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.     

A Two-Stage Particle Swarm Optimization Algorithm for MPPT of Partially Shaded PV Arrays

The power-voltage (P-V) characteristic curves of a PV array are nonlinear and have multiple peaks under partially shaded conditions (PSCs). This paper proposes a novel maximum power point tracking (MPPT) method for a PV system with reduced steady-state oscillation based on a two-stage particle swarm optimization (PSO) algorithm. The grouping method of the shuffled frog leaping algorithm (SFLA) is incorporated in the basic PSO algorithm (PSO-SFLA), ensuring fast and accurate searching of the global extremum. An adaptive speed factor is also introduced into the improved PSO to further enhance its convergence speed. Test results show that the proposed method converges in less than half the time taken by the conventional PSO method, and the power is improved by 33% under the worst PSCs, which confirms the superiority of the proposed method over the standard PSO algorithm in terms of tracking speed and steady-state oscillations under different PSCs.     

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。     

Stormwater Control Measures for Runoff and Water Quality Management in Urban Landscapes

This study develops and tests a novel optimization method for optimally selecting and sizing stormwater control measures (SCMs) in urban landscapes for selected design storms. The developed methodology yields SCMs that capture and retain stormwater via onsite percolation, remove stormwater pollutants, and minimize stormwater control expenditures. The resulting environmental optimization problem involves integer and real variables imbedded in an objective function that is subjected to multiple constraints. This study's methodology aims at practicality and ease of implementation in the solution of the SCM sizing and selection optimization problem while taking into account the main factors that govern stormwater management in urban landscapes. The near‐optimal global solution of the SCM selection and design problem is obtained with nonlinear programming and verified with the average of multiple solutions calculated with multiple runs of an optimization evolutionary algorithm. The developed methodology is illustrated with one stormwater project in the City of Los Angeles, California.     

Virtues of simple hydro-economic optimization: Baja California, Mexico

This paper uses simple hydro-economic optimization to investigate a wide range of regional water system management options for northern Baja California, Mexico. Hydro-economic optimization models, even with parsimonious model formulations, enable investigation of promising water management portfolios for supplying water to agricultural, environmental and urban users. CALVIN, a generalized hydro-economic model, is used in a case study of Baja California. This drought-prone region faces significant challenges to supply water to agriculture and its fast growing border cities. Water management portfolios include water markets, wastewater reuse, seawater desalination and infrastructure expansions. Water markets provide the flexibility to meet future urban demands; however conveyance capacity limits their use. Wastewater reuse and conveyance expansions are economically promising. At current costs desalination is currently uneconomical for Baja California compared to other alternatives. Even simple hydro-economic models suggest ways to increase efficiency of water management in water scarce areas, and provide an economic basis for evaluating long-term water management solutions.     

A Conflict-Resolution Model for the Conjunctive Use of Surface and Groundwater Resources that Considers Water-Quality Issues: A Case Study

The conjunctive use of surface and groundwater resources is one alternative for optimal use of available water resources in arid and semiarid regions. The optimization models proposed for conjunctive water allocation are often complicated, nonlinear, and computationally intensive, especially when different stakeholders are involved that have conflicting interests. In this article, a new conflict-resolution methodology developed for the conjunctive use of surface and groundwater resources using Nondominated Sorting Genetic Algorithm II (NSGA-II) and Young Conflict-Resolution Theory (YCRT) is presented. The proposed model is applied to the Tehran aquifer in the Tehran metropolitan area of Iran. Stakeholders in the study area have conflicting interests related to water supply with acceptable quality, pumping costs, groundwater quality, and groundwater table fluctuations. In the proposed methodology, MODFLOW and MT3D groundwater quantity and quality simulation models are linked with the NSGA-II optimization model to develop Pareto fronts among the objectives. The best solutions on the Pareto fronts are then selected using YCRT. The results of the proposed model show the significance of applying an integrated conflict-resolution approach to conjunctive use of surface and groundwater resources in the study area.     

