Tsunami Susceptibility Analysis in Coastal Area Petanahan District, Kebumen Regency

Safira, Fajar Aniq; Muryani, Chatarina; Tjahjono, Gentur Adi

doi:10.34312/jgeosrev.v4i2.13938

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…As one of the morphometric features, slope angle also has a crucial role in assessing tsunami susceptibility. According to (Safira et al, 2022), tsunami waves can hit land farther up to thousands of meters in areas with a low slope level. Meanwhile, the steep coast resists and reflects the waves from the sea.…”

Section: Results and Discussion Npv And Weight Of Physical Factorsmentioning

confidence: 99%

Tsunami Susceptibility Assessment Using Spatial Multi-Criteria Evaluation in Watukarung, Pacitan

Amri,

Hikmasari,

Nababan

et al. 2023

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Tsunamis are natural hazards that have the potential to cause significant damage and losses to the South Coast of Java. As the initial foundation for local spatial planning and risk reduction, preparing a tsunami susceptibility mapping is imperative to minimize the disaster's impact. This study aims to identify the spatial distribution of tsunami susceptibility in Watukarung Village, Pacitan Regency, using the Spatial Multi-Criteria Evaluation (SMCE) method. The variables involved in the modeling include landform, elevation, slope, distance from the shoreline, and distance from the river. The value and weight of each factor were determined using the pairwise technique in the SMCE framework. The research results indicated that the tsunami susceptibility in Watukarung comprises four classes: safe (598.40 ha), low (9.68 ha), moderate (23.92 ha), and high (25.13 ha). The areas most prone to tsunamis are generally identified in the southern part of Watukarung, which is generally associated with beach or alluvial plain landforms, very close to shore, and low land elevation. Ironically, human settlements and coastal tourism are overgrowing in the tsunami-prone zone, highlighting that risk reduction measures must be implemented optimally to anticipate tsunami hazards. As a recommendation, further research must be carried out to comprehensively represent the tsunami risk in the Watukarung coastal region. Keyword: Tsunami, Susceptibility, Spatial Multi-Criteria Evaluation, Watukarung, Pacitan

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Section: Results and Discussion Npv And Weight Of Physical Factorsmentioning

confidence: 99%

Tsunami Susceptibility Assessment Using Spatial Multi-Criteria Evaluation in Watukarung, Pacitan

Amri,

Hikmasari,

Nababan

et al. 2023

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“…Tsunamis will not travel too far inland on coastal slopes as it is received and reflected by the coastal cliffs. Whereas on a sloping coast, a tsunami can reach several hundred meters inland [33]. In table 7, the vulnerability level of an area is taken based on three measurement indicators, namely KNN, Random Forest, and SVM.…”

Section: Results and Analysismentioning

confidence: 99%

Tsunami Vulnerability and Risk Assessment in Banyuwangi District using machine learning and Landsat 8 image data

Wibowo,

Prasetyo,

Sembiring

2023

matrik

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The tsunami is a disaster that often occurs in Indonesia, there are no valid indicators to assess and monitor coastal areas based on functional land use and based on land cover which refers to the biophysical characteristics of the earth's surface. One of the recommended methods is the vegetation index. Vegetation index is a method from LULC that can be used to provide information on how severe the impact of the tsunami was on the area.In this study, an increase in the vegetation index was carried out using machine learning. The purpose of this study was to develop a tsunami vulnerability assessment model using the Vegetation Index extracted from Landsat 8 satellite imagery optimized with KNN, Random Forest and SVM. The stages of study, are: 1)extraction Landsat 8 images using algorithms NDVI, NDBI, NDWI, MSAVI, and MNDWI; 2) prediction of vegetation indices using KNN, Random Forest, and SVM algorithms. 3) accuracy testing using the MSE, RMSE, and MAE,4) spatial prediction using the Kriging function and 5) tsunami modelling vulnerability indicators. The results of this study indicate that the NDVI interpolation value is 0 - 0.1 which is defined as vegetation density, biomass growth, and moderate to low vegetation health. the NDWI value is 0.02 - 0.08 and the MNDWI value is 0.02 - 0.09 which is interpreted as the presence of surface water along the coast. MSAVI is a value of 0.1 – 0 which is defined as the absence of vegetation. The NDBI interpolation value is -0.05 - (-0.08) which is interpreted as the existence of built-up land with social and economic activities. From the results of research on the 10 areas studied, there are 3 areas with conditions that have a high level of tsunami vulnerability. 2 areas with medium vulnerability and 5 areas with low vulnerability to tsunami.

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“…Run-up tsunami waves will more easily enter land thousands of meters away with low or relatively flat slopes. At the same time, steep beaches will withstand and reflect tsunami waves (Safira et al, 2022;Aslam et al, 2020). The coastal areas of Ende City that are very susceptible are flat to gentle slopes with a slope of 0-6%.…”

Section: Slope Classificationmentioning

confidence: 99%

Tsunami Vulnerability Analysis Based on Coastal Typology in the Coastal Area of Ende City

Baru,

Mardiatno,

Marfai

2024

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A tsunami disaster threatens Ende City. Ende City's coastal area management policy regarding disaster mitigation must consider coastal typology. Efforts to reduce the risk of tsunamis must pay attention to the physical development of coastal land to maintain the coastal resources. Therefore, this research aimed to analyze the level of tsunami susceptibility based on coastal typology in the coastal area of Ende City. The methods used in this research are survey and spatial analysis. The survey results were compared with literature studies to determine coastal typology and land use. Spatial analysis uses overlay, scoring, and weighting techniques to produce tsunami susceptibility levels. Geospatial parameters that determine the level of susceptibility are topography, distance from the coastline, slope, distance from rivers, and coastal morphology. Susceptibility analysis is carried out for each coastal typology because each has a different response to tsunami. The research results show five classes of tsunami susceptibility: very susceptibility, susceptibility, somewhat susceptibility, safe, and very safe. Very susceptible areas are marine deposition coast typology, anthropogenic coast typology, and sub-area deposition coast typology. The secure areas are wave erosion coast typology and volcanic coast typology.Keywords: Coastal City of Ende, Coastal Typology, Tsunami, Susceptibility, Land Use.

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Tsunami Susceptibility Analysis in Coastal Area Petanahan District, Kebumen Regency

Cited by 3 publications

References 13 publications

Tsunami Susceptibility Assessment Using Spatial Multi-Criteria Evaluation in Watukarung, Pacitan

Tsunami Susceptibility Assessment Using Spatial Multi-Criteria Evaluation in Watukarung, Pacitan

Tsunami Vulnerability and Risk Assessment in Banyuwangi District using machine learning and Landsat 8 image data

Tsunami Vulnerability Analysis Based on Coastal Typology in the Coastal Area of Ende City

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