模糊综合评价在地下水质评价中的应用

在对水质进行评价时有多种方法可供选择,本文在针对将地下水水质进行污染级别划分,选用了模糊综合评价法对东昌府区的地下水质进行了评价。经计算该评价结果符合该区地下水的实际状况,具有一定的合理性。     

模糊综合评价在地下水质评价中的应用

在对水质进行评价时有多种方法可供选择,该文针对地下水水质污染级别划分．选用了模糊综合评价法对东昌府区的地下水质进行了评价。经计算该评价结果符合该区地下水的实际状况,具有一定的合理性。     

改进BP神经网络在地下水环境质量评价中的应用

以LM算法和步长自适应法对BP神经网络进行改进,并将输入数据采用压缩系数法进行处理, 用改进后的BP神经网络对黄河流域某地区地下水环境质量进行评价,并和内梅罗指数法、灰色聚类法评价结果相比较,结果表明改进后的BP神经网络计算速度快、评价精度高、结果客观准确。     

GMS在地下水污染质运移数值模拟预测中的应用

利用地下水模型系统软件GMS,对研究区污染物在地下水中污染运移进行数值模拟.根据模拟结果,规划基地的防渗减小了对地下水的污染影响.建议加强监测,在做好防渗的同时,开展水力调控措施研究,降低地下水污染应急处理的技术难度.     

人工神经网络模型在河流减水河段景观质量评价中的应用

参照风景评价的心理物理学方法的一般过程,引入"水面视觉比例"概念,对人工神经网络在河流景观质量评价中的应用进行研究,建立了一个可预测减水河段河流景观质量的人工神经网络模型。该模型具有良好的收敛性和泛化能力。该模型的预测评价结果表明:河流周围植被较好时,水面视觉比例在5%～20%之间增加,河流景观改善明显;当周围植被较差时,水面视觉比例在15%～40%之间增加,河流景观改善明显,即河流周围植被能缓解河段减水的不利景观影响。在水资源开发利用中,除应保证河道内一定的流量外,还应充分结合植物措施及工程措施,对河流河道进行适当的修复,以维持或改善河流景观。     

HEC-RAS模型在南汀河防洪评价中的应用

介绍了HEC-RAS模型计算闸坝壅水的基本原理和主要步骤,并以云南省临沧市孟定镇段的南汀河干流的壅水计算为实例,采用HEC-RAS模型,模拟了30年一遇、多年平均洪峰流量、平均年的设计洪水频率下,建坝前后的河道水面线,进而求得坝前壅水高度和上游影响范围。计算结果表明,HEC-RAS模型在分析计算闸坝对河道的阻水壅高影响时方便实用,工程适用性较好,可供防洪工程决策制定时参考。     

HEC-RAS模型在密江特大桥防洪评价中的应用

介绍了HEC-RAS模型计算桥梁壅水的基本原理和主要步骤,并以吉林市至珲春市高速铁路客运专线密江特大桥的壅水计算为实例,采用HEC-RAS模型,模拟了300年一遇、100年一遇、50年一遇、20年一遇以及10年一遇设计洪水频率下建桥前后的河道水面线,进而求得桥梁阻水壅高值和壅水长度。计算结果表明,HEC-RAS模型在分析计算桥梁对河道的阻水壅高影响时方便实用,工程适用性较好,可供防洪工程决策制定的参考。     

HEC-RAS模型的原理、结构及在防洪影响评价中的应用

HEC-RAS模型是国外一款一维水动力软件,可模拟涉及桥梁、涵闸、泵站等多种建筑物的单一河道、树状或环状河网复杂水力系统.本文从模型基本构架入手,介绍了HEC-RAS模型的原理、组成及主要用途,运用实例说明其在防洪评价中的应用,并用MIKE11模型对HEC-RAS模型成果合理性进行了验证.     

均值化综合污染指数法在前郭灌区地下水水质污染评价中的应用

均值化综合污染指数法在前郭灌区地下水水质污染评价中的应用吉林省水利水电勘测设计院萧长来内容提要：灌区地下水是灌区生态环境、经济环境和社会环境的重要影响因素。前郭灌区地下水水质污染日益严重。本文提出用均值化综合污染指数法来评价灌区地下水水质污染程度，效...     

