Risk Mapping of Groundwater Level in Peatland Area Utilizing a Spatio- Temporal Model with Weight Constructed Based on Minimum Spanning Tree

Abstract

Abstract 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. 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, by gridding the observed spatial locations. Although the amalgamation of these two models does not exhibit superior performance compared to univariate time series models in risk mapping, particularly in the context of groundwater level observed in peatland areas within Riau Province, Indonesia, the model can provide more robust conclusions.