正交化方法在水质模型参数确定中的应用

本文根据常微分方程参数反问题的数学理论，将正交化方法同有限差分法结合用于确定水质模型参数，并与正则化方法、最速下降法和共轭梯度法作了比较。其计算结果对比表明，正交化方法具有快速、简便、可靠的特点。更适合于水质模型参数的确定。     

河流水质模型中确定水文参数的经验方法探讨

河流水质模型中,要涉及一些重要的水文参数。本文探索了用经验方法推求水文参数的问题。文中论证了公式的合理性,提出了能表达河流断面形状特性的指标(β),建议了根据不同情况确定公式中参数的原则,还分析了公式的适用条件。研究成果对提高水质模型的适用性和精度都具有重要意义。     

基于遗传算法的EMI滤波器自动进化设计

将遗传算法和电路进化设计应用于电源EMI滤波器的优化设计。针对EMI滤波器设计所需满足的各种限制条件,综合考虑滤波器插入损耗、阻抗失配、EMC标准等因素,确定设计优化准则,选择进化算法优选适合的电路拓扑,用遗传算法确定元件参数,实现滤波器自动设计。对遗传算法存在的“早熟”现象提出改进措施。实验验证了该优化设计结果能有效抑制EMI噪声,具有实用意义。     

基于神经网络下的车辆自动控制系统决策算法分析研究

为了提高车辆自动控制能力,提出基于神经网络下的车辆自动控制系统决策算法。构建车辆自动控制系统的行驶动力学和运动学模型,以相位偏移和惯性转矩为约束参量构建车辆自动控制系统模糊反馈误差跟踪融合控制律,采用模糊参数融合和自适应参数调节方法进行车辆自动控制系统决策和模型预测,采用变结构神经网络控制的方法进行车辆自动控制系统的模糊决策构造,建立车辆自动控制系统决策的约束参数优化模型,采用自适应模糊跟踪融合识别方法进行车辆自动控制系统决策的参数寻优,实现车辆自动控制系统的优化决策控制。仿真结果表明,采用该方法进行车辆自动控制系统决策控制的自适应性性较好,控制输出的鲁棒性较强。     

河流水质模型参数的优化估算探讨

采用最速下降法和复合形法对河流动态水质模型的参数进行了优化估算，并从计算方法的适用范围、计算机编程的复杂程度、整体工作量的大小、计算结果的代表性和合理性等方面对这两种方法进行了比较分析。     

河汉水质模型参数的优化估算探讨

采用最速下降法和复合形法对河流动态水质模型的参数进行了优化估算，并从计算方法的适用范围、计算机编程的复杂程度，整体工作量的大小，计算结果的代表性合理性等方面对这两种方法进行了比较分析。     

基于相关向量机模型的腐蚀声发射信号识别

相关向量机(RVM)模型的分类性能与其核函数参数的选择有密切关系。本文分别利用人工蜂群算法(ABC)、粒子群算法(PSO)和遗传算法(GA)寻找相关向量机模型的最优参数,对几种方法的寻优性能进行了对比。采用基于二叉树结构的一对多扩展方法,对二分类相关向量机模型进行了扩展,建立了四分类模型。基于该分类模型对罐底腐蚀声发射信号进行识别,将声发射特征参数和频域参数作为模型的输入参数,获得了较好的识别结果。     

基于GF-1号卫星的龙江水库水环境遥感监测适宜性

研究基于GF-1(高分一号)卫星2014~2016年高分影像资料,对四川省资中县龙江水库开展卫星遥感监测,通过对叶绿素a、透明度、悬浮物3种水质参数进行反演,并结合实地调查和环境监测,分析可能存在的污染区域和相关指标及其相关性。结果表明卫星遥感监测可为龙江水库的水质监测、富营养化监管提供参考。但是,要提高遥感监测的准确度和精确度,需要优化相关参数、构建总氮总磷等关键指标的反演模型,以及未来传感器的改进和遥感科学的发展。     

人工神经网络在湟水水质综合评价中的应用

将“前向人工神经网络技术”应用于水质综合评价，提出了水质综合评价BP模型，并将该模型应用于实例进行效果检验，结果表明：人工神经网络BP模型用于水质综合评价是可行的，该方法适应性强，评价结果客观、合理。     

