From qualitative to quantitative environmental scenarios: Translating storylines into biophysical modeling inputs at the watershed scale

Scenarios are increasingly used for envisioning future social-ecological changes and consequences for human well-being. One approach integrates qualitative storylines and biophysical models to explore potential futures quantitatively and maximize public engagement. However, this integration process is challenging and sometimes oversimplified. Using the Yahara Watershed (Wisconsin, USA) as a case study, we present a transparent and reproducible roadmap to develop spatiotemporally explicit biophysical inputs [climate, land use/cover (LULC), and nutrients] that are consistent with scenario narratives and can be linked to a process-based biophysical modeling suite to simulate long-term dynamics of a watershed and a range of ecosystem services. Our transferrable approach produces daily weather inputs by combining climate model projections and a stochastic weather generator, annual narrative-based watershed-scale LULC distributed spatially using transition rules, and annual manure and fertilizer (nitrogen and phosphorus) inputs based on current farm and livestock data that are consistent with each scenario narrative.     

Spatial sensitivity analysis of multi-criteria weights in GIS-based land suitability evaluation

With growing interest in extending GIS to support multi-criteria decision-making (MCDM) methods, enhancing GIS-based MCDM with sensitivity analysis (SA) procedures is crucial to understand the model behavior and its limitations. This paper presents a novel approach of examining multi-criteria weight sensitivity of a GIS-based MCDM model. It explores the dependency of model output on the weights of input parameters, identifying criteria that are especially sensitive to weight changes and to show the impacts of changing criteria weights on the model outcomes in spatial dimension. A methodology was developed to perform simulations where the weights associated with all criteria used for suitability modelling were varied one-at-a-time (OAT) to investigate their relative impacts on the final evaluation results. A tool which incorporates the OAT method with the Analytical Hierarchy Process (AHP) within the ArcGIS environment was implemented. It permits a range of user defined simulations to be performed to quantitatively evaluate model dynamic changes, measures the stability of results with respect to the variation of different parameter weights, and displays spatial change dynamics. A case study of irrigated cropland suitability assessment addressing the application of the new GIS-based AHP-SA tool is described. It demonstrates that the tool is spatial, simple and flexible.     

The spatial framework for weight sensitivity analysis in AHP-based multi-criteria decision making

Criteria weights determined from pairwise comparisons are often the greatest contributor to the uncertainties in the AHP-based multi-criteria decision making (MCDM). During an MCDM process, the weights can be changed directly by adjusting the output from a pairwise comparison matrix, or indirectly by recalculating the matrix after varying its input. Corresponding weight sensitivity on multi-criteria evaluation results is generally difficult to be quantitatively assessed and spatially visualized. This study developed a unique methodology which extends the AHP-SA model proposed by Chen et al. (2010) to a more comprehensive framework to analyze weight sensitivity caused by both direct and indirect weight changes using the one-at-a-time (OAT) technique. With increased efficiency, improved flexibility and enhanced visualization capability, the spatial framework was developed as AHP-SA2 within a GIS platform. A case study with in-depth discussion is provided to demonstrate the new toolset. It assists stakeholders and researchers with better understanding of weight sensitivity for characterising, reporting and minimising uncertainty in the AHP-based spatial MCDM.     

Fuzzy logic based decision support for mass evacuations of cities prone to coastal or river floods

Facing climate change and more frequent extreme weather conditions, coastal floods and inundations will become more severe. Evacuation can be an efficient solution to secure people's safety in a major disaster. The main difficulty in making an evacuation decision is the imprecise, incomplete and spatially varying nature of the crisis information. In this paper, a fuzzy-logic based method combined with Geographic Information System is proposed to analyze evacuation decision making scenarios. The method can handle qualitative and quantitative data at the same time, avoid sudden changes of decisions affected by uncertainties, and evaluate the spatial necessity to evacuate to support evacuation decision making. The method has been tested at the city of Bordeaux in France. The maps produced representing the need to evacuate can help decision makers better understand evacuation decision situation in terms of local impacts and crisis management anticipation.     

Composite decision support by combining cost-benefit and multi-criteria decision analysis

This paper concerns composite decision support based on combining cost-benefit analysis (CBA) with multi-criteria decision analysis (MCDA) for the assessment of economic as well as strategic impacts within transport projects. Specifically a composite model for assessment (COSIMA) is presented as a decision support system (DSS). This COSIMA DSS ensures that the assessment is conducted in a systematic, transparent and explicit way. The modelling principles presented are illuminated with a case study concerning a complex decision problem. The outcome demonstrates the approach as a valuable DSS, and it is concluded that appraisals of large transport projects can be effectively supported using a combination of CBA and MCDA. Finally, perspectives of the future modelling work are given.     

