RISK MAPPING OF GROUNDWATER LEVEL CHANGES IN PEATLAND AREA UTILIZING A SPATIO-TEMPORAL MODEL WITH WEIGHT CONSTRUCTED BASED ON MINIMUM SPANNING TREE
Space-time extrapolation models are usually constrained to a limited number of observed locations and lack the ability to provide information about the values at unobserved locations. However, integrating these models with spatial interpolation techniques, it is possible to obtain more informativ...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/73062 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Space-time extrapolation models are usually constrained to a limited number of
observed locations and lack the ability to provide information about the values
at unobserved locations. However, integrating these models with spatial interpolation
techniques, it is possible to obtain more informative visual representations.
The Generalized Space-Time Autoregressive (GSTAR) model, as a multivariate
space-time extrapolation model, is often used due to its simplicity and costeffectiveness.
Within the framework of the GSTAR model, a crucial component is
the spatial weight matrix, which facilitates the establishment of spatial relationships
among different locations. This matrix can be constructed by employing
graph theory, particularly Minimum Spanning Tree (MST), as an extension of the
model. Additionally, spatial interpolation can be achieved through the utilization of
kriging methods. The amalgamation of these two models exhibits superior performance
compared to univariate time series models in risk mapping, particularly in
the context of groundwater level increments observed in peatland areas within Riau
Province, Indonesia. |
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