利用二维模型求解太湖水质CODMn的研究

在二维黎曼近似解模型的基础上建立了太湖水质预测模型，并运用该模型对太湖的水质指标CODMn了模拟。模拟的结果跟太湖各监测站的测量值相接近，表明该模型能较好的运用于太湖的水质预测。     

An imprecise fuzzy risk approach for water quality management of a river system

Uncertainty plays an important role in water quality management problems. The major sources of uncertainty in a water quality management problem are the random nature of hydrologic variables and imprecision (fuzziness) associated with goals of the dischargers and pollution control agencies (PCA). Many Waste Load Allocation (WLA) problems are solved by considering these two sources of uncertainty. Apart from randomness and fuzziness, missing data in the time series of a hydrologic variable may result in additional uncertainty due to partial ignorance. These uncertainties render the input parameters as imprecise parameters in water quality decision making. In this paper an Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is developed for water quality management of a river system subject to uncertainty arising from partial ignorance. In a WLA problem, both randomness and imprecision can be addressed simultaneously by fuzzy risk of low water quality. A methodology is developed for the computation of imprecise fuzzy risk of low water quality, when the parameters are characterized by uncertainty due to partial ignorance. A Monte-Carlo simulation is performed to evaluate the imprecise fuzzy risk of low water quality by considering the input variables as imprecise. Fuzzy multiobjective optimization is used to formulate the multiobjective model. The model developed is based on a fuzzy multiobjective optimization problem with max–min as the operator. This usually does not result in a unique solution but gives multiple solutions. Two optimization models are developed to capture all the decision alternatives or multiple solutions. The objective of the two optimization models is to obtain a range of fractional removal levels for the dischargers, such that the resultant fuzzy risk will be within acceptable limits. Specification of a range for fractional removal levels enhances flexibility in decision making. The methodology is demonstrated with a case study of the Tunga–Bhadra river system in India.     

Lake eutrophication management modeling using dynamic programming

Lake eutrophication problems have received considerable attention in Taiwan, especially because they relate to the quality of drinking water. In this study, steady-state river water quality and lake eutrophication models are solved using dynamic programming algorithms to find the nutrient removal rates for eutrophication control during dry season. The kinetic cycle of chlorophyll-a, phosphorus and nitrogen for a complete-mixed lake is considered in the optimization framework. The Newton-iterative technique is adopted to solve the nonlinear equations for the steady-state lake eutrophication model. The optimization framework is applied to Cheng-Ching Lake in southern Taiwan. Several nutrient loading scenarios for eutrophication control are studied. Optimization results for nutrient removal rates and corresponding wastewater treatment capacities of each reach of the Kao-Ping River define the least cost approach to lake eutrophication control. A natural purification method, structural free water surface wetland, is also suggested to save more investment and improve river water quality at the same time.     

An Interval-Parameter Waste-Load-Allocation Model for River Water Quality Management Under Uncertainty

A simulation-based interval quadratic waste load allocation (IQWLA) model was developed for supporting river water quality management. A multi-segment simulation model was developed to generate water-quality transformation matrices and vectors under steady-state river flow conditions. The established matrices and vectors were then used to establish the water-quality constraints that were included in a water quality management model. Uncertainties associated with water quality parameters, cost functions, and environmental guidelines were described as intervals. The cost functions of wastewater treatment units were expressed in quadratic forms. A water-quality planning problem in the Changsha section of Xiangjiang River in China was used as a study case to demonstrate applicability of the proposed method. The study results demonstrated that IQWLA model could effectively communicate the interval-format uncertainties into optimization process, and generate inexact solutions that contain a spectrum of potential wastewater treatment options. Decision alternatives can be generated by adjusting different combinations of the decision variables within their solution intervals. The results are valuable for supporting local decision makers in generating cost-effective water quality management strategies.     