基于 MCMC 的 BP 网络在水质评价中的应用

一、水质评价方法水质评价方法总的来说可分为两大类：单因子评价法和综合评价法。单因子评价法计算简单,可直观反映哪些指标超标,但由于是对单个水质指标独立进行评价,因此得到的评价结果不能全面反映水质量的整体状况,可能会导致较大的偏差。综合评价法可同时评价多种指标对水质的影响,从整体上给水质一个客观全面的评价。当前研究较多的综合评价法包括综合指数法、灰色聚类法、灰色模式识别法、模糊综合评判法、模糊模式识别法和人工神经网络模型等。综合指数法通过多个指标并赋予各指标不同权重的综合来判断水质标准,它综合考虑了各指标对水质的影响,     

地下水污染运移数值模拟及最优估计

本文基于剖开算子法，提出耦合求解地下水流方程和污染运移方程的数值方法，并Ｋａｌｍａｎ滤波技术在地下水污染研究中的应用。计算例表明，本文所提数值方法及Ｋａｌｍａｎ滤波理论的应用，能够避免数值模拟过程中的不良现象，大大提高计算精度，更好地应用于实际工程。     

地下水污染溯源技术应用进展

地下水污染相比于地表水与大气污染,具有隐蔽性、滞后性和不可逆性等特点,污染物进入地下水后,随着水流不断扩散,难以及时找到污染源头并进行有效控制。对于地下水中的污染进行溯源,是进行土壤地下水污染修复的前提条件。同时,水污染事件中污染物来源不清也会导致责任归属难判定,从而引起案件纠纷等系列问题。首先对常见的地下水污染物的种类和来源进行了归纳,之后对目前地下水污染溯源技术和主要研究方向进行了综述,最后结合溯源方法的实践应用提出该方向应用的重难点。     

晋城市地下水水质污染分析与防治

通过整理分析多年水质临测资料，论述了晋城市浅、中、深层地下水的水质基本特征，并进行了水质评价和污染分析，针对性地提出了防治污染的对策。     

钱塘江河口污染负荷估算方法及其应用

根据钱塘江河口的特点,提出其污染负荷由富春江电站下泄的背景负荷、河口区面源污染负荷与点源负荷3部分组成.根据3部分负荷的排入特点,提出了简便、适宜的污染负荷计算方法,并计算了2007,2008年的CODMn、NH3N与 TP 的入江负荷量.污染负荷计算结果表明,富春江电站下泄的水体量大,但携带的污染负荷相对较小,是钱塘江干流维持较好水质的主要有利因素;点源排放的污水量所占比例最小,但其所占污染负荷比例较高;面源污染也是主要的负荷贡献者.研究结果为钱塘江河口纳污总量控制与水环境管理决策提供依据.     

华北平原区地下水开采量估算研究

准确获取地下水开采量对于区域地下水资源评价和管理工作具有十分重要的意义。但由于地下水开采活动过于分散,目前管理和计量的手段不足,导致地下水开采量统计数据存在较大误差。通过建立华北平原地下水流区域数值模型,利用模拟水位与实际观测水位相拟合的方法,完成了华北平原开采量的反演估算,最终获得了华北平原2002年-2008年各年的地下水开采量,其平均年开采量为249.20亿m3。     

Application of Artificial Neural Networks and Particle Swarm Optimization for the Management of Groundwater Resources

Ground management problems are typically solved by the simulation-optimization approach where complex numerical models are used to simulate the groundwater flow and/or contamination transport. These numerical models take a lot of time to solve the management problems and hence become computationally expensive. In this study, Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) models were developed and coupled for the management of groundwater of Dore river basin in France. The Analytic Element Method (AEM) based flow model was developed and used to generate the dataset for the training and testing of the ANN model. This developed ANN-PSO model was applied to minimize the pumping cost of the wells, including cost of the pipe line. The discharge and location of the pumping wells were taken as the decision variable and the ANN-PSO model was applied to find out the optimal location of the wells. The results of the ANN-PSO model are found similar to the results obtained by AEM-PSO model. The results show that the ANN model can reduce the computational burden significantly as it is able to analyze different scenarios, and the ANN-PSO model is capable of identifying the optimal location of wells efficiently.     