Modeling land use decisions with Bayesian networks: Spatially explicit analysis of driving forces on land use change

Land use decisions result from complex deliberative processes and fundamentally influence the livelihoods of many. These decisions are made based on quantitatively measurable information like topography and on qualitative criteria such as personal preferences. Bayesian networks (BN) are able to integrate both quantitative and qualitative data and are thus suitable to approach such processes. We model land use decisions in a pre-Alpine area in Switzerland, integrating biophysical data and local actors' knowledge into a spatially explicit BN. A structured experts' process to elaborate three different BN including agriculture, forestry, and settlement provides the base for the modeling. A spatially explicit updating of the BN via questionnaires enables us to take local actors' characteristics into account. Results show which drivers are most important for land use decision-making in our case study region, and how an alteration of these drivers could change future land use. Furthermore, focusing on the probability of occurrence of various land uses in a spatially explicit manner gives insights into path-dependency of land use change. This knowledge can serve as information for planners and policy makers to design more effective policy instruments.     

Design of decision model for sensitive crop irrigation system

Agriculture Industry is highly dependent on environmental and weather conditions. Many times, crops are spoiled because of sudden changes in weather. Therefore, we need a decision model to take care the water requirement of sensitive crops of agriculture industry. The proposed work presents a novel and proficient hybrid model for sensitive crop irrigation system (SCIS). For implementation of the model, brassica crop is taken. The duration and amount of water to be supplied is based upon the weather prediction and soil condition information. The decision model is developed using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) for brassica crops. In this model, if the input data values are available in range, then ANFIS model would be preferred and if the data sets are available for training, testing and validation then ANN model would be the best choice. The soil moisture, soil status in terms of temperature and leaf wetness are the input and flow control of sprinklers is the out for SCIS. The predicted outputs are analysed to assert the suitability of the proposed approach in the brassica crops. The proposed SCIS achieved an accuracy of 91% and 99% for ANFIS and ANN models respectively.     

Implications of complexity and uncertainty for integrated modelling and impact assessment in river basins

The paper focuses on implications of complexity and uncertainty in climate change impact assessment at the river basin and regional scales. The study was performed using the process-based ecohydrological spatially semi distributed model SWIM (Soil and Water Integrated Model). The model integrates hydrological processes, vegetation/crop growth, erosion and nutrient dynamics in river basins. It was developed from the SWAT and MATSALU models for climate and land use change impact assessment. The study area is the German part of the Elbe River basin (about 100,000 km²). It is representative for semi-humid landscapes in Europe, where water availability during the summer season is the limiting factor for plant growth and crop yield. The validation method followed the multi-scale, multi-site and multi-criteria approach and enabled to reproduce (a) water discharge and nutrient load at the river outlet along with (b) local ecohydrological processes like water table dynamics in subbasins, nutrient fluxes and vegetation growth dynamics at multiple scales and sites. The uncertainty of climate impacts was evaluated using comprehensive Monte Carlo simulation experiments.     

Locally Implicit Time Integration Strategies in a Discontinuous Galerkin Method for Maxwell’s Equations

An attractive feature of discontinuous Galerkin (DG) spatial discretization is the possibility of using locally refined space grids to handle geometrical details. However, locally refined meshes lead to severe stability constraints on explicit integration methods to numerically solve a time-dependent partial differential equation. If the region of refinement is small relative to the computational domain, the time step size restriction can be overcome by blending an implicit and an explicit scheme where only the solution variables living at fine elements are treated implicitly. The downside of this approach is having to solve a linear system per time step. But due to the assumed small region of refinement relative to the computational domain, the overhead will also be small while the solution can be advanced in time with step sizes determined by the coarse elements. In this paper, we present two locally implicit time integration methods for solving the time-domain Maxwell equations spatially discretized with a DG method. Numerical experiments for two-dimensional problems illustrate the theory and the usefulness of the implicit–explicit approaches in presence of local refinements.     

Comparative analysis of relevance feedback methods based on two user studies

Rigorous analysis of user interest in web documents is essential for the development of recommender systems. This paper investigates the relationship between the implicit parameters and user explicit rating during their search and reading tasks. The objective of this paper is therefore three-fold: firstly, the paper identifies the implicit parameters which are statistically correlated with the user explicit rating through user study 1. These parameters are used to develop a predictive model which can be used to represent users’ perceived relevance of documents. Secondly, it investigates the reliability and validity of the predictive model by comparing it with eye gaze during a reading task through user study 2. Our findings suggest that there is no significant difference between the predictive model based on implicit indicators and eye gaze within the context examined. Thirdly, we measured the consistency of user explicit rating in both studies and found significant consistency in user explicit rating of document relevance and interest level which further validates the predictive model. We envisage that the results presented in this paper can help to develop recommender and personalised systems for recommending documents to users based on their previous interaction with the system.     

