Introduction to model-based resource potential mapping in a GIS environment: rationale, concepts, components, inputs, and outputs. Spatial data analysis in GIS: Spatial data models and management, elements of geoprocessing spatial query and conditional evaluation, reclassification, distance and density estimations, transformations, interpolation and neighbourhood operations, map algebra and mathematical operations, map overlay. Selection of model inputs: quantification of spatial associations, exploratory spatial data analysis, hypothesis testing, generating derivative input map layers. Model-based integration of input spatial data: knowledge-driven, data-driven, and hybrid models, linear and non-linear models - Fuzzy, Bayesian probabilistic, neural network and neurofuzzy.Validation, interpretation and evaluation of output resource potential maps.
Ph.D. interview is scheduled on Nov. 22, 2024
Posted on Oct. 22, 2024
PhD Topics for spring semester (Jan. to Apr. 2025)
Posted on Oct. 3, 2024