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Data Mining Practical Machine Learning Tools and Techniques

Statistical Analysis with Missing Data

A freely available dataset produced from three million Landsat satellite images reveals substantial changes in the distribution of global surface water over the past 32 years and their causes, from climate change to human actions. The distribution of surface water has been mapped globally, and local-to-regional studies have tracked changes over time. But to date, there has been no global and methodologically consistent quantification of changes in surface water over time. Jean-Francois Pekel and colleagues have analysed more than three million Landsat images to quantify month-to-month changes in surface water at a resolution of 30 metres and over a 32-year period. They find that surface waters have declined by almost 90,000 square kilometres—largely in the Middle East and Central Asia—but that surface waters equivalent to about twice that area have been created elsewhere. Drought, reservoir creation and water extraction appear to have driven most of the changes in surface water over the past decades. The location and persistence of surface water (inland and coastal) is both affected by climate and human activity1 and affects climate2,3, biological diversity4 and human wellbeing5,6. Global data sets documenting surface water location and seasonality have been produced from inventories and national descriptions7, statistical extrapolation of regional data8 and satellite imagery9,10,11,12, but measuring long-term changes at high resolution remains a challenge. Here, using three million Landsat satellite images13, we quantify changes in global surface water over the past 32 years at 30-metre resolution. We record the months and years when water was present, where occurrence changed and what form changes took in terms of seasonality and persistence. Between 1984 and 2015 permanent surface water has disappeared from an area of almost 90,000 square kilometres, roughly equivalent to that of Lake Superior, though new permanent bodies of surface water covering 184,000 square kilometres have formed elsewhere. All continental regions show a net increase in permanent water, except Oceania, which has a fractional (one per cent) net loss. Much of the increase is from reservoir filling, although climate change14 is also implicated. Loss is more geographically concentrated than gain. Over 70 per cent of global net permanent water loss occurred in the Middle East and Central Asia, linked to drought and human actions including river diversion or damming and unregulated withdrawal15,16. Losses in Australia17 and the USA18 linked to long-term droughts are also evident. This globally consistent, validated data set shows that impacts of climate change and climate oscillations on surface water occurrence can be measured and that evidence can be gathered to show how surface water is altered by human activities. We anticipate that this freely available data will improve the modelling of surface forcing, provide evidence of state and change in wetland ecotones (the transition areas between biomes), and inform water-management decision-making.

High-resolution mapping of global surface water and its long-term changes

Dealing with uncertainty is always a challenging problem, and different tools have been proposed to deal with it. Recently, a new model that is based on hesitant fuzzy sets has been presented to manage situations in which experts hesitate between several values to assess an indicator, alternative, variable, etc. Hesitant fuzzy sets suit the modeling of quantitative settings; however, similar situations may occur in qualitative settings so that experts think of several possible linguistic values or richer expressions than a single term for an indicator, alternative, variable, etc. In this paper, the concept of a hesitant fuzzy linguistic term set is introduced to provide a linguistic and computational basis to increase the richness of linguistic elicitation based on the fuzzy linguistic approach and the use of context-free grammars by using comparative terms. Then, a multicriteria linguistic decision-making model is presented in which experts provide their assessments by eliciting linguistic expressions. This decision model manages such linguistic expressions by means of its representation using hesitant fuzzy linguistic term sets.

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Hesitant Fuzzy Linguistic Term Sets for Decision Making

Multiple criteria decision analysis (MCDA) methods have become increasingly popular in the performance assessment and decision making of renewable energy systems due to the multi-dimensionality and complexity of its technical, environmental, economic and social impacts. The aim of this paper is to develop a preliminary performance assessment model for the feasibility analysis of constructing different multi-vector decentralized renewable energy systems in an industrial park. Four micro-grid energy systems are identified as alternative solutions in terms of the local environmental conditions and energy profile of the industrial park, and these alternatives are evaluated against a set of performance criteria. The evidential reasoning approach with the implementation of Intelligent Decision System (IDS) is then applied to aggregate assessment information in order to obtain the overall ranking of alternative solutions. sensitivity and trade-off analysis is conducted to validate the robustness of the decision making process.

Multiple Criteria Performance Assessment for Decentralized Energy Systems: A Case Study

An investigation into multiple criteria evaluation of consumer preferences under uncertainty

Belief rule-based (BRB) systems are an extension of traditional IF-THEN rule based systems and capable of capturing complicated nonlinear causal relationships between antecedent attributes and consequents. In a BRB system, various types of information and knowledge with uncertainties can be represented using belief structures, and a belief rule is designed with belief degrees embedded in its possible consequents. In this paper, we first review the scheme of belief rules for representing and inferring uncertain knowledge. Then we present two BRB system identification methods in which different training objectives are used. Finally, numerical studies are conducted to demonstrate the capability of BRB systems on uncertain nonlinear system modeling and identification.

Uncertain nonlinear system modeling and identification using belief rule-based systems

In this paper a decision support system for sys- tematically evaluating the impact of labeling products with their carbon footprints is developed and applied to prioritize products for carbon labeling in a large supermarket chain in the UK. Carbon labels may change consumers' behav- ior and encourage suppliers to implement carbon-reduction solutions. Those changes may, however, lead to unintended risks. To handle the challenges of uncertainties in the evalu- ation, the Evidential Reasoning approach and the Intelligent Decision System software for multi-criteria decision analy- sis are applied to support the process. The system developed can be applied to assessing the impact of sustainable devel- opment policies to maximize their benefits and minimize their risks.

/pdf/decision-support-system-for-evaluating-impact-of-product-3w3bm3hmm7.pdf

Decision Support System for Evaluating Impact of Product Carbon Labeling Scheme

Automobile insurance fraud detection has become critically important for reducing the costs of insurance companies. The majority of insurance companies use expert knowledge to detect fraud. Experience-based knowledge are interpretable and re-usable but the simplistic way that this knowledge is used in practice, often leads to some degree of misjudgment. This paper aims to establish a unique Evidential Reasoning (ER) rule that combines independent evidence from both experience based indicators and probabilities of fraud obtained from historical data. Each piece of evidence is weighted and then combined conjunctively with the weights optimised using a maximum likelihood evidential reasoning (MAKER) framework for datadriven inferential modelling. Based on a real-world insurance claim dataset, our experimental results reveal that the proposed approach preserves the interpretability and usability of expert detection system, and anticipates the changes in fraud practices by tracking the trend of the weights of experience-based indicators. Furthermore, the experimental results show that the proposed approach outperforms a number of widely used machine learning models, such as logistic regression and random forests.

Dong-Ling Xu

Papers

Multiple Criteria Performance Assessment for Decentralized Energy Systems: A Case Study

An investigation into multiple criteria evaluation of consumer preferences under uncertainty

Uncertain nonlinear system modeling and identification using belief rule-based systems

Decision Support System for Evaluating Impact of Product Carbon Labeling Scheme

Automobile Insurance Fraud Detection using the Evidential Reasoning Approach and Data-Driven Inferential Modelling