The incorporation of stochasticity in risk analysis and management: a case study

 The selection of optimal management strategies for environmental contaminants requires detailed information on the risks imposed on populations. These risks are characterized by both inter-subject variability (different individuals having different levels of risk) and by uncertainty (there is uncertainty about the risk associated with the Yth percentile of the variability distribution). In addition, there is uncertainty introduced by the inability to agree fully on the appropriate decision criteria. This paper presents a methodology for incorporating uncertainty and variability into a multi-medium, multi-pathway, multi-contaminant risk assessment, and for placing this assessment into an optimization framework to identify optimal management strategies. The framework is applied to a case study of a sludge management system proposed for North Carolina and the impact of stochasticity on selection of an optimal strategy considered. Different sets of decision criteria reflecting different ways of treating stochasticity are shown to lead to different selections of optimal management strategies.     

Decision Support Modeling: Data Assimilation,Uncertainty Quantification,and Strategic Abstraction

We present a framework for design and deployment of decision support modeling based on metrics which have their roots in the scientific method. Application of these metrics to decision support modeling requires recognition of the importance of data assimilation and predictive uncertainty quantification in this type of modeling. The difficulties of implementing these procedures depend on the relationship between data that is available for assimilation and the nature of the prediction(s) that a decision support model is required to make. Three different data/prediction contexts are identified. Unfortunately, groundwater modeling is generally aligned with the most difficult of these. It is suggested that these difficulties can generally be ameliorated through appropriate model design. This design requires strategic abstraction of parameters and processes in a way that is optimal for the making of one particular prediction but is not necessarily optimal for the making of another. It is further suggested that the focus of decision support modeling should be on the ability of a model to provide receptacles for decision-pertinent information rather than on its purported ability to simulate environmental processes. While models are compromised in both of these roles, this view makes it clear that simulation should serve data assimilation and not the other way around. Data assimilation enables the uncertainties of decision-critical model predictions to be quantified and maybe reduced. Decision support modeling requires this.     

An inexact fuzzy-chance-constrained two-stage mixed-integer linear programming approach for flood diversion planning under multiple uncertainties

In this study, an inexact fuzzy-chance-constrained two-stage mixed-integer linear programming (IFCTIP) approach is developed for flood diversion planning under multiple uncertainties. A concept of the distribution with fuzzy boundary interval probability is defined to address multiple uncertainties expressed as integration of intervals, fuzzy sets and probability distributions. IFCTIP integrates the inexact programming, two-stage stochastic programming, integer programming and fuzzy-stochastic programming within a general optimization framework. IFCTIP incorporates the pre-regulated water-diversion policies directly into its optimization process to analyze various policy scenarios; each scenario has different economic penalty when the promised targets are violated. More importantly, it can facilitate dynamic programming for decisions of capacity-expansion planning under fuzzy-stochastic conditions. IFCTIP is applied to a flood management system. Solutions from IFCTIP provide desired flood diversion plans with a minimized system cost and a maximized safety level. The results indicate that reasonable solutions are generated for objective function values and decision variables, thus a number of decision alternatives can be generated under different levels of flood flows.     

Evolutionary aseismic design and retrofit of structures with passive energy dissipation

A new computational framework is developed for the design and retrofit of building structures by considering aseismic design as a complex adaptive process. For the initial phase of the development within this framework, genetic algorithms are employed for the discrete optimization of passively damped structural systems. The passive elements may include metallic plate dampers, viscous fluid dampers and viscoelastic solid dampers. The primary objective is to determine robust designs, including both the non‐linearity of the structural system and the uncertainty of the seismic environment. Within the present paper, this computational design approach is applied to a series of model problems, involving sizing and placement of passive dampers for energy dissipation. In order to facilitate our investigations and provide a baseline for further study, we introduce several simplifications for these initial examples. In particular, we employ deterministic lumped parameter structural models, memoryless fitness function definitions and hypothetical seismic environments. Despite these restrictions, some interesting results are obtained from the simulations and we are able to gain an understanding of the potential for the proposed evolutionary aseismic design methodology. Copyright © 2005 John Wiley & Sons, Ltd.     