REGIONAL WATER SUPPLY PLANNING BY INTERACTIVE SYSTEMS ANALYSIS1

Abstract: This paper describes an interactive data and model generator that is intended to bridge the gap between the water resource enginner and planner and the mathematical progrmming systems approach to regional water supply planning. The optimization objective is to minimize total annual cost with respect to capital investment and operation and maintenance costs. The matrix generator formulates the necessary hydrologic, demographic and programming problem for system optimization. The interactive program guides the user through the input and optimization segments, totally eliminates the chore of manually structuring the model matrix aides in eliminating errors, and allows use by planners without skill in mathematical programming.     

Forecasting iron ore import and consumption of China using grey model optimized by particle swarm optimization algorithm

The iron and steel industry plays a fundamental role in a country's national economy, especially in developing countries. China is the largest iron ore consumption market in the world. However, because of limited domestic iron ore resources, a large proportion of iron ore is imported from other countries. Faced with the conflict between the iron ore supply shortage and the growing demand, it is necessary for the government to predict imports and total consumption. This paper develops a high-precision hybrid model based on grey prediction and rolling mechanism optimized by particle swarm optimization algorithm. We use the China Statistical Yearbook (1996–2011) as our database to test the efficiency and accuracy of the proposed method. According to the experimental results, the proposed new method clearly can improve the prediction accuracy of the original grey model. Future projections have also been done for iron ore imports and total consumption in China in the next five years.     

Institutional Constraints on Cost‐Effective Water Management: Selenium Contamination in Colorado's Lower Arkansas River Valley

Ground and surface water selenium (Se) contamination is problematic throughout the world, leading to harmful impacts on aquatic life, wildlife, livestock, and humans. A groundwater reactive transport model was applied to a regional‐scale irrigated groundwater system in the Lower Arkansas River Basin in southeastern Colorado to identify management practices that remediate Se contamination. The system has levels of surface water and groundwater Se concentrations exceeding the respective chronic standard and guidelines. We evaluate potential solutions by combining the transport model with an assessment of the cost to employ those practices. We use a framework common in economics and engineering fields alike, the Pareto frontier, to show the impact of four different best management practices on the tradeoffs between Se and cost objectives. We then extend that analysis to include institutional constraints that affect the economic feasibility associated with each practice. Results indicate that although water‐reducing strategies have the greatest impact on Se, they are the hardest for farmers to implement given constraints common to western water rights institutions. Therefore, our analysis shows that estimating economic and environmental tradeoffs, as is typically done with a Pareto frontier, will not provide an accurate picture of choices available to farmers where institutional constraints should also be considered.     

WATERSHED LEVEL BEST MANAGEMENT PRACTICE SELECTION AND PLACEMENT IN THE TOWN BROOK WATERSHED, NEW YORK1

Abstract: For a number of years, best management practices (BMPs) have been implemented within the Town Brook watershed as part of a watershed wide effort to reduce phosphorus losses to the New York City water supply reservoirs. Currently, there are no quantitative indications of the effectiveness of these practices at the watershed scale. Additionally, work is needed to evaluate management practice solutions for costs in relation to effectiveness. In this study we develop a methodology for evaluating management solutions to determine the best way(s) to select and place management practices so that pollutant removal targets are met at minimum cost. The study combines phosphorus losses as simulated by the Soil and Water Assessment Tool (SWAT), management practice effectiveness estimates from a predeveloped characterization tool, and practice costs in optimizations using a genetic algorithm. For a user defined target phosphorus removal (60 percent for this study), optimization favors nutrient management plans, crop rotations, contour strip cropping, and riparian forest buffers; the most cost effective scenario achieves a cost effectiveness of 24/kg phosphorus removal per year compared to the 34/kg phosphorus removal per year associated with the current basic implementation scheme. The study suggests that there is a need to evaluate potential solutions prior to implementation and offers a means of generating and evaluating the solutions.     

