River water quality management model using genetic algorithm

Conventional mathematical programming methods, such as linear programming, non linear programming, dynamic programming and integer programming have been used to solve the cost optimization problem for regional wastewater treatment systems. In this study, a river water quality management model was developed through the integration of a genetic algorithm (GA). This model was applied to a river system contaminated by three determined discharge sources to achieve the water quality goals and wastewater treatment cost optimization in the river basin. The genetic algorithm solution, described the treatment plant efficiency, such that the cost of wastewater treatment for the entire river basin is minimized while the water quality constraints in each reach are satisfied. This study showed that genetic algorithm can be applied for river water quality modeling studies as an alternative to the present methods.     

Environmental-economic models for total emission control of regional environmental pollution — input-output approach

This paper deals with a Leontief-type dynamic input-output analysis for total emission control of pollution. A dynamic input-output model can be described as a linear programming problem. In this paper an objective function to be minimized is chosen as the sum of pollutants emitted in a regional area throughout the planning period. It is assumed that consumption grows with a constant growth rate throughout the planning period. With this model, we can tell how to assign the elimination rate of pollutants to each production sector, under the constraint that the total emission standard is satisfied in each term of the planning period.     

Optimizing the horizontal structural diversity in uneven-aged northern hardwood stands

Two mathematical programming formulations are presented which allow the determination of diameter distributions that maximize the diameter class diversity in uneven-aged northern hardwood stands. Distributions generated from these models were found to be comparable from a management standpoint and could be incorporated into existing linear programming models as alternative management scenarios. The models presented here provide an initial framework for quantitatively addressing the requirements of the US National Forest Management Act in matters of diversity in the planning process.     

Multiple-pattern parameter identification and uncertainty analysis approach for water quality modeling

This paper presents a multiple-pattern parameter identification and uncertainty analysis approach for robust water quality modeling using a neural network (NN) embedded genetic algorithm (GA). The modeling approach uses an adaptive NN–GA framework to inversely solve the governing equations in a water quality model for multiple parameter patterns, along with an alternating fitness method to maintain solution diversity. The procedure was demonstrated through a coupled 2D hydrodynamic and eutrophication model for Loch Raven Reservoir in Maryland. The inverse problem was formulated as a nonlinear optimization problem minimizing the degree of misfit (DOM) between model results and observed data. A set of NN models was developed to approximate the input-output response relationship of the Loch Raven Reservoir model and was incorporated into a GA framework in an adaptive fashion to search for near-optimal solutions minimizing the DOM. The numerical example showed that the adaptive NN–GA approach is capable of identifying multiple parameter patterns that reproduce the observed data equally well. The resulting parameter patterns were incorporated into the numerical model, and a multiple-pattern robust water quality modeling analysis, along with a compound margin of safety (CMOS) method, was proposed and applied to analyze the parameter pattern uncertainty.     

Fouling mechanisms in the early stage of an enhanced coagulation-ultrafiltration process

We investigated the fouling performances of ultrafiltration (UF) membrane for treating in-line coagulated water in an enhanced coagulation-UF hybrid process. Then we analyzed the fouling mechanisms in the early stage of UF using mathematical models and microscopy observation methods. Finally, we discussed the impact of aeration on membrane fouling in this paper. The results showed that a two-stage of trans-membrane pressure (TMP) profile during the operation of enhanced coagulation-UF membrane was observed, and the relationship between permeability and operation time fitted well with a logarithmic curve. Membrane pores blocking and cake filtration were confirmed as main membrane fouling mechanisms using the mathematical models. The two stages of membrane fouling mechanisms were further deduced, namely, the membrane pore narrowing followed by the formation of cake layer. Membrane autopsy analysis using scanning electron microscopy (SEM) images of the membrane surface sampled from different filtration cycles also confirmed the mechanisms of pores blocking and cake filtration. Moreover, according to the variations of the permeability and membrane fouling resistance, aeration was able to mitigate and control the membrane fouling to a certain extent, but the optimization of aeration conditions still needs to be studied.     

