Natural resources are the basis of human welfare in developing as well as in developed societies. Their prudent and responsible management has to satisfy an ever increasing demand, driven by population growth and changes in lifestyle, and at the same time to meet an increasing set of constraints and concerns of environmental impacts and resource degradation.
All these driving forces of development, acting in concert and often reinforcing each other, make better and more efficient tools for environmental planning and management increasingly important. Better, simpler, but not necessarily more information, directly useful to a larger number of participants, in more open and participatory decision making processes, is urgently needed.
The management of natural resources and environment-related human resources requires:
Information technology, and in particular, the integration of data base management systems, remote sensing and image processing, simulation and multi-criteria optimization models, expert systems, and computer graphics provide some of the tools for effective decision support in natural resources management.
The integration of complex and powerful software tools in problem-oriented systems provides direct and easy access to large volumes of data. It supports their interactive analysis and helps to display and interpret results in a format directly understandable and useful for decision making processes. We think that AI systems can support a more natural, simple, interactive, participatory and effective approach to natural resources planning and management.
At the level of data and background-information, numerous and often incompatible information from disparate sources has to be brought together. Institutional, conceptual, and seemingly simple technical problems, such as different units of measurement, different map projections, hard to trace paper files and missing documentation, will be some of the obstacles frequently encountered.
At the level of tools, there are several levels of integration, ranging from simple file transfer between different methods and programs to fully integrated systems. Typical examples of different methods that lend themselves to integration include geographical information systems and models as well as expert systems; models and data bases; algorithmic models and expert systems; simulation and optimization models.