Geospatial mapping of Tsunami susceptibility of Karachi to Gwadar coastal area of Pakistan

Aslam, Bilal; Ismail, Sharjeel; Maqsoom, Ahsen

doi:10.1007/s12517-020-05916-4

Cited by 6 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Elevation describes the difference in vertical distance between points and sea level. Elevation determines the level of susceptibility to tsunamis Field (Sinaga et al, 2011;Aslam et al, 2020); the higher the level of susceptibility, the higher the risk of a tsunami; conversely, the lower the topography of an area, the more often an area is submerged by tsunami runoff. The level of tsunami susceptibility based on elevation parameters in the coastal area of Ende City is shown in Table 3.…”

Section: Results and Discussion Typology Of Ende City Coastal Areasmentioning

confidence: 99%

“…These two maps were created by dividing the height and slope of the coast using value grouping (reclassify). The classification of elevation and slope is divided into five classes modified from (Faiqoh et al, 2013;Aslam et al, 2020). Distance from the coastline data uses an RBI map for the Ende Regency area at a scale of 1: 25,000.…”

Section: Data Collection and Analysismentioning

confidence: 99%

“…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%

See 2 more Smart Citations

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

Baru,

Mardiatno,

Marfai

2024

View full text Add to dashboard Cite

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.

show abstract

Section: Results and Discussion Typology Of Ende City Coastal Areasmentioning

confidence: 99%

Section: Data Collection and Analysismentioning

confidence: 99%

See 1 more Smart Citation

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

Baru,

Mardiatno,

Marfai

2024

View full text Add to dashboard Cite

show abstract

“…These hills are separated from each other with a wide coastal plain containing dry riverbeds and water channels. It is the capital of the province Sindh, with a population of 23.5 million living in the urban area [54]. Despite being a coastal area and having cold sea breezes, the city is comparatively hot in a large portion of a year: March to August.…”

Section: Study Areamentioning

confidence: 99%

Urban Overheating Assessment through Prediction of Surface Temperatures: A Case Study of Karachi, Pakistan

Aslam

Maqsoom

Khalid

et al. 2021

IJGI

Self Cite

View full text Add to dashboard Cite

Global climate has been radically affected by the urbanization process in recent years. Karachi, Pakistan’s economic hub, is also showing signs of swift urbanization. Owing to the construction of infrastructure projects under the China-Pakistan Economic Corridor (CPEC) and associated urbanization, Karachi’s climate has been significantly affected. The associated replacement of natural surfaces by anthropogenic materials results in urban overheating and increased local temperatures leading to serious health issues and higher air pollution. Thus, these temperature changes and urban overheating effects must be addressed to minimize their impact on the city’s population. For analyzing the urban overheating of Karachi city, LST (land surface temperature) is assessed in the current study, where data of the past 20 years (2000–2020) is used. For this purpose, remote sensing data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) and Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors were utilized. The long short-term memory (LSTM) model was utilized where the road density (RD), elevation, and enhanced vegetation index (EVI) are used as input parameters. Upon comparing estimated and measured LST, the values of mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE) are 0.27 K, 0.237, and 0.15% for January, and 0.29 K, 0.261, and 0.13% for May, respectively. The low MAE, MSE, and MAPE values show a higher correlation between the predicted and observed LST values. Moreover, results show that more than 90% of the pixel data falls in the least possible error range of −1 K to +1 K. The MAE, MSE and MAPE values for Support Vector Regression (SVR) are 0.52 K, 0.453 and 0.18% and 0.76 K, 0.873, and 0.26%. The current model outperforms previous studies, shows a higher accuracy, and depicts greater reliability to predict the actual scenario. In the future, based on the accurate LST results from this model, city planners can propose mitigation strategies to reduce the harmful effects of urban overheating and associated Urban Heat Island effects (UHI).

show abstract

Geospatial analytics for multi-decadal morphodynamics along Gwadar coastal zone

Faizi

Mushtaq²,

Mahmood³

2022

J Coast Conserv

View full text Add to dashboard Cite

Geospatial mapping of Tsunami susceptibility of Karachi to Gwadar coastal area of Pakistan

Cited by 6 publications

References 19 publications

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

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

Urban Overheating Assessment through Prediction of Surface Temperatures: A Case Study of Karachi, Pakistan

Geospatial analytics for multi-decadal morphodynamics along Gwadar coastal zone

Contact Info

Product

Resources

About