,表现出较强的拟合能力和较高的预测精度。

基于DE-MCMC方法的滑坡堆积体土壤水力参数反演研究

极端降雨条件下,入渗可急剧增大滑坡体中软弱结构面上的土壤含水量和孔隙水压力,是触发滑坡的重要因素。因此,土壤水动力模拟是滑坡预警系统的核心,其模拟结果的准确与否取决于土壤渗透特性的参数化。该文以汶川地震扰动区的银洞子滑坡堆积体为例,以土壤含水量野外观测数据为基础,基于Richards方程构建入渗模型,利用自适应差分演化算法的马尔科夫链蒙特卡罗方法(Differential Evolution Markov chain Monte Carlo,简称DE-MCMC)反演van Genuchten土壤水力参数。通过比较土壤含水量的模拟值与实测值的拟合程度,评估DEMCMC算法在土壤水动力模拟中的适用性。结果表明,基于DE-MCMC算法反演的后验参数极大降低了先验参数分布的不确定性,并显著提升土壤含水量的模拟精度。将DE-MCMC方法应用于滑坡水文,可有效率定土壤渗透特性参数,为降雨型滑坡预警提供更为可靠的土壤水动力模拟结果。

水产养殖智能投饵技术研究进展

在水产养殖中,投喂成本占总养殖成本的40%~80%,是影响水产养殖效益的主要因素。因此,如何做到科学投喂是降低养殖成本、增加养殖经济效益的关键。智能投饵就是利用各类传感器获取鱼群摄食过程中的全局信息,结合相关算法与模型将信息反馈给控制系统动态调整投饵策略的投喂方式,是做到科学投喂、提高配合饲料利用率的有效手段。近年来,水产养殖智能投饵研究取得了一定成果,但由于实际养殖存在诸多不确定因素,实现智能投饵仍面临挑战。文章介绍了自动投饵机与自动投饵系统的研究现状,主要综述了基于机器视觉、声学、水质参数和生物模型等技术的智能投饵方法,指出了各方法的优缺点并进行总结。今后,应加强对水产养殖动物摄食过程中的图像、声音、环境因子、生物模型等信息的综合分析与运用,以提高智能投饵系统对摄食强度评估的准确性以及与养殖环境的匹配度。

Sentinel-2卫星影像福州市闽江水质时空变化反演分析

以闽江福州段为研究对象,利用哨兵二号(Sentinel-2)遥感影像构建了多元线性回归(MLR)、机器模型和深度学习模型3类水质参数反演模型,对比分析了不同模型的反演精度。结果显示:基于梯度提升决策树(GBDT)的水质参数反演模型精度最高,对测试集数据进行反演,高锰酸盐指数(COD_(Mn))、总氮(TN)、总磷(TP)的决定系数分别达到0.846,0.882和0.819。利用GBDT模型反演了2021—2023年闽江福州段水体的COD_(Mn),TN,TP浓度,分析了COD_(Mn),TN,TP的时空变化特征。

基于无人机多光谱影像的城市河道水质反演

利用无人机多光谱反射率影像和同步实测水质数据,建立基于机器学习的水质参数反演模型,并将该模型应用于张家港河。结果表明,基于XGBoost和随机森林的特征变量重要性分析方法选择氨氮反演的最佳波段组合,确定用随机森林进行氨氮反演精度较高,其测试集决定系数为0.91,平均绝对百分比误差为23.57%;反演结果能从空间上精细地反映张家港河光明村段支流水质的特点,并直观展示水质超标重点区域。

基于实测光谱的白洋淀叶绿素a浓度反演

内陆水体叶绿素a浓度决定其浮游生物含量,是衡量水体富营养化程度的主要指标,更是影响水体表观反射率光谱的重要因素之一。三波段生物光学模型能有效避免二类水体中悬浮物与化学物质等因素的影响,准确估算叶绿素a浓度。以雄安新区白洋淀为研究区域,利用实测水体光谱及水体叶绿素a浓度,分析水体光谱特征,利用穷举法确定三波段因子位置,建立三波段反演模型及精度验证,最后对结果进行分析评价。结果表明,在λ_(1)=673 nm,λ_(2)=729 nm,λ_(3)=782 nm处,三波段模型反演因子[R_(rs)^(-1)(673)-R_(rs)^(-1)(729)]R_(rs)(782)与叶绿素a浓度的相关系数最高为0.82。该模型的决定系数、均方根误差、平均绝对误差、平均相对误差分别为0.67、3.61μg/L、2.86μg/L、26.06%,表明对于悬浮物以及化学物质较高的水体,三波段模型相较于波段比值与单波段模型具有很好的反演精度。该研究为白洋淀叶绿素a浓度反演提供了一定的理论依据和技术支持,有助于白洋淀水质持续监测,也为后续使用三波段模型反演二类水体叶绿素a浓度提供了算法支持。

太赫兹时域光谱法测定水中重金属离子

采用太赫兹时域光谱法采集溶液时域数据,经过去噪和时域分析,汞、镉、铜离子溶液分别在频率为1.7 THz、0.7 THz、1.2 THz位置上建立线性模型,得到拟合效果最佳的决定系数,分别为0.969、0.896、0.932。该方法检出限为0.001 mg/L~0.002 mg/L,空白加标样6次测定结果的RSD为2.0%,加标回收率为94.0%~102%。将该方法用于测定某污水处理厂两个进口的实际水样,结果汞离子未检出。