The unbearable fuzziness of being sustainable: an integrated, fuzzy logic-based aquifer health index

Abstract We introduce a groundwater sustainability index offering a novel combination of features. It is holistic in the sense that it incorporates both water quantity and water quality indicators. The former employs the signal-to-noise ratio of long-term trends estimated via robust regression; the latter uses concentration of the primary contaminant of concern. A fuzzy inference system integrates these unlike metrics. The system also explicitly encodes expert knowledge and stakeholder values, and directly acknowledges subjectivity in environmental condition “grading,” through the use of linguistic rules and fuzzy sets, respectively. The fuzzy rule base is constructed such that poor environmental conditions captured by one measure are not hidden by good performance in another. A standard Mamdani (max–min) inference engine is used with centroid defuzzification. The outcome is an intuitively accessible index ranging from 0 to 100. The method is demonstrated using examples from the Abbotsford-Sumas aquifer, an important and managerially challenging transboundary (Canada–US) water resource. Editor D. Koutsoyiannis; Associate editor E. Rozos Citation Fleming, S.W., Wong, C., and Graham, G., 2014. The unbearable fuzziness of being sustainable: an integrated, fuzzy logic-based aquifer health index. Hydrological Sciences Journal, 59 (6), 1154–1166. http://dx.doi.org/10.1080/02626667.2014.907496

[1]  Teresa To,et al.  The Air Quality Health Index and Asthma Morbidity: A Population-Based Study , 2012, Environmental health perspectives.

[2]  Marko Bohanec,et al.  Decision Support , 2008, Encyclopedia of GIS.

[3]  Abdüsselam Altunkaynak,et al.  Fuzzy awakening in rainfall-runoff modeling , 2004 .

[4]  Reza Kerachian,et al.  Optimal redesign of groundwater quality monitoring networks: a case study , 2010, Environmental monitoring and assessment.

[5]  A. Robinson Fayek,et al.  Application of fuzzy logic to forecast seasonal runoff , 2003 .

[6]  A M Jinturkar,et al.  Determination of water quality index by fuzzy logic approach: a case of ground water in an Indian town. , 2010, Water science and technology : a journal of the International Association on Water Pollution Research.

[7]  Sean W. Fleming,et al.  Detection of long-term change in hydroelectric reservoir inflows: Bridging theory and practise , 2012 .

[8]  Deg-Hyo Bae,et al.  Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique , 2007 .

[9]  Gordon H. Huang,et al.  Prioritizing groundwater remediation policies: a fuzzy compatibility analysis decision aid. , 2007, Journal of environmental management.

[10]  A. Bárdossy,et al.  Development of a fuzzy logic-based rainfall-runoff model , 2001 .

[11]  A. Nedungadi,et al.  Fuzzy Rule-Based Hydrologic Models for Performance Assessment of Nuclear Waste Disposal Sites , 2009 .

[12]  Christian Borgelt,et al.  Computational Intelligence , 2016, Texts in Computer Science.

[13]  Sangam Shrestha,et al.  A framework for measuring groundwater sustainability , 2011 .

[14]  H. S. Kushwaha,et al.  Use of fuzzy synthetic evaluation for assessment of groundwater quality for drinking usage: a case study of Southern Haryana, India , 2008 .

[15]  Jan Juretzka,et al.  Decision Support System , 2001 .

[16]  Markus Disse,et al.  Fuzzy rule-based models for infiltration , 1993 .

[17]  H. Theil.  A Rank-Invariant Method of Linear and Polynomial Regression Analysis , 1992 .

[18]  K. Lansey,et al.  Sustainability Indicators for Long-Term Water Supply: Case Studies of Tucson Active Management Area , 2011 .

[19]  Terrestrial Environments,et al.  Ecological indicators for the nation , 2013 .

[20]  V. Tsihrintzis,et al.  Assessment of groundwater vulnerability to pollution: a combination of GIS, fuzzy logic and decision making techniques , 2006 .

[21]  Dhundi Raj Pathak,et al.  Combining Rule-Based Fuzzy Model with GIS to Predict Groundwater Vulnerability to Contamination , 2009 .

[22]  L. Wassenaar,et al.  Decadal geochemical and isotopic trends for nitrate in a transboundary aquifer and implications for agricultural beneficial management practices. , 2006, Environmental science & technology.

[23]  C. Cosenza,et al.  Groundwater vulnerability and risk mapping using GIS, modeling and a fuzzy logic tool. , 2007, Journal of contaminant hydrology.

[24]  William Ocampo-Duque,et al.  Assessing water quality in rivers with fuzzy inference systems: a case study. , 2006, Environment international.

[25]  Z. Şen.  Fuzzy Groundwater Classification Rule Derivation from Quality Maps , 2009 .

[26]  B. Zebarth,et al.  Agricultural land use practices and nitrate contamination in the Abbotsford Aquifer, British Columbia, Canada , 1998 .

[27]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[28]  David M Stieb,et al.  A New Multipollutant, No-Threshold Air Quality Health Index Based on Short-Term Associations Observed in Daily Time-Series Analyses , 2008, Journal of the Air & Waste Management Association.

[29]  Gordon H. Huang,et al.  An integrated fuzzy-stochastic modeling approach for risk assessment of groundwater contamination. , 2007, Journal of environmental management.

[30]  L. Nanus,et al.  Nitrate distributions and source identification in the Abbotsford-Sumas Aquifer, northwestern Washington State. , 2003, Journal of environmental quality.

[31]  A. Elmahdi,et al.  A decision support system for sustainable groundwater management. Case study: Gnangara Sustainability Strategy - Western Australia. , 2009 .

[32]  Zekai Sen,et al.  Fuzzy Logic and Hydrological Modeling , 2009 .

[33]  Robert I. McDonald,et al.  Global Urban Growth and the Geography of Water Availability, Quality, and Delivery , 2011, AMBIO.

[34]  Xia Meng,et al.  Communicating air pollution-related health risks to the public: an application of the Air Quality Health Index in Shanghai, China. , 2013, Environment international.

[35]  Sean W. Fleming,et al.  Signal-to-Noise Ratios of Geophysical and Environmental Time Series , 2010 .

[36]  Erol Kesk,et al.  FLOW PREDICTION MODEL WITH FUZZY LOGIC APPROACHES: DIM STREAM , 2007 .

[37]  F. E. Hicks,et al.  FORECASTING ICE JAM RISK AT FORT MCMURRAY, AB, USING FUZZY LOGIC , 2003 .

[38]  D. Helsel,et al.  Statistical methods in water resources , 2020, Techniques and Methods.

[39]  Lucien Duckstein,et al.  Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological and Engineering Systems , 1995 .

[40]  H. J. Henriksen,et al.  Assessment of exploitable groundwater resources of Denmark by use of ensemble resource indicators and a numerical groundwater–surface water model , 2008 .

[41]  Ann Louise Heathwaite,et al.  Nitrate in groundwater. , 1993 .

[42]  A decision support system for sustainable urban water supply , 2011 .

[43]  C. Sasser.  Ecological Indicators for the Nation , 2009 .

[44]  David G. Smith.  A New Form of Water Quality Index for Rivers and Streams , 1989 .

[45]  P. Sen.  Estimates of the Regression Coefficient Based on Kendall's Tau , 1968 .

[46]  Matthias Hinderer,et al.  Assessment of groundwater vulnerability , 2003 .