Application of a Multi-Objective Optimization Method to Provide Least Cost Alternatives for NPS Pollution Control

Nonpoint source (NPS) pollutants such as phosphorus, nitrogen, sediment, and pesticides are the foremost sources of water contamination in many of the water bodies in the Midwestern agricultural watersheds. This problem is expected to increase in the future with the increasing demand to provide corn as grain or stover for biofuel production. Best management practices (BMPs) have been proven to effectively reduce the NPS pollutant loads from agricultural areas. However, in a watershed with multiple farms and multiple BMPs feasible for implementation, it becomes a daunting task to choose a right combination of BMPs that provide maximum pollution reduction for least implementation costs. Multi-objective algorithms capable of searching from a large number of solutions are required to meet the given watershed management objectives. Genetic algorithms have been the most popular optimization algorithms for the BMP selection and placement. However, previous BMP optimization models did not study pesticide which is very commonly used in corn areas. Also, with corn stover being projected as a viable alternative for biofuel production there might be unintended consequences of the reduced residue in the corn fields on water quality. Therefore, there is a need to study the impact of different levels of residue management in combination with other BMPs at a watershed scale. In this research the following BMPs were selected for placement in the watershed: (a) residue management, (b) filter strips, (c) parallel terraces, (d) contour farming, and (e) tillage. We present a novel method of combing different NPS pollutants into a single objective function, which, along with the net costs, were used as the two objective functions during optimization. In this study we used BMP tool, a database that contains the pollution reduction and cost information of different BMPs under consideration which provides pollutant loads during optimization. The BMP optimization was performed using a NSGA-II based search method. The model was tested for the selection and placement of BMPs in Wildcat Creek Watershed, a corn dominated watershed located in northcentral Indiana, to reduce nitrogen, phosphorus, sediment, and pesticide losses from the watershed. The Pareto optimal fronts (plotted as spider plots) generated between the optimized objective functions can be used to make management decisions to achieve desired water quality goals with minimum BMP implementation and maintenance cost for the watershed. Also these solutions were geographically mapped to show the locations where various BMPs should be implemented. The solutions with larger pollution reduction consisted of buffer filter strips that lead to larger pollution reduction with greater costs compared to other alternatives.     

Autonomous real-time adaptive management of soil salinity using a receding horizon control algorithm: a pilot-scale demonstration

Soil salinization is a potentially negative side effect of irrigation with reclaimed water. While optimization schemes have been applied to soil salinity control, these have typically failed to take advantage of real-time sensor feedback. This study incorporates current soil observation technologies into the optimal feedback-control scheme known as Receding Horizon Control (RHC) to enable successful autonomous control of soil salinization. RHC uses real-time sensor measurements, physically-based state prediction models, and optimization algorithms to drive field conditions to a desired environmental state by manipulating application rate or irrigation duration/frequency. A simulation model including the Richards equation coupled to energy and solute transport equations is employed as a state estimator. Vertical multi-sensor arrays installed in the soil provide initial conditions and continuous feedback to the control scheme. An optimization algorithm determines the optimal irrigation rate or frequency subject to imposed constraints protective of soil salinization. A small-scale field test demonstrates that the RHC scheme is capable of autonomously maintaining specified salt levels at a prescribed soil depth. This finding suggests that, given an adequately structured and trained simulation model, sensor networks, and optimization algorithms can be integrated using RHC to autonomously achieve water reuse and agricultural objectives while managing soil salinization.     

Automatic Calibration of Hydrologic Models With Multi‐Objective Evolutionary Algorithm and Pareto Optimization1

Abstract: In optimization problems with at least two conflicting objectives, a set of solutions rather than a unique one exists because of the trade‐offs between these objectives. A Pareto optimal solution set is achieved when a solution cannot be improved upon without degrading at least one of its objective criteria. This study investigated the application of multi‐objective evolutionary algorithm (MOEA) and Pareto ordering optimization in the automatic calibration of the Soil and Water Assessment Tool (SWAT), a process‐based, semi‐distributed, and continuous hydrologic model. The nondominated sorting genetic algorithm II (NSGA‐II), a fast and recent MOEA, and SWAT were called in FORTRAN from a parallel genetic algorithm library (PGAPACK) to determine the Pareto optimal set. A total of 139 parameter values were simultaneously and explicitly optimized in the calibration. The calibrated SWAT model simulated well the daily streamflow of the Calapooia watershed for a 3‐year period. The daily Nash‐Sutcliffe coefficients were 0.86 at calibration and 0.81 at validation. Automatic multi‐objective calibration of a complex watershed model was successfully implemented using Pareto ordering and MOEA. Future studies include simultaneous automatic calibration of water quality and quantity parameters and the application of Pareto optimization in decision and policy‐making problems related to conflicting objectives of economics and environmental quality.     

