Habitat prediction and knowledge extraction for spawning European grayling (Thymallus thymallus L.) using a broad range of species distribution models

This study aims to apply seven data-driven methods (i.e. artificial neural networks [ANNs], classification and regression trees [CARTs], fuzzy habitat suitability models [FHSMs], generalized additive models [GAMs], generalized linear models [GLMs], random forests [RF] and support vector machines [SVMs]) to develop data-driven species distribution models (SDMs) for spawning European grayling (Thymallus thymallus), and to compare the predictive performance and the ecological relevance, quantified by the habitat information retrieved from these SDMs (i.e. variable importance and habitat suitability curves [HSCs]). The results suggest RF to yield the most accurate SDM, followed by SVM, CART, ANN, GAM, FHSM and GLM. However, inconsistencies between different performance measures were observed, indicating that different models may obtain a high score on a particular aspect and perform worse on other aspects. Despite their lower predictive ability, GAM, GLM and FHSM proved to be useful, since HSCs could be obtained and thus these techniques allow testing of ecological relevance and habitat suitability. Water depth and flow velocity appeared to be important variables for spawning grayling. The HSCs clearly indicate higher habitat suitability at a lower water depth, a low to medium flow velocity and a higher percentage of medium-sized gravel, whereas the models disagreed on the habitat suitability for the percentage of small-sized gravel. These findings demonstrate the applicability of data-driven SDMs for both habitat prediction and ecological knowledge extraction that are useful for management of a target species.     

Evaluation and comparison of data-driven and knowledge-supported Bayesian Belief Networks to assess the habitat suitability for alien macroinvertebrates

Defining habitats vulnerable to invasion is important to support the management of invasive alien species (IAS). We developed and applied data-driven and knowledge-supported data-driven Bayesian Belief Networks (BBNs) to assess the habitat suitability for alien gammarids. Data-driven model development using a Naive Bayes classifier and equal width discretization resulted in a habitat suitability model with a moderate technical performance (CCI = 68% K = 0.33). Although the structure of the knowledge-supported model yielded important ecological insight between environmental and biotic variables and the occurrence of alien gammarids, the performance was lower (CCI = 60% K = 0.19) compared to the purely data-driven model. The lower predictive performance of the knowledge-supported model may be attributed to its higher model complexity. Our study shows that BBNs can support the management of IAS as they are visually appealing, transparent models that facilitate integration of monitoring data and expert knowledge.     

Model-based remotely-sensed imagery interpretation

Abstract

We present a model-based remotely-sensed image interpretation expert system embeded in a knowledge-based geographic information system (K. BIS). The KBIS consists of four sub-systems: a pictorial data base system, an image interpretation expert system, a computer-aided planning system and a computer-aided cartographic system. The image interpretation expert system represents ecological knowledge and other expert knowledge by frames. Its reasoning process consists of a forward reasoning based on the Bayes classification of Landsat imagery, a backward reasoning using frame knowledge and reasoning using a spatial consistency model. A forest inventory study was conducted in Shaxian county, in the southern part of China, using this expert system. The results have shown a significant improvement. Building image interpretation expert systems within knowledge-based pictorial systems is very convenient and efficient because there are well-organized data, knowledge and procedures available.     

Elicitator: An expert elicitation tool for regression in ecology

Expert elicitation is the process of retrieving and quantifying expert knowledge in a particular domain. Such information is of particular value when the empirical data is expensive, limited or unreliable. This paper describes a new software tool, called Elicitator, which assists in quantifying expert knowledge in a form suitable for use as a prior model in Bayesian regression. Potential environmental domains for applying this elicitation tool include habitat modelling, assessing detectability or eradication, ecological condition assessments, risk analysis and quantifying inputs to complex models of ecological processes. The tool has been developed to be user-friendly, extensible and facilitate consistent and repeatable elicitation of expert knowledge across these various domains. We demonstrate its application to elicitation for logistic regression in a geographically based ecological context. The underlying statistical methodology is also novel, utilizing an indirect elicitation approach to target expert knowledge on a case-by-case basis. For several elicitation sites (or cases), experts are asked simply to quantify their estimated ecological response (e.g. probability of presence), and its range of plausible values, after inspecting (habitat) covariates via GIS.     

