Integrated water resources management of overexploited hydrogeological systems using Object-Oriented Bayesian Networks

Object-Oriented Bayesian Networks (OOBNs) have been used increasingly over the past few decades in fields as diverse as medicine, transport and aeronautics. In this paper, OOBNs are applied to the domain of integrated water management and used as a Decision Support System (DSS). This pioneering study, set in the Altiplano region of Murcia in Southern Spain, describes a method for the integrated analysis of a complex water system supplied by groundwater from four aquifers. This method is based on the development of a multivariable integrated technique based on Bayes' theorem. After identifying all relevant factors related to water management in the area these were then translated to variables within a Bayesian Network (BN) and the relationships between them investigated. Each network represented one of the four aquifer units. These individual BNs were then linked to form an OOBN which was used to represent the complex real-world situation. In this way a DSS to simulate the entire water system was constructed using a group of conventional Bns, linked to produce an OOBN. The main stakeholders of the region contributed to network design and construction throughout the entire process. The paper shows how this type of DSS can be used to evaluate the impacts of a range of management strategies that are available to local planners.

[1]  Anthony J. Jakeman,et al.  Development of Decision Support Tools to Assess the Sustainability of Coastal Lakes , 2005 .

[2]  K. Watkins.  Human Development Report 2006 - Beyond Scarcity: Power, Poverty and the Global Water Crisis , 2006 .

[3]  Richard York,et al.  Global biodiversity decline of marine and freshwater fish: A cross-national analysis of economic, demographic, and ecological influences , 2008 .

[4]  Nina Schwarz,et al.  An integrated modelling framework for simulating regional-scale actor responses to global change in the water domain , 2008, Environ. Model. Softw..

[5]  Charles J Vörösmarty,et al.  Scaling gridded river networks for macroscale hydrology: Development, analysis, and control of error , 2001 .

[6]  C. Vörösmarty,et al.  Global water assessment and potential contributions from Earth Systems Science , 2002, Aquatic Sciences.

[7]  Niklaus Wirth.  Good ideas, through the looking glass [computing history] , 2006, Computer.

[8]  E. Gaddis,et al.  Lessons for successful participatory watershed modeling: A perspective from modeling practitioners , 2008 .

[9]  Nuno Videira,et al.  Participatory Modelling in Environmental Decision-Making: The Ria Formosa Natural Park Case Study , 2003 .

[10]  C. Schönwiese,et al.  Overview of Results , 1997 .

[11]  Olli Varis,et al.  Water Resources Development in the Lower Senegal River Basin: Conflicting Interests, Environmental Concerns and Policy Options , 2002 .

[12]  Gerald W. Both,et al.  Object-oriented analysis and design with applications , 1994 .

[13]  O. Shenkar,et al.  The People’s Republic of China , 1994 .

[14]  Anders L. Madsen,et al.  Applications of object-oriented Bayesian networks for condition monitoring, root cause analysis and decision support on operation of complex continuous processes , 2005, Comput. Chem. Eng..

[15]  Deborah J. Armstrong.  The quarks of object-oriented development , 2006, CACM.

[16]  Mark E. Borsuk,et al.  A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis , 2004 .

[17]  Andrea Castelletti,et al.  Bayesian Networks and participatory modelling in water resource management , 2007, Environ. Model. Softw..

[18]  Emilio Custodio,et al.  Aquifer overexploitation: what does it mean? , 2002 .

[19]  J. Zalasiewicz,et al.  Are we now living in the Anthropocene , 2008 .

[20]  P. Crutzen,et al.  The Anthropocene: Are Humans Now Overwhelming the Great Forces of Nature , 2007, Ambio.

[21]  Erik Bohlin,et al.  Infrastructure to 2030 - telecom, land transport, water and electricity , 2006 .

[22]  John A. Harrison,et al.  Sources and delivery of carbon, nitrogen, and phosphorus to the coastal zone: An overview of Global Nutrient Export from Watersheds (NEWS) models and their application , 2005 .