COMPARATIVE STUDY OF OPTIMIZATION TECHNIQUES FOR IRRIGATION PROJECT PLANNING1

ABSTRACT: This study presents three optimization techniques for on‐farm irrigation scheduling in irrigation project planning: namely the genetic algorithm, simulated annealing and iterative improvement methods. The three techniques are applied to planning a 394.6 ha irrigation project in the town of Delta, Utah, for optimizing economic profits, simulating water demand, and estimating the crop area percentages with specific water supply and planted area constraints. The comparative optimization results for the 394.6 ha irrigated project from the genetic algorithm, simulated annealing, and iterative improvement methods are as follows: (1) the seasonal maximum net benefits are $113,826, $111,494, and $105,444 per season, respectively; and (2) the seasonal water demands are 3.03*10³ m³, 3.0*10³ m³, and 2.92*10³ m³ per season, respectively. This study also determined the most suitable four parameters of the genetic algorithm method for the Delta irrigated project to be: (1) the number of generations equals 800, (2) population size equals 50, (3) probability of crossover equals 0.6, and (4) probability of mutation equals 0.02. Meanwhile, the most suitable three parameters of simulated annealing method for the Delta irrigated project are: (1) initial temperature equals 1,000, (2) number of moves equal 90, and (3) cooling rate equals 0.95.     

A novel objective function for optimal DG allocation in distribution systems using meta-heuristic algorithms

Meta heuristic algorithms have been introduced as a powerful method to solve the nonlinear optimization problems. These algorithms have been employed in many complex engineering problems due to their high capability in finding the solutions and reaching the optimal results within a short period of time. Optimization of distributed generation units in distribution systems, which have profoundly impacted on the system losses and voltage profile, is one of these nonlinear problems. In this study, a novel objective function was proposed for optimization procedure by meta-heuristic algorithms. The related objective function consists of the total cost of distributed generation units, cost of the purchased natural gas, cost of distribution system power losses, and penalty for greenhouse gas emissions. The electrical, cooling, and heating loads were considered in this study. In the distribution system, the waste and fuel cell were used to supply the required heating and cooling loads. The meta-heuristic algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Imperialist Competitive Algorithm (ICA) were employed to find the optimal location and size of distributed generation units in a distribution system. A detailed performance analysis was done on 13 bus radial distribution system. The performances of three algorithms were compared with each other and results showed that the PSO was the fastest; and had the best solution and optimum results. Furthermore, the PSO reached the optimum solution in a fewer number of iterations than the GA and ICA algorithms.     

A model of costs and resources for rural and peri-urban water supply and sanitation in the 1990s

This article develops a model of cost and financing strategies for rural and peri-urban water supply and sanitation. It suggests that significant progress towards the World Summit for Children's goal of universal access to water supply and sanitation can be made if a combination of strategies is adopted. On the cost side, significant cost reductions should be possible through efficiency in resource use and reduction of system management costs. On the financing side, it suggests restructuring the financing of the sector with improved efficiency and greater cost recovery in urban services; full recovery of operation and maintenance costs; cost sharing through community contributions in kind such as local labour and financially in rural and peri-urban water supply for basic levels of service depending on willingness and ability to pay and full cost recovery for higher levels of service; a high degree of cost recovery in rural and peri-urban sanitation; development of institutional structures for both collection and management of revenues; development of alternate financing mechanisms such as rural credit schemes and revolving funds, adapted in specific country contexts, including the required institutional mechanisms; and additional allocations from governments and external support agencies. Additional government or external financing alone, while critical, will not of itself lead to effectiveness in the use of resources. Equally, cost recovery alone cannot lead to universal access and sustainable solutions. A composite set of actions is needed within which building capacities of institutions and people is necessary for sustainability .     

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.     

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.     

MODELING OF WATER SUPPLY/DEMAND IN THE SOUTH PLATTE RIVER BASIN, 1970–20201

ABSTRACT: The application of a water balance model in finding “solutions” to the supply/demand problem was demonstrated using the South Platte River basin as a case study. Solutions were ascertained by hand, using both “average” and “stress” supply/demand conditions, and were developed for 1980, 2000, and 2020; nonquantifiable boundary conditions were incorporated by judgement. The solution obtained for a particular set of conditions is not unique and has strong normative characteristics; thus it must be judged by various interest groups having different ethical positions. The water balance model has a tabular display format and so the “model” is merely a simple table, i.e., a “water balance table.” In this work the water balance table was displayed on an eight-foot by eight-foot color-coded magnetic board. The board provides a means to both find and display the needed supply/demand “solution.” The tabular display facilitates understanding of the systemwide solution and the formulation of value judgments. Based upon these value judgments and an initial “straw man” solution, successive negotiated solutions can be found which can minimize “conflict.”     

