8B_1_A map to hear - use of sound in enhancing the map use experience
5A_1_Land evaluation techniques comparing fuzzy ahp with ideal point methods
1. Geography Department Land Evaluation Techniques Comparing Fuzzy AHP with Ideal Point methodsMukhtar Elaalem Dr: Alexis ComberProf Dr: Pete Fisher http://www.le.ac.uk/geography/staff/pg_elaalem.html
3. 1.Introduction Land resources are gradually becoming limited Increases in population pressure on these natural resources Increased pressure is particularly problematic in countries with restricted water and soil resources such as developing countries Increased food production needed
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7. 2.Methodology/ Model structure Land evaluation model using Fuzzy AHP Convert the raw data (land characteristics) into standardized criterion scores scale using different fuzzy membership function models. Generation standardized criterion map layers Derivation weighted standardized fuzzy criterion map layers. Derivation fuzzy rating map layers Generation the final land suitability map
8. 2.Methodology/ Model structure Land evaluation model using an Ideal Point Determine the maximum and the minimum values for each of the weighted standardized map layer for each land characteristic Using the separation measure to compute “the distance” between the positive ideal point and each alternative An application the similar separation measure to determine “the distance” between the negative ideal point and each alternative Create maps from compute the relative closeness to the ideal point Ranking the alternatives and create the final land suitability map
13. 4. Summary Form this paper it can summarize that : Few areas highly suitable classes have been found from the use the Fuzzy AHP and Ideal Point classifications. Few areas less suitable classes have been found from the use the Fuzzy AHP and Ideal Point classifications Most locations moderate suitableclasses have been found from the use the Fuzzy AHP and Ideal Point classifications There is little differences in the result: An Ideal Point classification has some biasness towards negative and positive ideal values. The high percentages of the KHAT accuracy and an overall accuracy shows that there is a good agreement between the maps.
