The application of fuzzy mathematics in physical geography has been thoroughly studied by the landlord ... here, but I have a membership number if I want to download it. I checked the information before and applied for it. I also gave it to you. The account number is 8532748 and the password is 8532940:/soft/zhonghe/ziyuanyuhuanjin/201009/65438. It may be a little hard to find, but it is written here: the application of GIS, artificial intelligence and fuzzy mathematics in the study of physical geography. The concepts of GIS, artificial intelligence technology and fuzzy logic in the course of physical geography No:S07050 1ZY009 Subject: Geography hours/credits: 20/ 1 Introduction to teaching content This course focuses on GIS, artificial intelligence (A.I.) technology and The discussion will focus on the detailed list of natural resources and natural disasters. This course will first introduce the necessity of detailed inventory of natural resources and the vulnerability to disasters. It will highlight the challenges faced by traditional methods of conducting such inventories. Then it introduces how modern spatial information processing theory and technology can help overcome these challenges. The specific cases used in this course are soil resources investigation and landslide susceptibility mapping. The technologies to be discussed include: digital terrain analysis, knowledge acquisition based on personal construction, neural network, case-based reasoning and noise reduction technology of spatial data mining. Each technology will be introduced and discussed through a real application. Participants will also gain practical experience in using certain technologies. Software and real-world data sets will be provided. Bilingual textbooks or reference books Kelly, G.A., 1955, Personal Constructive Psychology (new york: Norton). Kelly, G.A., 1970, Introduction to Personal Construction Theory. Perspectives of Personal Construction Theory, edited by D. Bannister (London: Academic Press), p. 1-29. Kolodna, J. 1993. Case-based reasoning. Morgan Kaufman Press, san mateo, California. Masters, Timothy, 1993. Practical neural network recipes in C++, Academic Press, page 77- 1 16. Miller, H.J. and J.Han, 200 1, Geographic Data Mining and Knowledge Discovery: A Review. Geographic data mining and knowledge discovery, edited by h.j. miller and j.han. Francis), pp. 3-32. Qi, Fang Hui, Zhu Axiang, 2003. Discovering knowledge from soil maps by inductive learning, International Journal of Geographic Information Science, in press. Shi, Xu, Zhu Axiang, Bert, Qi, Simon Song, 2003. Case-based reasoning method for fuzzy soil mapping. Journal of American Soil Science Society, in press. Zhu, a.x., 1999. Knowledge acquisition process of natural resources mapping based on personal construction. International Journal of Geographic Information Science, Volume 13, No.2, Page119-141. Zhu, Xiang, 2000. Taking soil landscape as a spatial continuum diagram: neural network method. Water resources research. A.X. Zhu, B. Hudson, J.E. Burt, K. Lubich, 200 1. "Using GIS, Expert Knowledge and Fuzzy Logic for Soil Mapping", Journal of American Soil Science Society, Volume 65, Page 1463- 1472. Zhu axiang and Dai, 200 1. MacKay. "Influence of Spatial Details of Soil Information on Watershed Modeling", Journal of Water Literature, Vol. 248, pp. 54-77.