A novel group decision model based on mean–variance–skewness concepts and interval-valued fuzzy sets for a selection problem of the sustainable warehouse location under uncertainty

Recently, sustainable warehouse location has been regarded as one of the most critical and significant decision problems for long-term planning in the supply chain. This strategic decision can be effected by different quantitative and qualitative evaluation criteria via three dimensions of the sustainability. Main theme of the paper is to select the most optimal location decision from a number of potential sustainable warehouse candidates. For this purpose, this paper presents a novel multi-criteria decision-making model by a group of supply chain experts or decision makers with interval-valued fuzzy setting and asymmetric uncertainty information. Concepts of mean, variance and skewness are introduced into the proposed group decision model, and their mathematical relations are defined based on a fuzzy possibilistic statistical approach. Then, new relations in this model are presented for obtaining ideal solutions under uncertainty with two high and low values of the possibilistic mean and possibilistic standard deviation, along with the possibilistic cube root of skewness. In addition, novel separation measures and new fuzzy ranking index of hybridized relative closeness coefficients are presented to provide final preference order of warehouse location candidates under uncertain conditions. Finally, a sustainable warehouse location selection problem in a pharmaceutical company is presented and solved by the proposed group decision model to demonstrate its applicability and suitability.

     

Rough set based approach for inducing decision trees

This paper presents a new approach for inducing decision trees based on Variable Precision Rough Set Model. The presented approach is aimed at handling uncertain information during the process of inducing decision trees and generalizes the rough set based approach to decision tree construction by allowing some extent misclassification when classifying objects. In the paper, two concepts, i.e. variable precision explicit region, variable precision implicit region, and the process for inducing decision trees are introduced. The authors discuss the differences between the rough set based approaches and the fundamental entropy based method. The comparison between the presented approach and the rough set based approach and the fundamental entropy based method on some data sets from the UCI Machine Learning Repository is also reported.     

习题内外表示异质融合的知识追踪模型

现有知识追踪研究大多使用习题蕴涵的知识点等内隐信息或历史交互数据等外显信息建模习题表示,没有注意到内外信息的异质性特征,缺乏对习题内外信息的异质融合。针对上述问题,提出了融合内外异质信息的知识追踪模型。首先,基于知识点等内隐信息,计算历史知识点与当前知识点之间的相关程度,刻画历史知识点对当前知识点的影响,建模习题的内隐表示;其次,基于交互数据等外显信息,计算历史习题与当前习题之间的相关程度,获取历史习题对当前习题的影响,建模习题的外显表示;再次,基于上述习题的内外表示,使用通道注意力机制融合得到习题的内外异质表示,从而预测学习者的作答表现。为了验证提出模型的性能和有效性,选取了四个相关的基线模型,在三个真实数据集上进行了对比实验。实验结果表明：在性能方面,提出的模型在多个评价指标上均取得较好的效果;在有效性方面,消融实验证明了提出的模型可以更好地根据内外信息建模习题表示;在应用方面,设计智慧学习环境证明了提出的模型在实际教学场景中的可用性。     

Multi-criteria decision-making methods with optimism and pessimism based on Atanassov's intuitionistic fuzzy sets

The theory of Atanassov's intuitionistic fuzzy sets (A-IFSs) developed over the last several decades has found useful application in fields requiring multiple-criteria decision analysis. Since the membership–nonmembership pair in A-IFSs belongs to the bivariate unipolarity type, this article describes an approach that relates optimism and pessimism to multi-criteria decision analysis in an intuitionistic fuzzy-decision environment. First, several optimistic and pessimistic point operators were defined to alter the estimation of decision outcomes. Next, based on the core of the estimations, optimistic and pessimistic score functions were developed to evaluate each alternative with respect to each criterion. The suitability function was then established to determine the degree to which each an alternative satisfies the decision maker's requirement. Because the information on multiple criteria corresponding to decision importance is often incomplete, this study included suitability functions in the optimisation models to account for poorly known membership grades. Using a linear equal-weighted summation method, these models were transformed into a single objective optimisation model to generate the optimal weights for criteria. The feasibility and effectiveness of the proposed methods were illustrated through a practical example. Finally, computational experiments with enormous amounts of simulation data were designed to conduct a comparative analysis on the ranking orders yielded by different optimistic/pessimistic point operators.     

