Data mining as a tool for environmental scientists

Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modelling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data mining techniques could be usefully applied to difficult problems in the field. This paper introduces several data mining concepts and briefly discusses their application to environmental modelling, where data may be sparse, incomplete, or heterogenous

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

An Agent-Based Variogram Modeller: Investigating Intelligent, Distributed-Component Geographical Information Systems

Geo-Information Science (GIScience) is the field of study that addresses substantive questions concerning the handling, analysis and visualisation of spatial data. Geo- Information Systems (GIS), including software, data acquisition and organisational arrangements, are the key technologies underpinning GIScience. A GIS is normally tailored to the service it is supposed to perform. However, there is often the need to do a function that might not be supported by the GIS tool being used. The normal solution in these circumstances is to go out and look for another tool that can do the service, and often an expert to use that tool. This is expensive, time consuming and certainly stressful to the geographical data analyses. On the other hand, GIS is often used in conjunction with other technologies to form a geocomputational environment. One of the complex tools in geocomputation is geostatistics. One of its functions is to provide the means to determine the extent of spatial dependencies within geographical data and processes. Spatial datasets are often large and complex. Currently Agent system are being integrated into GIS to offer flexibility and allow better data analysis. The theis will look into the current application of Agents in within the GIS community, determine if they are used to representing data, process or act a service. The thesis looks into proving the applicability of an agent-oriented paradigm as a service based GIS, having the possibility of providing greater interoperability and reducing resource requirements (human and tools). In particular, analysis was undertaken to determine the need to introduce enhanced features to agents, in order to maximise their effectiveness in GIS. This was achieved by addressing the software agent complexity in design and implementation for the GIS environment and by suggesting possible solutions to encountered problems. The software agent characteristics and features (which include the dynamic binding of plans to software agents in order to tackle the levels of complexity and range of contexts) were examined, as well as discussing current GIScience and the applications of agent technology to GIS, agents as entities, objects and processes. These concepts and their functionalities to GIS are then analysed and discussed. The extent of agent functionality, analysis of the gaps and the use these technologies to express a distributed service providing an agent-based GIS framework is then presented. Thus, a general agent-based framework for GIS and a novel agent-based architecture for a specific part of GIS, the variogram, to examine the applicability of the agent- oriented paradigm to GIS, was devised. An examination of the current mechanisms for constructing variograms, underlying processes and functions was undertaken, then these processes were embedded into a novel agent architecture for GIS. Once the successful software agent implementation had been achieved, the corresponding tool was tested and validated - internally for code errors and externally to determine its functional requirements and whether it enhances the GIS process of dealing with data. Thereafter, its compared with other known service based GIS agents and its advantages and disadvantages analysed

Review of QSAR Models and Software Tools for Predicting of Genotoxicity and Carcinogenicity

This review of QSARs for genotoxicity and carcinogenicity was performed in a broad sense, considering both models available in software tools and models that are published in the literature. The review considered the potential applicability of diverse models to pesticides as well as to other types of regulated chemicals and pharmaceuticals. The availability of models and information on their applicability is summarised in tables, and a range of illustrative or informative examples are described in more detail in the text. In many cases, promising models were identified but they are still at the research stage. For routine application in a regulatory setting, further efforts will be needed to explore the applicability of such models for specific purposes, and to implement them in a practically useful form (i.e. user-friendly software). It is also noted that a range of software tools are research tools suitable for model development, and these require more specialised expertise than other tools that are aimed primarily at end-users such as risk assessors. It is concluded that the most useful models are those which are implemented in software tools and associated with transparent documentation on the model development and validation process. However, it is emphasised that the assessment of model predictions requires a reasonable amount of QSAR knowledge, even if it is not necessary to be a QSAR practitioner.JRC.DG.I.6-Systems toxicolog

Artificial Intelligence - How can water planning and management benefit from?

IAHR White Papers Online: https://www.iahr.org/index/detail/6

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Recent advances in directional statistics

Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio-temporal data. An overview of currently available software for analysing directional data is also provided, and potential future developments discussed.Comment: 61 page

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 299)

This bibliography lists 96 reports, articles, and other documents introduced into the NASA scientific and technical information system in June, 1987