Spatial Dynamics of Tsunami Prone Areas in Pariaman City, West Sumatera

Abstract

Pariaman City is one of cities in Indonesia that has a very high incidence of earthquakes both on land and under the sea. This is caused the Pariaman City region is directly adjacent to the Indian Ocean which is the convergence for two tectonic plates, namely the Eurasian Plate and the Indo-Australian Plate. One of these plates goes down into the other plate then it happens the subduction. Subduction earthquakes that result from convergence two plates very active in generating tsunami waves. This study aims to analyze the spatial dynamics model for tsunami prone areas in Pariaman City by using the Cellular Automata-Markov Chains (CA-MC) method, this method is used to modeling tsunami prone areas in Pariaman City in 2030 based on driving factors that given to models. Driving factors used in this study are elevation, slope, distance from the coastline, distance from the road, and distance from the river. CA-MC presents land cover changes depend on neighboring cells. After the model is generated, then analyzed based on Pariaman City spatial plan in 2030 to be compared. To obtain tsunami prone areas, the prediction model for 2030 land cover is overlaid with tsunami hazard. The results showed that from 2018 to 2030, there was an increase in tsunami prone areas with low, medium and high classes in settlements areas.

Spatial Dynamics Model of Earthquake Prone Area in Ambon City

“…Both of these variables are analyzed by overlay techniques outlined in the Table 7 and Figure 8 analysis. In an effort to mitigate earthquake disasters in Ambon City from the aspect of spatial arrangement, the spatial distribution of earthquake-prone areas is the most important analysis [14]. From the results of the map of earthquake-prone areas obtained from BPBD Ambon City will be covered with land cover map of Ambon City in 2021 and the results of the prediction of land cover in 2031, it can be seen the land built / settlements that are prone to earthquakes.…”

“…After the model is produced, the model accuracy test will be tested using the calculation of OA (Overall Accuracy)/ Kappa Coefficient [38]. From the results of the simulation obtained it is expected that accuracy reaches >70% [14].…”

“…After the modeling results are obtained, the RTRW digitization results are compared to the results of the 2031 land cover model to see the accuracy value. If the accuracy value >70 then it can be used for further analysis [14]…”

Spatial Study of Land Cover Changes and Land Surface Temperature in Bekasi Regency, West Java, Indonesia

“…Land cover predictions for 2020 were validated with a 2020 land cover map using a confusion matrix and calculating the Kappa Index of Agreement. Furthermore, land cover predictions are made in 2028 [12] [13].…”