三角模糊数型多准则群决策的VIKOR扩展方法

运用VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR)方法研究模糊多准则群决策问题常常将其分成模糊信息集结和VIKOR方法求解两个阶段。个体评估信息集结方法不同,所得到的群体集结结果也不同,获得的妥协解可能会存在较大差异。鉴于此,基于含有三角模糊数的多准则群决策问题,分析现有两种主流群体信息集结方法存在的缺陷,基于个体评估值与群体评估值的距离最优和较高的相似度两个目标,设计群体信息集结优化模型,提出一种拓展的VIKOR方法。最后通过实例分析说明了所提出方法的有效性和可行性。     

Integrating spatial planning and flood risk management: A new conceptual framework for the spatially integrated policy infrastructure

Flooding is a widely occurring natural hazard that noticeably damages property, people, and the environment. In the context of climate change, the integration of spatial planning with flood-risk management has gained prominence as an approach to mitigating the risks of flooding. The absence of easy access to integrated and high-quality information, and the technologies and tools to use information are among the factors that impede this integration. Limited research has been conducted to develop a framework and to investigate the role of information and technologies in this integration. This study draws primarily on the European experiences and literature and identifies three dimensions of the integration of spatial planning with flood-risk management: territorial, policy, and institutional. To facilitate integration and in accord with these three dimensions, a Spatially Integrated Policy Infrastructure (SIPI) is conceptualised that encompasses data and information, decision support and analysis tools, and access tools and protocols. This study presents the connections between SIPI elements and integration dimensions, which is important for a better understanding of roles of geographic information and technologies in integration. The conceptual framework of SIPI will govern further development and evaluation of SIPI.     

IA-SDSS: A GIS-based land use decision support system with consideration of carbon sequestration

Land use, land use change and forestry (LULUCF) can play a positive role in mitigating global warming by sequestering carbon from the atmosphere into vegetation and soils. Local entities (e.g. local government, community, stockholders) have been making great efforts in enhancing carbon sequestration (CS) of local forests for mitigating global climate change and participating in international carbon-trade promoted by the Kyoto Protocol. Approaches and tools are needed to assess the enhancement of CS through land use changes and proper policy decisions. This paper presents an integrated assessment framework and a spatial decision support system (IA-SDSS) as a tool to support land-use planning and local forestry development with consideration of CS. The IA-SDSS integrates two process-based carbon models, a spatial decision (EMDS) module, a spatial cost-benefit analysis (CBA) module, and the analytic hierarchy process (AHP) module. It can provide spatially explicit CS information as well as CS-induced economic benefits under various scenarios of the carbon credit market. A case study conducted in Liping County, Guizhou Province, China demonstrated that the IA-SDSS developed in this study is applicable in supporting decision-making on ‘where’ and ‘how’ to adopt forestry land use options in favor of CS.     

基于文本与社交信息的用户群组识别

社交媒体上的个人群体信息对于理解社交网络结构非常有用,现有研究主要基于用户之间的链接和显式社交信息识别用户的个人群体,很少考虑使用文本信息与隐含社交信息。但是隐含社交信息以及文本信息,在显式的社交信息缺乏时对于识别用户的群体是非常有帮助的。在本文中,我们提出一种隐含因子图模型有效地利用各种隐含与显式的社交与文本信息对用户的群组进行识别。其中,显式的文本与社交信息是通过用户发表的文本与个人关系生成的,同时,我们利用矩阵分解模型自动生成隐含的文本与社交信息。最后,我们利用因子图模型与置信传播算法对显式与隐含的文本与社交信息进行集成,并对用户群组识别模型进行学习与预测。实验证明我们的方法能有效地对用户群组进行识别。     

Multi-criteria selection of a deep-water port in the Eastern Baltic Sea

Sea ports play a significant role in the development of a modern economy. The Baltic Sea is an arterial transport corridor between Eastern and Western Europe. There is need to develop a deep-water sea port in the Klaipeda region to satisfy economic needs. This problem involves a multitude of requirements and uncertain conditions that have to be taken into consideration simultaneously. Numerous studies have been designated for the resolution of similar problems by employing multi-criteria as an aid. This paper proposes an integrated multi-criteria decision-making model to solve the problem. The backbone of the proposed model consists of a combination of Analytic Hierarchy (AHP) and Fuzzy Ratio Assessment (ARAS-F) methods. This model is presented as a form of decision aiding that could to be implemented when regarding any specific port or a like site selection.