产业生态系统多尺度能值整合评价方法

能值分析方法为自然环境资源与人类社会、经济的统一评价提供了新的思路,但面对当今产业生态学发展的实践需求,在多尺度整合分析、动态模型构建等方面仍显薄弱,需要与经济学等其他学科相对成熟的分析方法与模型加以整合。文章以能值综合方法为核心介质,从成本分析与效果分析的整合、能值分析与区域经济分析的整合,以及能值分析表与投入-产出矩阵模型的整合三个方面进行了产业生态学的能值整合研究方法的具体构建,以促进能值理论方法与产业生态学研究需求的进一步耦合。     

A model for the analysis of seasonal aspects of water quality control

A multiseasonal mathematical programming model for the analysis of water quality control within a river basin is presented. Phenomena of interseasonal variation in flow intensity and the river's pollutant assimilative capacity are introduced into the model by defining several seasons and their characterizations by seasonal parameters. An application of the model to a typical case situation is reported. The results suggest that seasonal adjustments in the treatment levels of wastewater treatment plants involve a considerable saving potential in comparison with a situation in which treatment levels are rigidly determined for the entire year.     

Finding all optimal solutions to the reserve site selection problem: formulation and computational analysis

The problem of selecting nature reserves has received increased attention in the literature during the past decade, and a variety of approaches have been promoted for selecting those sites to include in a reserve network. One set of techniques employs heuristic algorithms and thus provides possibly sub-optimal solutions. Another set of models and accompanying algorithms uses an integer programming formulation of the problem, resulting in an optimization problem known as the Maximal Covering Problem, or MCP. Solution of the MCP provides an optimal solution to the reserve site selection problem, and while various algorithms can be employed for solving the MCP they all suffer from the disadvantage of providing a single optimal solution dictating the selection of areas for conservation. In order to provide complete information to decision makers, the determination of all alternate optimal solutions is necessary. This paper explores two procedures for finding all such solutions. We describe the formulation and motivation of each method. A computational analysis on a data set describing native terrestrial vertebrates in the state of Oregon illustrates the effectiveness of each approach.     

辐射型生态工程结构优化研究

本文根据辐射型生态工程——星火养殖场的生产情况,建立新型投入产出模型,结合线性规划模型,对系统结构进行综合评价和优化设计。模式Ⅰ优化结果,利润最大值41万元,是实际值的4倍;模式Ⅱ的最优结构与模式Ⅰ的相同,利润最大值77万元。结果表明,充分利用系统设施和不断采用新技术是取得最佳效益的两个重要方面。     

Solving the probabilistic reserve selection problem

The Reserve Selection Problem consists in selecting certain sites among a set of potential sites for biodiversity protection. In many models of the literature, the species present and able to survive in each site are supposed to be known. Here, for every potential site and for every species considered, only the probability that the species survives in the site is supposed to be known. The problem to select, under a budgetary constraint, a set of sites which maximizes the expected number of species is known in the literature under the name of probabilistic reserve selection problem. In this article, this problem is studied with species weighting to deal differently with common species and rare species. A spatial constraint is also considered preventing to obtain too fragmented reserve networks. As in Polasky et al. (2000), the problem is formulated by a nonlinear mathematical program in Boolean variables. Camm et al. (2002) developed a mixed-integer linear programming approximation that may be solved with standard integer programming software. The method gives tight approximate solutions but does not allow to tell how far these solutions are from the optimum. In this paper, a slightly different approach is proposed to approximate the problem. The interesting aspect of the approach, which also uses only standard mixed-integer programming software, is that it leads, not only to an approximate solution, but also to an upper limit on the true optimal value. In other words, the method gives an approximate solution with a guarantee on its accuracy. The linear reformulation is based on an upper approximation of the logarithmic function by a piecewise-linear function. The approach is very effective on artificial instances that include up to 400 sites and 300 species. Within an average CPU time of about 12 min, near-optimal solutions are obtained with an average relative error, in comparison to the optimum, of less than 0.2%.     

Progress in global ecological modelling

The development of ecological modelling on global level since the middle of the 19th century is first reviewed, including application of statistical analysis, introduction of logistic curve, earth surface modeling, systems ecology, computer-oriented mathematical models and spatially explicit models. Finally, we discuss problems existing in ecological modelling on global level.     

Aggregation and fishery dynamics: Multiple time scales,times to extinction,and random environments

This paper is a mathematical study of the models introduced by C.W. Clark and M. Mangel to describe the tropical tuna purse seine fishery. By casting the equations into dimensionless form, it is easy to study the bifurcations of the system models and to study the dynamical behavior of the models. The dynamics are analyzed by using singular perturbation theory. A number of stochastic problems related to the dynamics of the models are formulated and solutions are obtained by using the diffusion approximation and asymptotic analysis. Finally, the question of model identification is addressed, and two methods for model identification are sketched.     

