PREDICTIVE MODEL FOR EARLY WARNING SYSTEM FOR HOTSPOT OCCURRENCE UTILIZING SPATIO-TEMPORAL AND PROBIT REGRESSION MODEL BASED ON PEATLAND CHARACTERISTICS AND PRECIPITATION
Spatial mapping in hotspot modeling typically does not account for temporal effects. This limitation becomes significant when time information is a crucial factor, such as in early warning systems. The formulation of a probit regression model with covariates following a space-time process, such a...
Saved in:
| Main Author: | |
|---|---|
| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/84181 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Spatial mapping in hotspot modeling typically does not account for temporal
effects. This limitation becomes significant when time information is a crucial
factor, such as in early warning systems. The formulation of a probit regression
model with covariates following a space-time process, such as a combination of
the Generalized Space-Time Autoregressive (GSTAR) model and kriging methods,
can address this issue by producing predictions of hotspot probabilities for future
times. The GSTAR model functions as an extrapolator, providing time-based
information, while the kriging model offers spatial estimates for the observation
area, thereby completing the data set for subsequent input into the regression model
to generate probability values. Furthermore, temporal information necessitates
revising the assumption of independence among observations, requiring the
Generalized Estimating Equation (GEE) method for estimating the parameters
of the modified probit regression model. The results of this predictive model
construction demonstrate a fairly good performance in mapping the risk of hotspot
occurrence in Riau Province based on peatland characteristics and rainfall. |
|---|