A LINEAR ANALYSIS OF AN URBAN WATER SUPPLY SYSTEM1

The use of linear programming as a planning tool for determining the optimal long-range development of an urban water supply system was explored. A stochastic trace of water demand was synthesized and used as an input to the model. This permitted evaluating the feasibility of imposing demand restrictions as an effective cost reduction mechanism. The City of Lincoln, Nebraska, was used as the urban model. The fundamental problem was to allocate limited water supplies from several sources to an urban load center to minimize costs and comply with system constraints. The study period covered twenty years, and findings indicate the planning direction for stage development during this period. Sensitivity analyses were performed on cost coefficients and demands. Thirteen sources were included in the initial computations. Conclusions were that linear programming and generated demand traces are useful tools for both short- and long-term urban water supply planning. Lowering peak demands results in long-range development of fewer sources of supply and more economic and efficient use of the supplies developed.     

Multi-criteria PSO-based optimal design of grid-connected hybrid renewable energy systems

ABSTRACT

Human-induced climate change through the over liberation of greenhouse gases, resulting in devastating consequences to the environment, is a concern of considerable global significance which has fuelled the diversification to alternative renewable energy sources. The unpredictable nature of renewable resources is an impediment to developing renewable projects. More reliable, effective, and economically feasible renewable energy systems can be established by consolidating various renewable energy sources such as wind and solar into a hybrid system using batteries or back-up units like conventional energy generators or grids. The precise design of these systems is a critical step toward their effective deployment. An optimal sizing strategy was developed based on a heuristic particle swarm optimization (PSO) technique to determine the optimum number and configuration of PV panels, wind turbines, and battery units by minimizing the total system life-cycle cost while maximizing the reliability of the hybrid renewable energy system (HRES) in matching the electricity supply and demand. In addition, by constraining the amount of conventional electricity purchased from the grid, environmental concerns were also considered in the presented method. Various systems with different reliabilities and potential of reducing consumer’s CO₂ emissions were designed and the behavior of the proposed method was comprehensively investigated. An HRES may reduce the annualized cost of energy and carbon footprint significantly.     

传染病医院建筑给排水设计初探

本文针对传染病医院建筑给排水的设计进行研究.从实例出发,分析了传染病院的功能特点及不同类型传染病所采取的措施要点,从有利于洁净及防止二次污染、污水处理及消防给水设计等几个方面提出自己的看法及相应的技术措施.     

WATERSHED BASED,INSTITUTIONAL APPROACH TO DEVELOPING CLEAN WATER RESOURCES1

ABSTRACT: Access to clean and sufficient amounts of water is a critical problem in many countries. A watershed approach is vital in understanding pollution pathways affecting water resources and in developing participatory solutions. Such integration of information with participatory approaches can lead to more sustainable solutions than traditional “crisis‐to‐crisis” management approaches. This study aims at applying a watershed based joint action approach to manage water resources. Since most watersheds have urban and rural sources of pollution and a wide disparity in access to and use of water, alternative solutions need to take an integrated approach through cooperative actions. An institutional model was applied to seven subwatersheds in Honduras to evaluate various sources and effects of water contamination and water shortages. Two specific pathways of water resources degradation were studied (contamination from coffee pulp manufacturing and urban nonpoint sources) to develop alternative solutions that mitigate downstream impacts of access to clean water. A locally driven joint mechanism to reuse coffee pulp in farming systems is proposed. Such an institutional solution can maximize benefits to both farms and the coffee pulp industry. A combination of education and investment in sanitary facilities in urbanizing areas is proposed to minimize urban sources of water contamination.