Validating the GIS-based Flood Susceptibility Model Using Synthetic Aperture Radar (SAR) Data in Sengah Temila Watershed, Landak Regency, Indonesia

DKI Jakarta, the capital city of Indonesia, as the center of growth and development has a high population. The high population has an impact on land use change in an effort to fulfill housing which directly impacts the increase in building density or Normalized Diffrence Built-up Index (NDBI). The purpose of this research is to analyze the rate of land subsidence in DKI Jakarta and analyze the effect of building density on land subsidence. The impact of high growth rates creates the potential for natural and anthropogenic disasters. This research uses the Differential Inferometry Synthetic Aperarure Radar (DinSAR) method, in observing land subsidence with accuracy reaching cm units. The data used in this research is Sentinel 1A image with observation time in 2018, 2020, and 2023. The results showed that land subsidence in DKI Jakarta was dominated in the North Jakarta area, the highest land subsidence value reached -26.08 cm/year. The fastest land subsidence occurs in densely populated areas prone to flooding. The correlation results show that there is no relationship between the high level of building density and land subsidence.

Section: Introductionmentioning

confidence: 99%

Analysis Of Land Subsidance Rates In DKI JAKARTA In 2018, 2020 And 2022 With The DINSAR Method

Zini,

Danardono

2024

“…Geographic information system (GIS) can be used to investigate flood[21][22][23][24]. There were several calculations for population density, population with disabilities, vulnerable age group, poor population, and female population.…”

mentioning

confidence: 99%

Spatial distribution of social vulnerability to flood disasters in Mejobo Sub-District, Kudus Regency, Indonesia

Mujianti,

Hadibasyir,

Renika

et al. 2024

Central Java province has the highest disaster risk index (score 203) of 33 provinces in Indonesia. In 2022, the Province was most affected by floods with 203 incidents. Kudus Regency is one of the regencies in Central Java that is often affected by floods. Mejobo sub-district is the sub-district with the most flood events in the last 5 years. The frequent occurrence of floods triggers various emerging problems such as social vulnerability. Hence, the purpose of this study is to examine social vulnerability and population analysis in the Mejobo sub-district. The calculation of vulnerability classes uses 5 parameters, namely population density, people with disabilities, vulnerable age groups, poor people, and women. which is then scored for each parameter and classified into 3 vulnerability classes (low, middle, and high vulnerability). The results obtained for low vulnerability were found in 3 villages, namely Jojo, Temulus, and Tenggeles Villages. Moderate vulnerability is in the villages of Hadiwarno, Kirig, and Mejobo. In the high vulnerability class, there are 5 villages, namely Golantepus, Jepang, Gulang, Kesambi, and Payaman.

Network analysis for determining the fastest evacuation routes in flood-prone areas of the Tuntang Watershed, Indonesia

Maulana,

Sari

2024

Flooding in the Tuntang Watershed, Central Java Province, is a hydrological disaster that occurs regularly. Several factors, such as high rainfall, limited water catchment areas, and narrowing riverbanks, cause flooding. The amount of material and non-material losses can be minimized through an effective disaster mitigation process. One way is to determine and establish flood evacuation routes to shelters. This research aims to analyze the fastest and most effective evacuation route for residents using Geographic Information Systems. This research process includes analyzing flood-prone areas, ranging from highly vulnerable areas to areas that are not vulnerable. Furthermore, it analyzes population density, distribution of residential areas, natural physical conditions, and road conditions and identifies the location of the best evacuation center for residents affected by flooding. The method used is Sample Additive Weighting (SAW) to determine the best evacuation point and Network Analysis Fastest Route to determine the optimal evacuation route to the designated evacuation center. The results of this study include modeling a flood evacuation route with an estimated effective evacuation time of 30-45 minutes, consisting of 50 fastest alternative routes to appropriate shelters.