Benefits of the combined use of stochastic multi-criteria evaluation with principal components analysis

Multi-criteria analysis techniques are well known decision support methods and are widely applied in various disciplines. However, defining the input criteria values for the basic decision matrix which contains all criteria values for every alternative considered is normally not an easy task. Especially qualitative criteria variables which are frequently represented as linguistic terms may be hard to quantify. Moreover, some criteria cannot be represented by just one crisp value, but they may offer a range of possible values. Stochastic multi-criteria approaches which call for distribution models instead of single numerical values can be used in these cases. Outranking multi-criteria methods proved that simulation based stochastic techniques are well suited to give better insight into the preference structure of a variety of decision alternatives. However, besides the knowledge of the preference structure, it is also important to find out about the similarity of decision alternatives which allows a modeller to categorize a decision alternative as a really unique option or as just one option out of a greater subset of very similar alternatives. To be able to perform this categorization, principal components analysis (PCA) was used. The results of the PCA are compared to the results of a stochastic outranking analysis.     

A diagnostic assessment of evolutionary algorithms for multi-objective surface water reservoir control

Globally, the pressures of expanding populations, climate change, and increased energy demands are motivating significant investments in re-operationalizing existing reservoirs or designing operating policies for new ones. These challenges require an understanding of the tradeoffs that emerge across the complex suite of multi-sector demands in river basin systems. This study benchmarks our current capabilities to use Evolutionary Multi-Objective Direct Policy Search (EMODPS), a decision analytic framework in which reservoirs’ candidate operating policies are represented using parameterized global approximators (e.g., radial basis functions) then those parameterized functions are optimized using multi-objective evolutionary algorithms to discover the Pareto approximate operating policies. We contribute a comprehensive diagnostic assessment of modern MOEAs’ abilities to support EMODPS using the Conowingo reservoir in the Lower Susquehanna River Basin, Pennsylvania, USA. Our diagnostic results highlight that EMODPS can be very challenging for some modern MOEAs and that epsilon dominance, time-continuation, and auto-adaptive search are helpful for attaining high levels of performance. The ϵ-MOEA, the auto-adaptive Borg MOEA, and ϵ-NSGAII all yielded superior results for the six-objective Lower Susquehanna benchmarking test case. The top algorithms show low sensitivity to different MOEA parameterization choices and high algorithmic reliability in attaining consistent results for different random MOEA trials. Overall, EMODPS poses a promising method for discovering key reservoir management tradeoffs; however algorithmic choice remains a key concern for problems of increasing complexity.     

Water resources management under dual uncertainties: a factorial fuzzy two-stage stochastic programming approach

In this study we propose a factorial fuzzy two-stage stochastic programming (FFTSP) approach to support water resources management under dual uncertainties. The dual uncertainties in terms of fuzziness in modeling parameters and variability of α-cut levels are taken into account. As different α-cut levels are assigned to each fuzzy parameter (instead of an identical α-cut level), the effects of α-cut levels on fuzzy parameters can be considered. Factorial analysis method is integrated with fuzzy vertex method to tackle the interactive effects of fuzzy parameters within a two-stage stochastic programming framework. The effects of the interactions among fuzzy parameters under various α-cut level combinations can be examined. The FFTSP approach is applied to a water resources management case to demonstrate its applicability. The results show that this approach can not only give various optimized solutions according to decision makers’ confidence levels but also provide in-depth analyses for the effects of fuzzy parameters and their interactions on the solutions. In addition, the results show that the effects of diverse α-cut combinations should not be disregarded because the results may differ under some specific α-cut combinations. The dual sequential factorial analyses embedded in the FFTSP approach guarantee most variations in a system can be analyzed. Therefore water managers are able to gain sufficient knowledge to make robust decisions under uncertainty.     