GIS and geostatistics: Essential partners for spatial analysis

Initially, geographical information systems (GIS) concentrated on two issues: automated map making, and facilitating the comparison of data on thematic maps. The first required high quality graphics, vector data models and powerful data bases, the second is based on grid cells that can be manipulated by suites of mathematical operators collectively termed map algebra. Both kinds of GIS are widely available and are taught in many universities and technical colleges. After more than 20 years of development, most standard GIS provide both kinds of functionality and good quality graphic display, but until recently they have not included the methods of statistics and geostatistics as tools for spatial analysis. Recently, standard statistical packages have been linked to GIS for both exploratory data analysis and statistical analysis and hypothesis testing. Standard statistical packages include methods for the analysis of random samples of cases or objects that are not necessarily co-located in space—if the results of statistical analysis display a spatial pattern then that is because the underlying data also share that pattern. Geostatistics addresses the need to make predictions of sampled attributes (i.e., maps) at unsampled locations from sparse, often expensive data. To make up for lack of hard data geostatistics has concentrated on the development of powerful methods based on stochastic theory. Though there have been recent moves to incorporate ancillary data in geostatistical analyses, insufficient attention has been paid to using modern methods of data display for the visualization of results. GIS can serve geostatistics by aiding geo-registration of data, facilitating spatial exploratory data analysis, providing a spatial context for interpolation and conditional simulation, as well as providing easy-to-use and effective tools for data display and visualization. The value of geostatistics for GIS lies in the provision of reliable interpolation methods with known errors, methods of upscaling and generalization, and for supplying multiple realizations of spatial patterns that can be used in environmental modeling. These stochastic methods are improving understanding of how errors in models of spatial processes accrue from errors in data or incompleteness in the structure of the models. New developments in GIS, based on ideas taken from map algebra, cellular automata and image analysis are providing high level programming languages for modeling dynamic processes such as erosion or the development of alluvial fans and deltas. Research has demonstrated that these models need stochastic inputs to yield realistic results. Non-stochastic tools such as fuzzy subsets have been shown to be useful for spatial analysis when probabilistic approaches are inappropriate or impossible. The conclusion is that in spite of differences in history and approach, the linkage of GIS, statistics and geostatistics provides a powerful, and complementary suite of tools for spatial analysis in the agricultural, earth and environmental sciences.     

Setting Realistic Recovery Targets for Two Interacting Endangered Species,Sea Otter and Northern Abalone

Failure to account for interactions between endangered species may lead to unexpected population dynamics, inefficient management strategies, waste of scarce resources, and, at worst, increased extinction risk. The importance of species interactions is undisputed, yet recovery targets generally do not account for such interactions. This shortcoming is a consequence of species‐centered legislation, but also of uncertainty surrounding the dynamics of species interactions and the complexity of modeling such interactions. The northern sea otter (Enhydra lutris kenyoni) and one of its preferred prey, northern abalone (Haliotis kamtschatkana), are endangered species for which recovery strategies have been developed without consideration of their strong predator–prey interactions. Using simulation‐based optimization procedures from artificial intelligence, namely reinforcement learning and stochastic dynamic programming, we combined sea otter and northern abalone population models with functional‐response models and examined how different management actions affect population dynamics and the likelihood of achieving recovery targets for each species through time. Recovery targets for these interacting species were difficult to achieve simultaneously in the absence of management. Although sea otters were predicted to recover, achieving abalone recovery targets failed even when threats to abalone such as predation and poaching were reduced. A management strategy entailing a 50% reduction in the poaching of northern abalone was a minimum requirement to reach short‐term recovery goals for northern abalone when sea otters were present. Removing sea otters had a marginally positive effect on the abalone population but only when we assumed a functional response with strong predation pressure. Our optimization method could be applied more generally to any interacting threatened or invasive species for which there are multiple conservation objectives. Definición de Metas de Recuperación Realistas para Dos Especies en Peligro Interactuantes, Enhydra lutris y Haliotis kamtschatkana     

Comparison of sensitivity analysis techniques: A case study with the rice model WARM

