F-IND: A framework to design fuzzy indices of environmental conditions

Abstract Multivariable indices of environmental conditions summarize the information provided by several biotic or abiotic variables into a single value of immediate interpretability. Thus they are important instruments for monitoring. Developing new indices that combine different variables is not a trivial task: variables may be qualitative, or measured in different units, and the relationship between primitive components and quality may be ambiguous. Fuzzy logic has been repeatedly proposed as an effective technique to cope with such problems; however, the variety of choices that exist at each stage of the development of fuzzy models may present a problem for the index designer. In this paper we present F-IND, a framework to create fuzzy indices by means of a simplified and intuitive procedure. It allows to capture the expert knowledge of the system under study (air, soil, water) to easily generate a multivariable index of environmental conditions. F-IND is implemented in Java, to achieve an optimal portability on any operating system.

[1]  Bernard De Baets,et al.  Fuzzy rule-based models for decision support in ecosystem management. , 2004, The Science of the total environment.

[2]  William Silvert,et al.  Fuzzy indices of environmental conditions , 2000 .

[3]  Michele Scardi,et al.  An expert system based on fish assemblages for evaluating the ecological quality of streams and rivers , 2008, Ecol. Informatics.

[4]  V. Garg,et al.  Analysis of groundwater quality using fuzzy synthetic evaluation. , 2007, Journal of hazardous materials.

[5]  Carlo Marchini,et al.  A fuzzy logic model to recognise ecological sectors in the lagoon of Venice based on the benthic community , 2006 .

[6]  Henk Udo,et al.  ASSESSMENT OF THE CONTRIBUTION OF SUSTAINABILITY INDICATORS TO SUSTAINABLE DEVELOPMENT: A NOVEL APPROACH USING FUZZY SET THEORY , 2001 .

[7]  Dong-Hoon Park,et al.  Fuzzy expert system to determine stream water quality classification from ecological information , 1997 .

[8]  Mahmut Bayramoglu,et al.  Adaptive neuro-fuzzy based modelling for prediction of air pollution daily levels in city of Zonguldak. , 2006, Chemosphere.

[9]  N B Chang,et al.  Identification of river water quality using the fuzzy synthetic evaluation approach. , 2001, Journal of environmental management.

[10]  Helen M. Regan,et al.  A proposal for fuzzy International Union for the Conservation of Nature (IUCN) categories and criteria , 2000 .

[11]  Guillermo A. Mendoza,et al.  Fuzzy methods for assessing criteria and indicators of sustainable forest management , 2004 .

[12]  Shang-Lien Lo,et al.  Diagnosing reservoir water quality using self-organizing maps and fuzzy theory. , 2002, Water research.

[13]  O. Fränzle,et al.  Complex bioindication and environmental stress assessment , 2006 .

[14]  William W. L. Cheung,et al.  A Fuzzy Logic Expert System to Estimate Intrinsic Extinction Vulnerabilities of Marine Fishes to Fishing , 2004 .

[15]  Wolfgang Ahlf,et al.  Application of a Sediment Quality Triad and Different Statistical Approaches (Hasse Diagrams and Fuzzy Logic) for the Comparative Evaluation of Small Streams , 2002, Ecotoxicology.

[16]  Keith M. Reynolds,et al.  Suitability for conservation as a criterion in regional conservation network selection , 2008, Biodiversity and Conservation.

[17]  Christian Kampichler,et al.  Ground beetle occurrence and moor degradation: modelling a bioindication system by automated decision-tree induction and fuzzy logic , 2004 .

[18]  Richard B. Shepard,et al.  Quantifying Environmental Impact Assessments Using Fuzzy Logic , 2005 .

[19]  W. Silvert.  Ecological impact classification with fuzzy sets , 1997 .

[20]  J S Gray,et al.  Pollution-induced changes in populations. , 1979, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[21]  Hans-Georg Frede,et al.  Quantification of soil properties based on external information by means of fuzzy‐set theory , 2002 .

[22]  Keith M. Reynolds.  NetWeaver for EMDS user guide (version 1.1): a knowledge base development system. , 1999 .

[23]  Javier Romero,et al.  A multivariate index based on the seagrass Posidonia oceanica (POMI) to assess ecological status of coastal waters under the water framework directive (WFD). , 2007, Marine pollution bulletin.

[24]  W. Silvert,et al.  Describing benthic impacts of fish farming with fuzzy sets: theoretical background and analytic methods , 1998 .

[25]  E. Cameron,et al.  An application of fuzzy logic to the assessment of aquifers' pollution potential , 2001 .

[26]  Antonio Marcomini,et al.  Macroalgae, nutrient cycles, and pollutants in the Lagoon of Venice , 1992 .

[27]  Jean-Paul Chavas,et al.  Ecosystem Valuation under Uncertainty and Irreversibility , 2000, Ecosystems.

[28]  J. Marques,et al.  Opportunistic macroalgae metrics for transitional waters. Testing tools to assess ecological quality status in Portugal. , 2007, Marine pollution bulletin.

[29]  Tony Prato.  A fuzzy logic approach for evaluating ecosystem sustainability , 2005 .

[30]  Robert Jandl,et al.  Identification of degraded forest soils by means of a fuzzy‐logic based model , 2002 .

[31]  Agnese Marchini,et al.  Response of a Fuzzy INdex of Ecosystem integrity (FINE) to water and sedimentary chemical data in two northern Adriatic lagoons , 2007 .

[32]  M. Burgman,et al.  Assessment of Threat and Conservation Priorities under Realistic Levels of Uncertainty and Reliability , 1998 .

[33]  Richard W. Hamming,et al.  Error detecting and error correcting codes , 1950 .

[34]  Christopher R. Pyke.  Assessing suitability for conservation action: Prioritizing interpond linkages for the California tiger salamander , 2005 .

[35]  William Ocampo-Duque,et al.  A neural-fuzzy approach to classify the ecological status in surface waters. , 2007, Environmental pollution.

[36]  Martin Wilkinson,et al.  The use of macroalgal species richness and composition on intertidal rocky seashores in the assessment of ecological quality under the European Water Framework Directive. , 2007, Marine pollution bulletin.

[37]  Michele Mistri,et al.  Evaluation of the applicability of a fuzzy index of ecosystem integrity (FINE) to characterize the status of Tyrrhenian lagoons. , 2007, Marine environmental research.

[38]  LUIS A. BOJÓRQUEZ-TAPIA,et al.  Integrating Fuzzy Logic, Optimization, and GIS for Ecological Impact Assessments , 2002, Environmental management.

[39]  Yilmaz Icaga,et al.  Fuzzy evaluation of water quality classification , 2007 .

[40]  Shang-Lien Lo,et al.  Application of two-stage fuzzy set theory to river quality evaluation in Taiwan. , 2003, Water research.

[41]  Aysen Ergin,et al.  Marine litter prediction by artificial intelligence. , 2004, Marine pollution bulletin.

[42]  Renkuan Guo,et al.  Predicting air pollution using fuzzy membership grade Kriging , 2007, Comput. Environ. Urban Syst..

[43]  Peter Nijkamp,et al.  Qualitative multicriteria methods for fuzzy evaluation problems: An illustration of economic-ecological evaluation , 1995 .

[44]  Agnese Marchini,et al.  The fuzzy index of ecosystem integrity (FINE): a new index of environmental integrity for transitional ecosystems , 2008, Hydrobiologia.

[45]  D. Borri,et al.  A fuzzy approach for modelling knowledge in environmental systems evaluation , 1998 .

[46]  M. Enea,et al.  Fuzzy approach to the environmental impact evaluation , 2001 .