An incremental solution for developing knowledge-based software: its application to an expert system for isokinetics interpretation

Many researchers working in the field of knowledge engineering (KE) are now concerned with identifying a model suitable for developing knowledge-based software and, especially, expert systems (ES). It is important to find a standard model that meets current needs and incorporates techniques successfully implemented in SE (object- or event-orientation, etc.), which are also of keen interest in KE.In this paper, we present an iterative and incremental solution for developing ES, according to which the system domain is derived naturally from the problem domain, thus surmounting the problems now involved in the transition from the conceptual model of the problem to the formal model of the system.As compared with conventional development models, this solution encompasses five main tools, which are:• Use cases with their respective actor interaction diagrams and activity flow diagrams in order to specify the expert system.• The concept dictionary, which allows knowledge engineers to define, bound and select the meaning of each concept used by experts.• The static conceptual model, which provides an overview (concepts and their relations) of the expert system (ES) modelled.• The control and process model, which models the knowledge and metaknowledge used by the expert to attain a goal.• An object-oriented metamodel, which outputs the formal knowledge model, providing an efficient, reusable, extendible and easy-to-implement ES architecture.To demonstrate the robustness of this solution, we describe how it was applied to an ES that interprets the graphs output by an isokinetics machine for a blind person. An isokinetics machine assesses the strength of the muscles of the leg, arm, etc.     

Prediction of composite suitability index for physical habitat simulations using the ANFIS method

A physical habitat simulation is a useful tool for assessing the impact of river development or restoration on river ecosystem. Conventional methods of physical habitat simulation use the habitat suitability index models and their success depends largely on how well the model reflects monitoring data. One of preferred habitat suitability index models is habitat suitability curves, which are normally constructed based on monitoring data. However, these curves can easily be affected by the subjective opinion of the expert. This study introduces the ANFIS method for predicting the composite suitability index for use in physical habitat simulations. The ANFIS method is a hybrid type of artificial intelligence technique that combines the artificial neural network and fuzzy logic. The method is known to be a powerful approach especially for developing nonlinear relationships between input and output datasets.In this study, the ANFIS method was used to predict the composite suitability index for the physical habitat simulation of a 2.5 km long reach of the Dal River in Korea. Zacco platypus was chosen as the target fish of the study area. A 2D hydraulic simulation was performed, and the hydraulic model was validated by comparing the measured and predicted water surface elevations. The distribution of the composite suitability index predicted by the ANFIS model was compared with that using the habitat suitability curves. The comparisons reveal that the two distributions are similar for various flows. In addition, the distribution of the composite suitability index of the Dal River is computed by the ANFIS method using monitoring data for the other watersheds, namely the Hongcheon River, the Geum River, and the Chogang Stream. The monitoring data for the Chogang Stream, correlation pattern of which was the most similar to that of the Dal River, yielded the distribution of the composite suitability index, which was very close to that obtained using data for the Dal River. This is also supported by the mean absolute percentage error for the difference in the weighted usable areas.     

ADAPTIVE DECISION SUPPORT SYSTEM FOR AUTOMATIC PHYSICAL EFFORT PLAN GENERATION—DATA-DRIVEN APPROACH

Mathematical models delivered using both expert knowledge and experimental data improve understanding of dynamic properties of the system under consideration. This is useful for different purposes, such as prediction, diagnosis, decision making, and system control. A data-driven approach has been found to be particularly useful in designing adaptive decision support systems. We demonstrate the usefulness of data-driven models in a custom application designed for sport training management. We have developed a system that makes use of expert knowledge together with measurement data (heart rate, electromyography, and acceleration) as well as environmental (Global Positioning System) in order to generate an optimal training plan. The system performs such tasks as modeling of the athlete's cardiovascular system, estimation of the athlete's parameters, and adaptation of the model to the athlete.     

Toward an engineering science of knowledge-based design

In this paper, we present a framework for organizing, evaluating, and developing knowledge-based models of the design process. We argue that evaluation of a design process model can be carried out from three usefully distinguished perspectives: the knowledge it embodies; the functionality of the design process, from a problem-solving viewpoint; and the implementation of the design process as an actual program. This paper focuses on the first two perspective. We systematically introduce a set of basic functional components, and show how existing approaches or systems can be viewed as configurations of these components, in which domain knowledge has been incorporated. As we lay out this framework, we illustrate it in a simple way by using it to describe knowledge-based house floorplanners. We then complete our presentation by analysing a more complex knowledge-based system (DONTE) that designs circuits.     