The considerable complexity often included in biophysical models leads to the need of specifying a large number of parameters and inputs, which are available with various levels of uncertainty. Also, models may behave counter-intuitively, particularly when there are nonlinearities in multiple input-output relationships. Quantitative knowledge of the sensitivity of models to changes in their parameters is hence a prerequisite for operational use of models. This can be achieved using sensitivity analysis (SA) via methods which differ for specific characteristics, including computational resources required to perform the analysis. Running SA on biophysical models across several contexts requires flexible and computationally efficient SA approaches, which must be able to account also for possible interactions among parameters. A number of SA experiments were performed on a crop model for the simulation of rice growth (Water Accounting Rice Model, WARM) in Northern Italy. SAs were carried out using the Morris method, three regression-based methods (Latin hypercube sampling, random and Quasi-Random, LpTau), and two methods based on variance decomposition: Extended Fourier Amplitude Sensitivity Test (E-FAST) and Sobol’, with the latter adopted as benchmark. Aboveground biomass at physiological maturity was selected as reference output to facilitate the comparison of alternative SA methods. Rankings of crop parameters (from the most to the least relevant) were generated according to sensitivity experiments using different SA methods and alternate parameterizations for each method, and calculating the top-down coefficient of concordance (TDCC) as measure of agreement between rankings. With few exceptions, significant TDCC values were obtained both for different parameterizations within each method and for the comparison of each method to the Sobol’ one. The substantial stability observed in the rankings seem to indicate that, for a crop model of average complexity such as WARM, resource intensive SA methods could not be needed to identify most relevant parameters. In fact, the simplest among the SA methods used (i.e., Morris method) produced results comparable to those obtained by methods more computationally expensive.     

Optimization model for a wolf-ungulate system

Public outcries against predator control create a need to devise management policies that optimally balance the cost (managerial and environmental) of predator control against the benefit of ungulate harvesting. To address this problem, an optimization procedure utilizing stochastic dynamic programming is described. Through this approach, optimal feedback strategies for a wolf-ungulate system in Alaska are estimated. The dynamic predator-prey model used in the analysis is based on parameter estimates from data collected over an eight-year period in Denali (Mt. McKinley) National Park. Stability analysis of the system revealed that stability properties depend on predator search efficiency. The effects of random fluctuations in winter severity and alternative objective functions are considered in the estimation of optimal feedback strategies. Optimal moose harvesting strategies appear to be dependent on wolf control costs. If no wolf control cost is assessed, optimal moose harvest is independent of wolf density. Optimal wolf control strategies are completely insensitive to moose density. The strategies are compared to current and simplified management policies.     

Hard or soft environmental systems?

Recent trends in lake and stream water quality modeling indicate a conflict between the search for improved accuracy through increasing model size and complexity, and the search for applicability through simplification of already existing models. Much of this conflict turns on the fact that that which can be simulated in principle issimply not matched by that which can be observed and verified in practice. This paper is concerned with that conflict. Its aim is to introduce and clarify some of the arguments surrounding two issues of key importance in resolving the conflict: uncertainty in the mathematical relationships hypothesized for a particular model (calibration and model structure identification); and uncertainty associated with the predictions obtained from the model (prediction error analysis). These are issues concerning the reliability of models and model-based forecasts. The paper argues, in particular, that there is an intimate relationship between prediction and model calibration. This relationship is especially important in accounting for uncertainty in the development and use of models. Using this argument it is possible to state a dilemma which captures some limiting features of both large and small models.     

The short-run impact of environmental protection costs on U.S. product prices

Using an input-output framework, this paper investigates the magnitude of the impact that environmental protection costs had on prices in the United States in 1977. With the imposition of environmental protection costs, the projected total price increases for individual industries range from 0.12% to 6.58%. Due to data limitations, the constraints of input-output analysis, and the structural changes that occurred in the economy from 1972 to 1977, the results should not be taken as definitive. However, the results do indicate the typical sort of variation in impact likely to occur across industries and that the average impact on prices was modest.     

厌氧条件下土壤中有机氯降解动力学模型及其参数优化——以五氯酚（PCP）为例

研究厌氧条件下土壤中有机氯降解动力学模型及其参数优化方法,对阐明有机氯降解的动力学反应机制具有十分重要的理论意义。文章以厌氧条件下土壤中五氯酚（PCP）降解为例,归纳介绍了两类用于描述土壤中PCP降解动力学过程的模型：一类模型是不考虑微生物生长的基质降解模式,以一级动力学模型最有代表性,但这类模型多数情况下没有或没有完全体现环境因素对PCP降解的抑制作用;另一类模型是考虑微生物生长的基质降解模式,以Monod动力学模型使用最普遍,但这类模型只在微生物生长的指数期适用,对延迟期、稳定期和衰亡期需经扩展才可应用。logistic动力学模型可广泛用于以上两类模型,是可以近似地描述微生物经适应过程和共代谢作用而致有机氯降解的简单模型。文章还总结了有机氯降解非线性模型参数的优化方法,并对其优缺点进行了分析和比较。针对目前土壤中有机氯降解动力学模型及其参数优化研究中存在的问题,对今后的研究方向进行了展望。