Two-stage decision-making under uncertainty and stochasticity: Bayesian Programming

This paper develops a new method for decision-making under uncertainty. The method, Bayesian Programming (BP), addresses a class of two-stage decision problems with features that are common in environmental and water resources. BP is applicable to two-stage combinatorial problems characterized by uncertainty in unobservable parameters, only some of which is resolved upon observation of the outcome of the first-stage decision. The framework also naturally accommodates stochastic behavior, which has the effect of impeding uncertainty resolution. With the incorporation of systematic methods for decision search and Monte Carlo methods for Bayesian analysis, BP addresses limitations of other decision-analytic approaches for this class of problems, including conventional decision tree analysis and stochastic programming. The methodology is demonstrated with an illustrative problem of water quality pollution control. Its effectiveness for this problem is compared to alternative approaches, including a single-stage model in which expected costs are minimized and a deterministic model in which uncertain parameters are replaced by their mean values. A new term, the expected value of including uncertainty resolution, or EVIUR, is introduced and evaluated for the illustrative problem. It is a measure of the worth of incorporating the experimental value of decisions into an optimal decision-making framework. For the illustrative problem, the two-stage adaptive management framework extracted up to approximately 50% of the gains of perfect information. The strength and limitations of the method are discussed and conclusions are presented.     

Two-stage fuzzy chance-constrained programming: application to water resources management under dual uncertainties

In this study, a two-stage fuzzy chance-constrained programming (TFCCP) approach is developed for water resources management under dual uncertainties. The concept of distribution with fuzzy probability (DFP) is presented as an extended form for expressing uncertainties. It is expressed as dual uncertainties with both stochastic and fuzzy characteristics. As an improvement upon the conventional inexact linear programming for handling uncertainties in the objective function and constraints, TFCCP has advantages in uncertainty reflection and policy analysis, especially when the input parameters are provided as fuzzy sets, probability distributions and DFPs. TFCCP integrates the two-stage stochastic programming (TSP) and fuzzy chance-constrained programming within a general optimization framework. TFCCP incorporates the pre-regulated water resources management policies directly into its optimization process to analyze various policy scenarios; each scenario has different economic penalty when the promised amounts are not delivered. TFCCP is applied to a water resources management system with three users. Solutions from TFCCP provide desired water allocation patterns, which maximize both the system’s benefits and feasibility. The results indicate that reasonable solutions were generated for objective function values and decision variables, thus a number of decision alternatives can be generated under different levels of stream flows, α-cut levels and fuzzy dominance indices.     

地震应急对策决策支持软件的设计与开发

基于空间决策支持技术,设计与开发了地震应急对策软件,在软件的总体设计、功能模块、数据库建设、地震应急对策模型及接口设计与模块集成等方面进行了研究与开发,并在“青岛市地震应急指挥决策支持软件系统”中得到实际应用和检验。实践证明,该软件能够实现城市地震灾害信息的科学管理,智能制定各种地震应急对策和生动的可视化,从而有效提高地震应急的效率和响应速度,为城市地震应急工作提供了有效的辅助决策手段;建成的“青岛市地震应急对策系统”对全国大中城市地震应急指挥技术系统的建设具有示范意义。     

道路网络抗震功能失效空间决策支持方法研究

地震作用后桥梁的破坏是导致道路网络功能失效的主要原因。在进行道路网络抗震功能失效分析这一决策过程中,不仅要应用空间分析模型对空间数据进行分析,而且也要对属性数据进行有效的决策分析,因此本文结合了G IS(地理信息系统)与DSS(决策支持系统),形成了以模型库为驱动核心的道路网络抗震功能失效分析空间决策支持,本文着重对数据库及模型库的构建进行了深入研究。     

Contaminant remediation decision analysis using information gap theory

Decision making under severe lack of information is a ubiquitous situation in nearly every applied field of engineering, policy, and science. A severe lack of information precludes our ability to determine a frequency of occurrence of events or conditions that impact the decision; therefore, decision uncertainties due to a severe lack of information cannot be characterized probabilistically. To circumvent this problem, information gap (info-gap) theory has been developed to explicitly recognize and quantify the implications of a severe lack of information in decision making. This paper presents a decision analysis based on info-gap theory developed for a contaminant remediation scenario. The analysis provides decision support in determining the fraction of contaminant mass to remove from the environment. An info-gap uncertainty model is developed to characterize uncertainty due to a lack of information concerning the contaminant flux. The info-gap uncertainty model groups nested, convex sets of functions defining contaminant flux over time based on their level of deviation from a nominal contaminant flux. The nominal contaminant flux defines a best estimate of contaminant flux over time based on existing, though incomplete, information. A robustness function is derived to quantify the maximum level of deviation from nominal that still ensures compliance for alternative decisions. An opportuneness function is derived to characterize the possibility of meeting a desired contaminant concentration level. The decision analysis evaluates how the robustness and opportuneness change as a function of time since remediation and as a function of the fraction of contaminant mass removed.     

