Intelligent Geo-Information Systems (IGIS) for Water Resources Planning and Management

  • Uwe Arnold
  • Gerhard Rouve
Part of the NATO ASI Series book series (volume 26)


Most of the concepts and actual data presented in this paper are the result of a recently finished joint research project at Aachen University of Technology. The main goal of this project was to identify domains of high application potential for knowledge-based systems (KBS) in water resources planning and management for the benefit of water resources protection. For this purpose, a state-of-the-art review, a system analysis, a rigorous demand investigation, and a feasibility study were carried out. In addition, a basic system concept (hardware and software) for fulfilling the requirements of the application domain was outlined. The literature review led to the identification of 55 related KBS application proejcts. To analyze the demand for KBS in the water resources field, about 2000 questionnaires were distributed (with an extraordinary response rate of more than 15%), and 30 in-depth interviews were carried out with experts in the field. The results, which are summarized in this paper, include information about the state of computer application, frequently occurring problems, and the attitude towards knowledge processing technology among practitioning engineers. Moreover, the interviews led to the identification of more than thirty possible application scenarios. Three of those being considered both most promising and thematically interrelated were chosen as pilot-scenarios for the feasibility analysis.

Common user requirements for knowledge-based systems in the domain of water resources protection are: these tools should be economical, i.e. not “overloaded,” extendable, compatible among each other and, above all, capable of being integrated into the existing and well-established computational “world.” Therefore, the creation of water resources KBS should be coordinated by means of a common development basis. Taking this into account, the basic system concept, as outlined during the project, features a generic knowledge base for water resources problems. This knowledge base contains and structures objected-oriented representations for general water resources entities and their interrelations (object-oriented data base concept). The generic knowledge base can serve as a common development platform for application and problem-specific knowledge processing tools. Moreover, with respect to the requirements of full integration into the conventional data processing world, an object-oriented approach was chosen for the linkage of application-specific KBS components with a GIS, an interactive graphics user environment, and an object-oriented data base corresponding with frame-based KBS components. The result of this concept which is discussed within this paper is an “Intelligent Geo-Information System” (1015) for water resources applications. The feasibility of the system concept was investigated by a professional computer consulting company with special attention to the application-specific requirements of the pilot scenarios. The final evaluation of the system feasibility was positive. In order to illustrate the system concept and its application potential, a demonstration prototype was developed on a hypermedia and micro basis.


Water Resource Application Scenario System Concept Knowledge Processing Water Resource Planning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Arnold, U., B. Datta and P. Hanscheid (1989). Intelligent Geographic Information Systems (IGIS) and Surface Water Modeling. Proceedings, Third Scientific Assembly of the International Association of Hydrological Sciences, Baltimore, MD, May 1989, pp. 407–416.Google Scholar
  2. Arnold, U. and G.T. Orlob (1989). Decision Support for Estuarine Water Quality Management. J. of Water Resources Planning and Management, ASCE, 115 (6), pp. 775–792.CrossRefGoogle Scholar
  3. Arnold, U., E. Ritterbach and G. Rouve (1989). Expert Systems for Flood Damage Prevention. Proceedings, UNCRD 3rd IntnL Research and Training Seminar on Regional Development Planning for Disaster Prevention, Nagoya, Japan, September 5 - 7.Google Scholar
  4. Arnold, U. (1989). Intelligente Informationssysteme. 2. IVWK-Fortbildungslehrgang Wasserwirtschaft — EDV in der Wasserwirtschaftl-ichen Planungs - und Verwal- tungspraxis, Aachen, September 25 - 29.Google Scholar
  5. Barr, A. and E.A. Feigenbaum (1981). The Handbook of Artificial Intelligence, Vol 1. Pitman, 409 pp.Google Scholar
  6. Barr, A. and E.A. Feigenbaum (1982). The Handbook of Artificial Intelligence, Vol 2. Pitman, 428 pp.Google Scholar
  7. FAW (1990). Proceedings zum FAW-Workshop “Umweltinformatik 1990. ” 1.-5. Okt. 1990, FAW-Forschungsinstitut fur Anwendungsorientierte Wissensverarbeitung, Ulm, 1989 (in Vorbereitung).Google Scholar
  8. Frost, R.A. (1986). Introduction to Knowledge Base Systems. Collins, London.Google Scholar
  9. Graham, I. and P.L. Jones (1987). A Theory of Fuzzy Frames. Proceedings, Conference on Expert Systems ‘87, Brighton, December 14–17, pp. 77–88.Google Scholar
  10. Infovation (1989). Expertensystem-Software-Markt 89. Auszuge aus einer Studie im Auftrag der GMD — Gesellschaft fur Mathematik und Datenverarbeitung, interner Bericht, ca. 230 Seiten, Infovation, Bonn.Google Scholar
  11. Kostem, C.N. and M.L. Maher (1986). Expert Systems in Civil Engineering. American Society of Civil Engineers, New York, NY.Google Scholar
  12. Palmer, R.N. (1985). A Review of Artificial Intelligence. Proceedings, ASCE Specialty Conference on Computer Applications in Water Resources, Buffalo, NY, June 10-12, pp. 591–600.Google Scholar
  13. Parsaye, K., M. Chignell, S. Khoshafian and H. Wong (1989). Intelligent Databases. John Wiley & Sons, New York, NY.Google Scholar
  14. Rouve, G. (Hrsg.), U. Arnold (unter Mitarbeit von), D. Bungers, T. Hemmann, R. Honert and R. Otterpohl (1990). Anforderungen an Expertensysteme fur den Gewasserschultz. Research Report to be published with Deutscher Universitatsverlag.Google Scholar
  15. Shapiro, S.C. and D. Eckroth (1987). Encyclopedia of Artificial Intelligence, Vols. 1 and 2. John Wiley & Sons, Inc., New York, NY.Google Scholar
  16. Simonovic, S.P. (1989). Expert System Design of an Intelligent Decision Support for Surface Water Quantity Data Management. Proceedings, Hydrocomp ‘89, Dubrovnik, Yugoslavia, June 13–16, pp. 383–93.Google Scholar
  17. Simonovic, S.P. and D.A. Savic (1989). Intelligent Decision Support and Reservoir Management and Operations. J. of Computing in Civil Engineering, ASCE, 3 (4), pp. 367–386.CrossRefGoogle Scholar
  18. Zadeh, L.A. (1985). The Role of Fuzzy Logic in the Management of Uncertainty in Expert Systems. In: Approximate Reasoning in Expert Systems, Gupta et al. (eds.), Elsevier Science Publ.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • Uwe Arnold
    • 1
  • Gerhard Rouve
    • 1
  1. 1.Institute for Hydraulic Engineering and Water Resources DevelopmentAachen University of TechnologyAachenGermany

Personalised recommendations