Optimal nonpoint source pollution control strategies for a reservoir watershed in Taiwan

The purpose of this study is to develop a model for optimal nonpoint source pollution control for the Fei-Tsui Reservoir watershed in Northern Taiwan. Several structural best management practices (BMPs) are selected to treat stormwater runoff. The complete model consists of two interacting components: an optimization model based on discrete differential dynamic programming (DDDP) and a zero-dimensional reservoir water quality model. A predefined procedure is used to locate suitable sites for construction of various selected BMPs in the watershed. In the optimization model, the objective function is to find the best combination of BMP type and placement, which minimizes the total construction and operation, maintenance, and repair (OMR) costs of the BMPs. The constraints are the water quality standards for total phosphorus (TP) and total suspended solids (TSS) concentrations in the reservoir. A zero-dimensional reservoir water quality model of the Vollenweider type is embedded in the optimization framework to simulate pollutant concentrations in Fei-Tsui Reservoir. The resulting optimal cost and benefit of water quality improvement are depicted by the model-derived trade-off curves. The modeling framework developed in the present study could be used as an efficient tool for planning a watershed-wide implementation of BMPs for mitigating stormwater pollution impact on the receiving water bodies.     

Parameter Uncertainty Reduction for SWAT Using Grace,Streamflow, and Groundwater Table Data for Lower Missouri River Basin1

Abstract: This study incorporates the newly available Gravity Recovery and Climate Experiment (GRACE) water storage data and water table data from well logs to reduce parameter uncertainty in Soil and Water Assessment Tool (SWAT) calibration using a SUFI2 (sequential uncertainty fitting) framework for the Lower Missouri River Basin. Model evaluations are performed in multiple stages using a multiobjective function consisting of multisite streamflow and GRACE water storage data as well as a groundwater component. Results show that (1) a model calibrated with both streamflow and GRACE data simultaneously can maintain the water balance for the whole basin, but may improperly partition surface flow and base flow. Additional inclusion of the groundwater constraint can significantly improve the model performance in groundwater hydrological processes. In our case, the estimation of specific yield of shallow aquifers has been increased to 10^?2 from previous much underestimated level (<10^?3). (2) The daily streamflow data are needed to confine the parameters related to water flow in channels such as the Manning’s coefficient, which are less sensitive to the monthly simulations. (3) Parameters are nonuniformly sensitive for different goal variables, and thus, proper specification of a prior distribution of parameters may be the key factor for global optimization algorithms to obtain stable and realistic model performance.     

REAL CODED GENETIC ALGORITHM OPTIMIZATION OF LONG TERM RESERVOIR OPERATION1

ABSTRACT: An optimization and simulation model holds promise as an efficient and robust method for long term reservoir operation, an increasingly important facet of managing water resources. Recently, genetic algorithms have been demonstrated to be highly effective optimization methods. According to previous studies, a real coded genetic algorithm (RGA) has many advantages over a binary coded genetic algorithm. Accordingly, this work applies an RGA to obtain the 10‐day (the traditional period of reservoir operation in Taiwan) operating rule curves for the proposed reservoir system. The RGA is combined with an effective and flexible scheme for coding the reservoir rule curves and applied to an important reservoir in Taiwan, considering a water reservoir development scenario to the year 2021. Each rule curve is evaluated using a complex simulation model to determine a performance index for a given flow series. The process of generating and evaluating decision parameters is repeated until no further improvement in performance is obtained. Many experiments were performed to determine the suitable RGA components, including macro evolutionary (ME) selection and blend‐α crossover. Macro evolution (ME) can be applied to prevent the premature problem of the conventional selection scheme of genetic algorithm. The purpose of adjusting a of a crossover scheme is to determine the exploratory or exploitative degree of various subpopulations. The appropriate rule curve searched by an RGA can minimize the water deficit and maintain the high water level of the reservoir. The results also show that the most promising RGA for this problem consists of these revised operators significantly improves the performance of a system. It is also very efficient for optimizing other highly nonlinear systems.     

分段控制江水污染治理系数的最优解

本文根据（环境科学概论）的水质基本方程，对江水分段水质管理提出数学模型，用线性规划和最优化方法提出控制江水污染的决策措施，我们可以根据这些数据有计划地提出治理方案，使江水水质符合国家标准，同时使资金投入最省。