Geoscience Reference
In-Depth Information
Exploring the Role of Genetic Algorithms
and Artificial Neural Networks for
Interpolation of Elevation
in Geoinformation Models
Hossein Bagheri, Seyyed Yousef Sadjadi and Saeed Sadeghian
Abstract These one of the most significant tools to study many engineering
projects is three-dimensional modelling of the Earth that has many applications
in the Geospatial Information System (GIS), e.g. creating Digital Train Modelling
(DTM). DTM has numerous applications in the fields of sciences, engineering,
design and various project administrations. One of the most significant events in
DTM technique is the interpolation of elevation to create a continuous surface.
There are several methods for interpolation, which have shown many results due
to the environmental conditions and input data. The usual methods of interpola-
tion used in this study along with Genetic Algorithms (GA) have been optimised
and consisting of polynomials and the Inverse Distance Weighting (IDW) method.
In this chapter, the Artificial Intelligent (AI) techniques such as GA and Neural
Networks (NN) are used on the samples to optimise the interpolation methods and
production of Digital Elevation Model (DEM). The aim of entire interpolation
methods is to evaluate the accuracy of interpolation methods. Universal interpola-
tion occurs in the entire neighbouring regions can be suggested for larger regions,
which can be divided into smaller regions. The results obtained from applying GA
and ANN individually, will be compared with the typical method of interpola-
tion for creation of elevations. The resulting had performed that AI methods have
a high potential in the interpolation of elevations. Using artificial networks algo-
rithms for the interpolation and optimisation based on the IDW method with GA
could be estimated the high precise elevations.
Search WWH ::




Custom Search