Andy Verkeyn

Fuzzy models for accumulation of reported community noise annoyance from combined sources

Many scientists have investigated noise annoyance caused by combined sources. However, general annoyance reported in a social survey still has many unknown features. In this work the cognitive process involved in coming to a general noise rating based on a known, in context, rating of annoyance by particular sources is studied. A comparison of classical and fuzzy models is used for this. The new fuzzy linguistic models give a meaning to the successful strongest component or dominant source model that was used in previous work. They also explain to some extent particular features not included in that previous model. The variance not predicted by the fuzzy linguistic model is contrasted with personal data of the test subjects (age, gender, and education level) and the context of the question in the questionnaire. Only age seems to play a significant role.

Sugeno integrals for the modelling of noise annoyance aggregation

This paper investigates the use of the Sugeno integral for modelling the unconscious aggregation performed by people when trying to rate the discomfort of their living environment. This general annoyance rating is modelled based on known annoyance caused by a number of sources or activities. The approach is illustrated on data of a Flemish survey.

An iterative fuzzy model for cognitive processes involved in environment quality judgement

Social surveys are commonly used to assess the quality of the living environment. Models allow quantifying the impact of future developments. Starting from basic knowledge on the cognitive process of human judgement a fuzzy model is developed, focussing on the aggregation of the impact of particular activities to a global judgement.

Fuzzy Integrals as a Tool for Obtaining an Indicator for Quality of Life

Many governments and international organizations recognize the fact that classical economic indicators do not accurately reflect the quality of life in a country or region. Indicators that reflect the subjective evaluation of well-being or quality of life by inhabitants have to be constructed based on multiple criteria. Multi-criteria evaluation systems based on fuzzy integrals seem very well suited for this task as they tend to approximate overall quality assessment by inhabitants quite well. This paper discusses the choice of fuzzy integrals and analyses the suitability of the approach based on a survey with 2000 people.

Fuzzy modeling of traffic noise annoyance

This paper presents a fuzzy rule-based model for the prediction of traffic noise annoyance. Several inference schemes are compared for their performance in prediction capabilities as well as in speed. It is shown that the fastest implementation does an equally good job, after optimization of certainty degrees attached to the rules. For this optimization, a genetic algorithm is applied.

Genetic learning of fuzzy integrals accumulating human-reported environmental stress

In this paper, we develop models based on fuzzy integrals (both of the Choquet and Sugeno type) for accumulating annoyance by noise, odor or light caused by particular sources or activities. As underlying fuzzy measures, we have opted for k-maxitive measures (in particular 1-maxitive or 2-maxitive) as the best known crisp model points in this direction. The fuzzy measures are learnt from survey data and optimized using genetic algorithms. Attention is paid to several types of inconsistencies that typically arise in data sets collected through social surveys. Also, special care is taken to make sure that the Sugeno integral and the genetic algorithm that optimizes the associated fuzzy measure operates solely on the ordinal scale of linguistic labels.

ANNOYANCE PREDICTION WITH FUZZY RULE BASES

This paper places the perception of noise annoyance within the framework of environmental assessment. It is shown that due to the inherent vagueness and uncertainties in the involved concepts, available data and knowledge, fuzzy techniques are very well suited for the modeling of this perception. This idea is then used to construct a fuzzy rule based model for the prediction of the level of general noise annoyance. It turns out that this model can be decomposed in building blocks, each describing the noise annoyance caused by an individual source. Finally, the models are tested against data collected in social surveys and the results are reported.

What can classical and fuzzy concepts contribute to the analysis of masking effects in environmental noise surveys

In laboratory research masking of one noise source by another is well studied. Field research based on noise surveys fails to show this effect clearly. Both the uncertainty in noise levels and other environmental variables and the fuzziness in annoyance contribute to the difficulty of the problem. Traditional statistical and fuzzy techniques were used to look for masking effects in a large environmental noise dataset gathered in Austria. The fuzzy analyses use a rule‐based model that is constructed to predict noise annoyance. Specific attention is paid to rules that predict masking based on variables such as background noise level, distance to masker, direction of bedroom and living room windows, etc.

Noise annoyance mapping

Geographical Information Systems (GIS) allow attaching different types of information to geographical primitives. Compared to general-purpose databases, these spatial databases are augmented with a number of functions directly related to geography. Queries based on distance, surface, and spatial aggregation are but a few examples. Even more important are the extended possibilities for visualization that a typical GIS application offers. Because of these, GIS are now becoming very popular in applications related to different fields and in particular play an important role in policy decision support. The policy maker has all available information at his or her fingertips and can identify “black point” or areas of potential interest at a glimpse. This chapter focuses on applications where human perception plays an important role and on EIA(Environmental Impact Assessment). It is build around a particular case: the assessment of noise annoyance, but concepts and methods introduced are obviously much broader. There are two great points of concern when addressing the aforementioned application areas using classical GIS. Firstly, working with human perception of for example the quality of life, the quality of the living environment, safety, or disturbance by noise or odor, quickly introduces a kind of vagueness, that classical GIS cannot easily store nor process.

Fuzzy translation tool for linguistic terms

An automatic translation tool for linguistic terms is built. The terms are represented by fuzzy sets and the translations are based on the similarity degree between those fuzzy sets. The tool is tested on 21 adverbs in 9 languages. The fuzzy sets are constructed with a probability based approach, based on data from an International study on the choice of appropriate terms to label a noise annoyance scale. The results are in agreement with common sense translations. A detailed sensitivity analysis shows that the procedure is stable for many operator choices.