地震自动速报信息在桥梁监测警报系统中的应用

介绍地震自动速报信息系统与桥梁监测警报系统的构成,通过将地震自动速报技术与强震动观测技术相结合,形成一套既有强震动观测功能又能接收处理即时地震信息的桥梁监测报警系统,应用结果显示,能丰富强震动观测系统的功能,为桥梁管理提供更多决策支持.     

Estimating landslide susceptibility areas considering the uncertainty inherent in modeling methods

Landslides are one of the most dangerous types of natural disasters, and damage due to landslides has been increasing in certain regions of the world because of increased precipitation. Policy decision makers require reliable information that can be used to establish spatial adaptation plans to protect people from landslide hazards. Researchers presently identify areas susceptible to landslides using various spatial distribution models. However, such data are associated with a high amount of uncertainty. This study focuses on quantifying the uncertainty of several spatial distribution models and identifying the effectiveness of various ensemble methods that can be used to provide reliable information to support policy decisions. The area of study was Inje-gun, Republic of Korea. Ten models were selected to assess landslide susceptibility. Moreover, five ensemble methods were selected for the aggregated results of the 10 models. The uncertainty was quantified using the coefficient of variation and the uncertainty map we developed revealed areas with strongly differing values among single models. A matrix map was created using an ensemble map and a coefficient of variation map. Using matrix analysis, we identified the areas that are most susceptible to landslides according to the ensemble model with a low uncertainty. Thus, the ensemble model can be a useful tool for supporting decision makers. The framework of this study can also be employed to support the establishment of landslide adaptation plans in other areas of the Republic of Korea and in other countries.     

Improving long-range hydrological forecasts with extended Kalman filters

Abstract

There is a continuing effort to advance the skill of long-range hydrological forecasts to support water resources decision making. The present study investigates the potential of an extended Kalman filter approach to perform supervised training of a recurrent multilayer perceptron (RMLP) to forecast up to 12-month-ahead lake water levels and streamflows in Canada. The performance of the RMLP was compared with the conventional multilayer perceptron (MLP) using suites of diagnostic measures. The results of the forecasting experiment showed that the RMLP model was able to provide a robust modelling framework capable of describing complex dynamics of the hydrological processes, thereby yielding more accurate and realistic forecasts than the MLP model. The performance of the method in the present study is very promising; however, further investigation is required to ascertain the versatility of the approach in characterizing different water resources and environmental problems.

Citation Muluye, G. Y. (2011) Improving long-range hydrological forecasts with extended Kalman filters. Hydrol. Sci. J. 56(7), 1118–1128.     

Decision support for integrated river basin management—Scientific research challenges

Integrated River Basin Management(IRBM)has been a long discussed way to sustainably manage water and land resources;yet,very few examples of effective IRBM are found because there is a lack of sufficient scientific support,as well as institutional accommodation,to successfully implement it.This paper overviews the major challenges with IRBM,the promising scientific approaches for the implementation of IRBM,and the areas of needed research,with considerable issues and experiences from China.It is expected that novel research will draw together disparate disciplines into an integrated scientific framework,upon which better modeling tools,stakeholder involvement,and decision-making support can be built.Cutting-edge new technologies will bring ideas of IRBM forward to theory and finally to practice.The paper will prompt scientists to undertake research to fill in the gaps in the current IRBM knowledge base and provide practitioners guidance on how to incorporate scientifically based information within the IRBM decision process.     