[23]  C. Perrings,et al.  Future challenges , 2007, Proceedings of the National Academy of Sciences.

[24]  Lawrence Stevens.  Artificial intelligence, the search for the perfect machine , 1985 .

[25]  H. Segers,et al.  The Freshwater Animal Diversity Assessment: an overview of the results , 2008, Hydrobiologia.

[26]  D. Strayer,et al.  Freshwater biodiversity conservation: recent progress and future challenges , 2010, Journal of the North American Benthological Society.

[27]  Soumyananda Dinda.  Environmental Kuznets Curve Hypothesis: A Survey , 2004 .

[28]  T. Bayes.  An essay towards solving a problem in the doctrine of chances , 2003 .

[29]  Heidi Christiansen Barlebo,et al.  Reflections on the use of Bayesian belief networks for adaptive management. , 2008, Journal of environmental management.

[30]  G. Brundtland,et al.  Our common future , 1987 .

[31]  E. Sanderson,et al.  The Human Footprint and the Last of the Wild , 2002 .

[32]  T. Karl,et al.  Global climate change impacts in the United States. , 2009 .

[33]  Martinez Austria. Polioptro,et al.  Synthesis of the 4th World Water Forum , 2006 .

[34]  J. Lamarque,et al.  Global Biodiversity: Indicators of Recent Declines , 2010, Science.

[35]  WirthNiklaus.  Good Ideas, through the Looking Glass , 2006 .

[36]  John Bromley,et al.  Guidelines for the use of Bayesian networks as a participatory tool for Water Resource Management , 2005 .

[37]  P. Gleick.  Global Freshwater Resources: Soft-Path Solutions for the 21st Century , 2003, Science.

[38]  Mohammad Reza Nikoo,et al.  Developing real time operating rules for trading discharge permits in rivers: Application of Bayesian Networks , 2009, Environ. Model. Softw..

[39]  Edella Schlager,et al.  Challenges of governing groundwater in U.S. western states , 2006 .

[40]  T. Brooks,et al.  Global Biodiversity Conservation Priorities , 2006, Science.

[41]  David J Marchette.  Bayesian Networks and Decision Graphs , 2003, Technometrics.

[42]  Ryan C. Atwell.  Mediated Modeling: A System Dynamics Approach to Environmental Consensus Building , 2006, Landscape Ecology.

[43]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[44]  Dragan A. Savic,et al.  An evolutionary Bayesian belief network methodology for optimum management of groundwater contamination , 2009, Environ. Model. Softw..

[45]  C. Vörösmarty,et al.  Global water resources: vulnerability from climate change and population growth. , 2000, Science.

[46]  Alfonso Domínguez Padilla.  Gestión integrada de los recursos hídricos de la unidad hidrogeológica mancha oriental mediante la utilizacion de redes bayesianas , 2004 .

[47]  Margaret A. Palmer,et al.  Restoration of Ecosystem Services for Environmental Markets , 2009, Science.

[48]  M. Meybeck.  Global analysis of river systems: from Earth system controls to Anthropocene syndromes. , 2003, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[49]  Grady Booch,et al.  Object-oriented analysis and design with applications, third edition , 2007, SOEN.

[50]  J. R. Lund,et al.  Hydro-economic Modeling in River Basin Management: Implications and Applications for the European Water Framework Directive , 2007 .

[51]  J. Last.  Our common future. , 1987, Canadian journal of public health = Revue canadienne de sante publique.

[52]  Nuno Videira,et al.  Participatory decision making for sustainable development—the use of mediated modelling techniques , 2006 .

[53]  C. Sullivan,et al.  Targeting attention on local vulnerabilities using an integrated index approach: the example of the climate vulnerability index. , 2005, Water science and technology : a journal of the International Association on Water Pollution Research.

[54]  Alfonso Dominguez,et al.  Bayesian networks in planning a large aquifer in Eastern Mancha, Spain , 2007, Environ. Model. Softw..

[55]  Carmen Jordá Such,et al.  Confederación Hidrográfica del Júcar , 1996 .

