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Annual International Conference On GIS, GPS AND Remote Sensing.

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Presentation on theme: "Annual International Conference On GIS, GPS AND Remote Sensing."— Presentation transcript:

1 Annual International Conference On GIS, GPS AND Remote Sensing

2 R.Naveen Kumar Goud B.E 4/4 Civil Engineering Vasavi College Of Engineering Hyderabad, Andhra Pradesh,India

3 GA Optimization Technique On Spatio-Temporal Interpolation For Dynamic GIS

4 abstract Generating cross-sectional data of arbitrary time slice is a basic function to support temporal operations such as time- series analysis and integration of dynamic models in GIS environment. Here we propose a interpolation scheme for class variable data under the framework of optimization of likelihood.

5 Objective of the Study Dynamic analysis of spatial data are needed in various fields. Difficult to generate spatio-temporal filed of quality data for analysis. Quality data, models describing structure is integrated with observational data.

6 Integration methods for data and models have been mainly developed for continuous variables in meteorology and oceanography. For class variables such as land use types, there are primitive interpolation methods,nearest neighbor interpolation.

7 Genetic Algorithm GA are used as approach to optimization which requires efficient and effective search. They combine survival of the fittest among structures.

8 five basic aspects the representation of problem, the initialization of population, the definition of evaluation function, the definition of genetic operators, the determination of parameters.

9 Optimization Scheme for Variable Interpolation S-T data can be divided into two types: continuous VD & class VD. The estimation of time of changes according to "class boundary distance". we go to integrate observational class data with structural models to make robust and reliable spatio-temporal interpolation of class variables.

10 Searching for spatio-temporal field of class data is typical combinatorial optimization problem, we introduce the genetic algorithm as a optimization scheme. The likelihood is computed based on both the fitness to observational data and that to behavioral models.

11 Application of Genetic Algorithm 3D Representation of an Individual. Initialization of Population. Definition of Individual's Fitness.

12 Representation of an Individual

13 The S-T relations affect the transitional probability in three ways Spatial Continuity: Assumption that the same class data tends to continue in spatial dimension. Temporal Continuity: This is an extension of spatial continuity to temporal domain. The third aspect is Expansion-Contraction relations:

14 Definition of Individual's Fitness

15 Calculation of Individual's Fitness Individual's fitness has two parts: behavioral fitness & observational fitness. By multiplying behavioral fitness and observational fitness, overall fitness can be computed. To integrate behavioral models and observational data, the overall fitness has to be optimized.

16 Definition of Operators Reproduction : This is a process in which individual strings are copied according to their objective function values or the fitness values That strings with a higher value have a higher probability of contributing one or more offspring in the next generation

17 Reproduction

18 Crossover

19 Mutation Mutation operator plays a secondary role in the simple GA.

20 Hill-Climbing method That exploits the best among known possibilities for finding an improved solution. Although Hill-Climbing strategies is easy to trap in one of local maxima more far away from the optimal solution.

21 Conclusion and Future Prospects Conclusion and Future Prospects GA/HC can be very rigorous because it can generate the most likely spatio- temporal distribution of class variables under observational data and a behavioral model. Hill-Climbing method can be effective method to greatly improve the efficiency of GA.

22 References Bramlette, M.F. (1991): Initialization, Mutation and Selection Methods in Genetic Algorithms for Function Optimization, Davis, L. (1987) : Genetic Algorithms and Simulated Annealing, Eshelman, L.J. and J.D.Schaffer (1991): Preventing Premature Convergence in Genetic Algorithms by Preventing Incest. Goldberg, D.E. (1989) : GENETIC ALOGRITHMS in Search, Optimization and Machine Learning. Gold, C.M.(1989): Surface interpolation, spatial adjacency and GIS, Three Dimensional Applications in Geographic Information System, Huang, S.B. and R.Shibasaki(1995): Development of Genetic Algorithm /Hill-climbing Method for Spatio-temporal Interpolation,. Shibasaki,R., T.lto and Y.Honda (1993):

23 Thank You


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