Scientific Research Based Optimisation and Geo-information Technologies for Integrating Environmental Planning in Disaster Management

Natural and environmental disasters have profound social, economic, psychological, and demographic effects on the stricken individuals and communities. The literature of disaster management of the 21th Century has pointed out that there is a missing part in the knowledge, scientific research, and technological development that can optimise disaster risk reduction. With the improvement of dynamic optimisation and geo-information technologies, it has become very important to determine optimal solutions based on the stability and accuracy of the measurements that support disaster management and risk reduction. However, a scientific approach to the solution of these disasters requires robotic algorithms that can provide a degree of functionality for spatial representation and flexibility suitable for quickly creating optimal solution that account for the uncertainty present in the changing environment of these disasters. Moreover, the volume of data collected for these disasters is growing rapidly, and sophisticated means to optimise this volume in a consistent, dynamic and economical procedure are essential. This chapter effectively links wider strategic aims of bringing together innovative ways of thinking based on scientific research, knowledge and technology in many scientific disciplines to providing optimal solutions for disaster management and risk reduction. Real-life applications using these disciplines will be presented.

[1]  N. Ward.  Understanding GPS — Principles and Applications . Elliott D. Kaplan (Editor). £75. ISBN: 0-89006-793-7. Artech House Publishers, Boston & London. 1996. , 1997, Journal of Navigation.

[2]  Conducted by Roger Harris.  An interview with Gabriel Accascina Regional Co-ordinator Asia-Pacific Development Information Programme (APDIP) and United Nations Development Programme (UNDP) Kuala Lumpur, Malaysia , 1999 .

[3]  Hussain Aziz Saleh,et al.  Effective Heuristics for the GPS Survey Network of Malta: Simulated Annealing and Tabu Search Techniques , 2001, J. Heuristics.

[4]  Hussain Aziz Saleh,et al.  The design of the global navigation satellite system surveying networks using genetic algorithms , 2004, Eng. Appl. Artif. Intell..

[5]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[6]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[7]  Andrzej Jaszkiewicz,et al.  Genetic local search with distance preserving recombination operator for a vehicle routing problem , 2003, Eur. J. Oper. Res..

[8]  Lance M. Leslie,et al.  Environmental Modelling and Prediction , 2002 .

[9]  Hussain Aziz Saleh,et al.  An Artificial Intelligent design for GPS Surveying Networks , 2003 .

[10]  Nicholas Frank Pidgeon,et al.  Man-Made Disasters, Second Edition , 1997 .

[11]  Hussain Aziz Saleh.  Ants Can Successfully Design GPS Surveying Networks , 2002 .

[12]  R. Vidal.  Applied simulated annealing , 1993 .

[13]  P. V. Oosterom,et al.  GEO-INFORMATION FOR DISASTER MANAGEMENT , 2008 .

[14]  B. Turner.  Man Made Disasters , 1995 .

[15]  Jon Eisenberg,et al.  Improving Disaster Management: The Role of IT in Mitigation, Preparedness, Response, and Recovery , 2007 .

[16]  Karen K. Kemp.  Environmental modeling with GIS: A strategy for dealing with spatial continuity (93-3) , 1993 .

[17]  J. Feyen,et al.  Water and Urban Development Paradigms : Towards an Integration of Engineering, Design and Management Approaches , 2008 .

[18]  Kurt Fedra,et al.  GIS and Environmental Modeling , 1993 .

[19]  A. Leick.  GPS satellite surveying , 1990 .

[20]  J. Farman,et al.  Late lessons from early warnings: the precautionary principle 1896-2000 , 2002 .

[21]  F. Cuny.  Disasters and Development , 1983 .

[22]  D. Mileti.  Disasters by Design: A Reassessment of Natural Hazards in the United States , 1999 .

[23]  H. Saleh,et al.  HEURISTICS FOR IMPROVED EFFICIENCY IN THE USE OF THE GLOBAL NAVIGATION SATELLITE SYSTEMS FOR ESTABLISHING POSITIONING NETWORKS , 2002 .

[24]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[25]  B. Wisner,et al.  At Risk: Natural Hazards, People's Vulnerability and Disasters , 1996 .

[26]  Hussain Aziz Saleh,et al.  HEURISTIC METHODS FOR DESIGNING A GLOBAL POSITIONING SYSTEM SURVEYING NETWORK IN THE REPUBLIC OF SEYCHELLES , 2002 .

[27]  Michael F. Goodchild,et al.  Gis and Environmental Modeling: Progress and Research Issues , 1996 .

[28]  Hussain Aziz Saleh,et al.  Handicapped Person Transportation: An application of the Grouping Genetic Algorithm , 2006, Eng. Appl. Artif. Intell..

[29]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[30]  Hussain Aziz Saleh,et al.  Near-optimal design of Global Positioning System (GPS) networks using the Tabu Search technique , 2003, J. Glob. Optim..

[31]  Philippe De Maeyer,et al.  Intelligent decision support system based geo-information technology and spatial planning for sustainable water management in Flanders, Belgium , 2009 .