Identification of Pollution Sources in Transient Groundwater Systems

A methodology using a nonlinear optimization model is presentedfor estimating unknown magnitude, location and duration ofgroundwater pollution sources under transient flow and transportconditions. The proposed optimization model incorporates thegoverning equations of flow and solute transport as binding equalityconstraints, and thus essentially simulates the physical processes oftransient flow and transient transport in the groundwater systems.The proposed inverse model identifies unknown sources of pollution byusing measured values of pollutant concentration at selectedlocations. Performance of the proposed model for the identificationof unknown groundwater pollution sources is evaluated for anillustrative study area in a hypothetical confined aquifer underdifferent cases of data availability. The effect of observation welllocation vis-à-vis pollution source location on identificationaccuracy is also investigated. Performance of the developedidentification model is also evaluated for a condition whenconcentration measurements are missing during few initial timeperiods after the pollution sources become active. The effect ofspecified initial guesses of the variable values on the optimalsolutions are also investigated. These performance evaluation resultsdemonstrate the limitations and potential applicability of theproposed optimization model for identifying the sources of pollutionin transient groundwater systems.     

三塘湖地区地下水污染特征研究

文章中研究区地下水主要为碎屑岩类孔隙裂隙水。含水层岩为砂岩,裂隙较发育,富水性弱。由于地下水补给量极小,径流速度慢,导致地下水水质矿化度较大,因此研究区地下水污染较严重,水质较差,超标项较多,大多数不满足Ⅲ类生活饮用水及工业、农业用水标准的要求。     

Application of Artificial Neural Networks for Identifying Optimal Groundwater Pumping and Piping Network Layout

The simulation-optimization approach is often used to solve water resource management problem although repeated use of the simulation model enhances the computational load. In this study, Artificial Neural Network (ANN) and Bagged Decision Trees (BDT) models were developed as an approximator for Analytic Element Method (AEM) based groundwater flow model. Developed ANN and BDT models were coupled with Particle Swarm Optimization (PSO) model to solve the well-field management problem. The groundwater flow model was developed for the study area and used to generate the dataset for the training and testing of the ANN & BDT models. These coupled ANN-PSO & BDT-PSO models were employed to find the optimal design and cost of the new well-field system by optimizing discharge & co-ordinate of wells along with the cost effective layout of piping network. The Minimum Spanning Tree (MST) based model was used to find out the optimal piping network layout and checking the hydraulic constraints in the piping network. The results show that the ANN & BDT models are good approximators of AEM model and they can reduce the computational burden significantly although ANN model performs better than BDT model. The results show that the coupling of piping network model with simulation-optimization model is very significant for finding the cost effective and realistic design of the new well-field system.     

Application of Genetic Algorithms and Artificial Neural Networks in Conjunctive Use of Surface and Groundwater Resources

Abstract

In this paper, a methodology for conjunctive use of surface and groundwater resources is developed using the combination of the Genetic Algorithms (GAs) and the Artificial Neural Networks (ANN). Water supply to agricultural demands, reduction of pumping costs and control of groundwater table fluctuations are considered in the objective function of the model. In the proposed model, the results of MODFLOW groundwater simulation model are used to train an ANN. The ANN as groundwater response functions is then linked to the GA based optimization model to develop the monthly conjunctive use operating policies. The model is applied to the surface and groundwater allocation for irrigation purposes in the southern part of Tehran. A new ANN is also trained and checked for developing the real-time conjunctive use operating rules.

The results show the significance of an integrated approach to surface and groundwater allocation in the study area. A simulation of the optimal policies shows that the cumulative groundwater table variation can be reduced to less than 4 meters from the current devastating condition. The results also show that the proposed model can effectively reduce the run time of the conjunctive use models through the composition of a GA-based optimization and a ANN-based simulation model.