Multiple hazard incidents lifecycle cost assessment of structural systems considering state‐dependent repair times and fragility curves

The performance and serviceability of structural systems during their lifetime can be significantly affected by the occurrence of extreme events. Despite their low probability, there is a potential for multiple occurrences of such hazards during the relatively long service life of systems. This paper introduces a comprehensive framework for the assessment of lifecycle cost of infrastructures subject to multiple hazard events throughout their decision‐making time horizon. The framework entails the lifecycle costs of maintenance and repair, as well as the salvage value of the structure at the end of the decision‐making time horizon. The primary features of the proposed framework include accounting for the possibility of multiple hazard occurrences, incorporating effects of incomplete repair actions on the accumulated damage through damage state‐dependent repair times, and requiring limited resources in terms of input data and computational costs. A dynamic programming procedure is proposed to calculate the expected damage condition of the structure for each possibility of the number of hazard incidents based on state‐dependent fragility curves. The proposed framework is applied to a moment‐frame building located in a region with high seismicity, and lifecycle costs are evaluated for six retrofit plans. The results displayed variation in the ranking of the retrofit actions with respect to decision‐making time horizon. Furthermore, the sensitivity analyses demonstrated that disregarding repair time in the lifecycle cost analysis can result in false identification of unsafe retrofit actions as optimal and reliable strategies. Copyright © 2016 John Wiley & Sons, Ltd.     

An Elitist Multiobjective Tabu Search for Optimal Design of Groundwater Remediation Systems

This study presents a new multiobjective evolutionary algorithm (MOEA), the elitist multiobjective tabu search (EMOTS), and incorporates it with MODFLOW/MT3DMS to develop a groundwater simulation‐optimization (SO) framework based on modular design for optimal design of groundwater remediation systems using pump‐and‐treat (PAT) technique. The most notable improvement of EMOTS over the original multiple objective tabu search (MOTS) lies in the elitist strategy, selection strategy, and neighborhood move rule. The elitist strategy is to maintain all nondominated solutions within later search process for better converging to the true Pareto front. The elitism‐based selection operator is modified to choose two most remote solutions from current candidate list as seed solutions to increase the diversity of searching space. Moreover, neighborhood solutions are uniformly generated using the Latin hypercube sampling (LHS) in the bounded neighborhood space around each seed solution. To demonstrate the performance of the EMOTS, we consider a synthetic groundwater remediation example. Problem formulations consist of two objective functions with continuous decision variables of pumping rates while meeting water quality requirements. Especially, sensitivity analysis is evaluated through the synthetic case for determination of optimal combination of the heuristic parameters. Furthermore, the EMOTS is successfully applied to evaluate remediation options at the field site of the Massachusetts Military Reservation (MMR) in Cape Cod, Massachusetts. With both the hypothetical and the large‐scale field remediation sites, the EMOTS‐based SO framework is demonstrated to outperform the original MOTS in achieving the performance metrics of optimality and diversity of nondominated frontiers with desirable stability and robustness.     

Visual analytics clarify the scalability and effectiveness of massively parallel many-objective optimization: A groundwater monitoring design example

In this study, we contribute a comprehensive framework for simultaneously assessing solution quality and scalability for massively parallel multiobjective evolutionary algorithm (MOEA)-based search using a highly challenging optimization—assimilation application. Visual analytics are used to evaluate how changes in search metric performance relate to actual decision relevant changes in the Pareto approximate set. The application focuses on a four objective groundwater monitoring application in which parallel scalability is tested across compute core counts ranging from 64 to a maximum of 8192. This study demonstrates that parallel search performance must be assessed in terms of how well speedup is exploited to improve the quality of search results and that solely focusing on differences in computational time can be deceptive. Our results demonstrate how visualization can clarify when an MOEA’s search shifts from “translating” the approximation set to “diversifying” its coverage over the extent of the objectives. This is an important observation. If shorter parallel run durations are required, the rapid early translation of the set may yield a reasonable approximation of the Pareto approximate set where further search is unnecessary. Although a groundwater application is used to demonstrate our parallelization, the visual analytics and metrics utilized to characterize the parallel scalability of MOEA-based search are broadly applicable in water resources and beyond.     

对国家地震应急小型移动指挥协同平台建设的探讨

本文阐述了国务院抗震救灾指挥部地震应急小型移动指挥协同平台建设的理论依据及必要性;介绍了平台设计、平台体系结构和建设内容;结合大震时的使用情况,总结了国务院抗震救灾指挥部地震应急小型移动指挥协同平台,在全方位、全天候地震应急决策支持信息服务保障方面发挥的作用及实践经验,并对我国今后的地震应急决策支持信息服务体系建设做了初步思考和探讨。