[56]  G. Allen,et al.  Freshwater Ecoregions of the World: A New Map of Biogeographic Units for Freshwater Biodiversity Conservation , 2008 .

[57]  R. Naiman,et al.  Freshwater biodiversity: importance, threats, status and conservation challenges , 2006, Biological reviews of the Cambridge Philosophical Society.

[58]  Anthony Ricciardi,et al.  Extinction Rates of North American Freshwater Fauna , 1999 .

[59]  André E. Punt,et al.  Information flow among fishing vessels modelled using a Bayesian network , 2004, Environ. Model. Softw..

[60]  Bruce G. Buchanan,et al.  The MYCIN Experiments of the Stanford Heuristic Programming Project , 1985 .

[61]  John Lyons,et al.  Conservation status of imperiled north American freshwater and diadromous fishes , 2008 .

[62]  Antonio Martínez Marín,et al.  Plan hidrológico de la cuenca del Segura , 2006 .

[63]  S. El‐Swaify,et al.  Multiple objective decision making for land, water, and environmental management : proceedings of the First International Conference on Multiple Objective Decision Support Systems (MODSS) for Land, Water and Environmental Management: Concepts, Approaches, and Applications , 1998 .

[64]  Lakhmi C. Jain,et al.  Introduction to Bayesian Networks , 2008 .

[65]  M. Kottelat,et al.  Handbook of European freshwater fishes , 2007 .

[66]  Sakari Kuikka,et al.  Learning Bayesian decision analysis by doing: lessons from environmental and natural resources management , 1999 .

[67]  E. Lopez-Gunn,et al.  Is self-regulation a myth? Case study on Spanish groundwater user associations and the role of higher-level authorities , 2006 .

[68]  Carrie V. Kappel,et al.  Global priority areas for incorporating land–sea connections in marine conservation , 2009 .

[69]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems , 1988 .

[70]  Avi Pfeffer,et al.  Object-Oriented Bayesian Networks , 1997, UAI.

[71]  Reza Kerachian,et al.  Developing monthly operating rules for a cascade system of reservoirs: Application of Bayesian Networks , 2009, Environ. Model. Softw..

[72]  R. Soni.  The International Union for the Conservation of Nature and Natural Resources , 1960, Oryx.

[73]  Carrie V. Kappel,et al.  A Global Map of Human Impact on Marine Ecosystems , 2008, Science.

[74]  Anthony J. Jakeman,et al.  A Bayesian network approach for assessing the sustainability of coastal lakes in New South Wales, Australia , 2007, Environ. Model. Softw..

[75]  O. Brown,et al.  Rising temperatures, rising tensions : climate change and the risk of violent conflict in the Middle East , 2009 .

[76]  Finn Verner Jensen,et al.  Public participation modelling using Bayesian networks in management of groundwater contamination , 2007, Environ. Model. Softw..

[77]  W. Reid,et al.  Millennium Ecosystem Assessment , 2005 .

[78]  R. Naiman,et al.  The challenge of providing environmental flow rules to sustain river ecosystems. , 2006, Ecological applications : a publication of the Ecological Society of America.

[79]  Arlen W. Harbaugh,et al.  A modular three-dimensional finite-difference ground-water flow model , 1984 .

[80]  Carmen Revenga,et al.  Geospatial Indicators of Emerging Water Stress: An Application to Africa , 2005, Ambio.

[81]  J. Bromley,et al.  The use of Hugin® to develop Bayesian networks as an aid to integrated water resource planning , 2005, Environ. Model. Softw..

[82]  M. V. D. Belt,et al.  Mediated Modeling: A System Dynamics Approach To Environmental Consensus Building , 2004 .

[83]  A. F. Adams,et al.  The Survey , 2021, Dyslexia in Higher Education.

[84]  José M. Matías,et al.  Reforestation planning using Bayesian networks , 2009, Environ. Model. Softw..

[85]  J. Ares,et al.  User manual of Visual Balan V. 1.0 Interactive code for water balances and refueling estimation , 1999 .