annual international conference on gis, gps and remote sensing
TRANSCRIPT
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Annual Annual
International Conference
On GIS , GPS AND Remote Sensing
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R.Naveen Kumar Goud
B.E 4/4 Civil Engineering
Vasavi College Of Engineering
Hyderabad, Andhra Pradesh ,India
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GA Optimization Technique OnGA Optimization Technique On
Spatio-Temporal Interpolation Spatio-Temporal Interpolation
For Dynamic GIS For Dynamic GIS
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abstractabstract • 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.
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Objective of the Study 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 .
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• 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 .
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Genetic AlgorithmGenetic Algorithm
• GA are used as approach to optimization which requires efficient and effective search.
• They combine survival of the fittest among structures .
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five basic aspects 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 .
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Optimization Scheme for Optimization Scheme for Variable InterpolationVariable 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.
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• 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.
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Application of Genetic AlgorithmApplication of Genetic Algorithm
• 3D Representation of an Individual.
• Initialization of Population .
• Definition of Individual's Fitness .
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Representation of an IndividualRepresentation of an Individual
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The S-T relations affect the The S-T relations affect the transitional probability in three waystransitional 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:
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Definition of Individual's FitnessDefinition of Individual's Fitness
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Calculation of Individual's FitnessCalculation 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.
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Definition of OperatorsDefinition 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
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ReproductionReproduction
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CrossoverCrossover
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MutationMutation
• Mutation operator plays a secondary role in the simple GA.
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Hill-Climbing methodHill-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.
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Conclusion and Future ProspectsConclusion 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.
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ReferencesReferences • 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):• http://www.oursland.net/projects/PopulationExperiment/
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Thank You