Fishing ground prediction using a knowledge-based expert system geographical information system model in the South and Central Sulawesi coastal waters of Indonesia

A knowledge-based expert system model working on the basis of a geographical information system (GIS) was applied to predict fishing ground spots in the coastal waters of South and Central Sulawesi. The model is designed by the integration of multisource data to answer ‘what?’, ‘where?’, and ‘why?’ questions of the fishing ground location. Despite the fact that GIS is a powerful tool for dealing with the first two questions, GIS is inferior for answering the ‘why?’ question in geo-studies. One of the possible ways of overcoming the inferiority of GIS for answering the ‘why?’ question of geo-studies is by integrating an expert system in a GIS to form a knowledge-based expert system GIS model. In this study, we used a series of sea surface temperature (SST) satellite data, sea surface chlorophyll-a (SSC) and turbidity derived from MODIS Aqua in the period 2003–2005 as input data, to understand the temporal and seasonal variability of the marine environment of the study area, and identified the oceanographic phenomena, i.e. upwelling, front or eddy. A spatial configuration map of the predicted fishing ground spots was then developed and integrated using a knowledge-based expert system GIS model generated by the Erdas Macro Language (EML) of Erdas Imagine 9.0 software. To verify this result, a series of in situ fishing ground spot data of the study area were collected for similar periods, and they were then analysed using a simple statistical method. The result shows that the predicted fishing ground spots generated by the knowledge-based expert system GIS model corresponded well with in situ data with a high accuracy level of 85%. This result has demonstrated that the knowledge-based expert system GIS model can be applied to predict, localize and determine fishing ground spots in which their accuracy level will be determined by the completeness of spatial knowledge of the domain expertise and the sophistication level of the programming utilities being used.     

Concept over time: the combination of probabilistic topic model with wikipedia knowledge

Probabilistic topic models could be used to extract low-dimension aspects from document collections, and capture how the aspects change over time. However, such models without any human knowledge often produce aspects that are not interpretable. In recent years, a number of knowledge-based topic models and dynamic topic models have been proposed, but they could not process concept knowledge and temporal information in Wikipedia. In this paper, we fill this gap by proposing a new probabilistic modeling framework which combines both data-driven topic model and Wikipedia knowledge. With the supervision of Wikipedia knowledge, we could grasp more coherent aspects, namely, concepts, and detect the trends of concepts more accurately, the detected concept trends can reflect bursty content in text and people’s concern. Our method could detect events and discover events specific entities in text. Experiments on New York Times and TechCrunch datasets show that our framework outperforms two baselines.     

A fuzzy connectionist expert system for visual pattern classification

In this paper, a connectionist model to integrate knowledge-based techniques into neural network approaches for visual pattern classification is presented. We propose a new structure of connectionist model which has rule-following capability as well as instance-based learning capability. Each node of the proposed network is doubly linked by two types of connections: positive connection and negative connection. Such connectionism provides a methodology to construct the classifier from the rule base and allows the expert knowledge to be utilized for the effective learning. For visual pattern classification, we present the techniques for knowledge representation and utilization using the concepts of fuzzy rules and fuzzy relations. We also discuss in this paper some advantageous characteristics of the model: result explanation capability and rule refinement capability. From the experimental results of the handwritten digit classification, the feasibility of the proposed model is evaluated.     

Computer programs that know: A tutorial

Computational process models have emerged as complements to more traditional statistical and mathematical models. Knowledge-based models and expert systems are particularly well suited for modeling within ill-structured and complex domains. In this tutorial, we discuss the feasibility, building, and application of knowledge-based models in some detail, emphasizing knowledge acquisition, knowledge representation, and system control issues. We conclude the essay with a more practical look at some of the intrigue and challenges associated with building knowledge-based models.     

知识和数据协同驱动的群体智能决策方法研究综述EI北大核心CSCD

群体智能(Collectire intelligence,CI)系统具有广泛的应用前景.当前的群体智能决策方法主要包括知识驱动、数据驱动两大类,但各自存在优缺点.本文指出,知识与数据协同驱动将为群体智能决策提供新解法.本文系统梳理了知识与数据协同驱动可能存在的不同方法路径,从知识与数据的架构级协同、算法级协同两个层面对典型方法进行了分类,同时将算法级协同方法进一步划分为算法的层次化协同和组件化协同,前者包含神经网络树、遗传模糊树、分层强化学习等层次化方法;后者进一步总结为知识增强的数据驱动、数据调优的知识驱动、知识与数据的互补结合等方法.最后,从理论发展与实际应用的需求出发,指出了知识与数据协同驱动的群体智能决策中未来几个重要的研究方向.     

Assessing certainty and uncertainty in riparian habitat suitability models by identifying parameters with extreme outputs

The aim of this paper is to introduce a computationally efficient uncertainty assessment approach using an index-based habitat suitability model. The approach focuses on uncertainty in ecological knowledge regarding parameters of index curves and weights. A case study determines which of two 15-year periods has more suitable surface water and groundwater regimes for riparian vegetation. The uncertainty assessment consists of defining constraints on index curves and weights. Linear programming is used to identify parameters that yield two extreme outputs: maximising and minimising differences between the two periods. Because they are extremes, if both outputs agree on which period is better (e.g. maximum and minimum differences are both positive), then all other models will also agree. Identifying models with extreme outputs prompts learning about the boundaries of our knowledge and identifies patterns about what is considered certain. It helps build an understanding of what is already known despite the high uncertainty.     

Applying KADS to KADS: knowledge-based guidance for knowledge engineering

Abstract: The KADS methodology (Schreiber et al. 1993; Tansley & Hayball 1993) and its successor, CommonKADS (Wielinga et al. 1992) have proved to be very useful approaches for modelling the various transformations involved between eliciting knowledge from an expert and encoding this knowledge in a computer program. These transformations are represented in a series of models. While it is widely agreed that these methods are excellent approaches from a theoretical viewpoint, the documentation provided concentrates on defining what models should be produced, with only general guidance on how the models should be produced. This has the advantage of making KADS and CommonKADS widely applicable, but it also means that considerable training and experience is required to become proficient in them. This paper reviews three projects that investigated the feasibility of producing specific guidance for certain decisions which are required when using KADS or CommonKADS to develop a knowledge-based system. Guidance was produced for the identification of the generic task addressed by a knowledge-based system; for the selection of appropriate AI techniques for implementing the analysed knowledge; and for selecting a suitable tool for implementing the system. Each set of guidance was encoded in its own knowledge-based system, which was itself developed with the assistance of KADS or CommonKADS. These projects therefore both studied and applied KADS and CommonKADS in order to produce knowledge-based guidance for knowledge engineers. The projects showed that it was feasible to produce heuristic guidance which could be understood, applied and occasionally overridden by knowledge engineers. The guidance provides reasonably experienced knowledge engineers with a framework for making the key decisions required by CommonKADS, in the same way that CommonKADS provides knowledge engineers with a framework for representing knowledge. The projects also produced some new insights about CommonKADS domain modelling and about the process of task identification.     

The role of the industrial engineer in developing expert systems

The most popular area of Artificial Intelligence application today is in expert systems. This paper contains a discussion of expert systems, otherwise known as knowledge-based systems and knowledge systems. The principal components of an expert system, and the evolution of expert systems are presented. The suitability of a task to an expert system is proposed. When a task is suitable for an expert system application, the system must be developed by a knowledge engineer. The methodology that the knowledge engineer must go through to develop an expert system is demostrated. Industrial engineers have formal training in many areas which can be useful when assumming the role of knowledge engineer. These areas of industrial engineering and how they are beneficial is discussed. What the future may hold in store is also pondered.     

Knowledge elicitation using more than one expert to cover the same domain

Knowledge elicitation is accepted as being one of the most problematic areas in the creation of a knowledge-based system.A large amount of research has already concentrated on finding more efficient and effective techniques for eliciting knowledge from an individual expert. However, little attention has been given to the involvement of more than one source of expertise in knowledge-based system development.This paper is based on the authors' practical experience gained when developing a knowledge-based system for the conceptual design of bridges. It shows that the use of more than one expert throughout the knowledge elicitation process can improve both the efficiency of the approach and the quality of the knowledge acquired.     

Expertise transfer, knowledge elicitation, and delayed recall in a design context

This paper describes a new method for knowledge elicitation that may contribute to effective expertise transfer from human experts to knowledge-based systems. The method was applied to knowledge transfer in an aerospace design context. Knowledge was transferred directly from an expert designer to both expert and novice “receivers” of information. Transfer occurred in a natural way, without intervention from a knowledge engineer. To evaluate the process, the information receivers were required to recall the transmitted knowledge after a seven week delay. Results suggest that this method can be effective for expertise transfer and can indicate desirable characteristics for knowledge-based systems which aim to be adaptable to users' differing levels of competence.