Geodetic evidence of land subsidence in Cirebon, Indonesia

Bramanto, Brian; Gumilar, Irwan; Sidiq, Teguh P.; Rahmawan, Yan A.; Abidin, Hasanuddin Z.

doi:10.1016/j.rsase.2023.100933

Cited by 4 publications

(5 citation statements)

References 48 publications

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“…Based on past research [25,26,[44][45][46][47][48][49][50][51][52], LS is considered by four different overarching criteria, including hydrometeorological (5 variables), geological (3 variables), Geomorphological (13 variables), and human-caused (6 variables) (Table 1). Hydrometeorological criteria (e.g., a decline in groundwater level (DGWL), depth of groundwater (DTGW), flow accumulation, flow direction, and climate type) are dominant determinants of LS occurrence [25,45,47,[51][52][53].…”

Section: Causes Of Land Subsidencementioning

confidence: 99%

Land Subsidence Susceptibility Mapping Using Interferometric Synthetic Aperture Radar (InSAR) and Machine Learning Models in a Semiarid Region of Iran

Gharechaee

Samani

Sigaroodi

et al. 2023

Land

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Most published studies identify groundwater extraction as the leading cause of land subsidence (LS). However, the causes of LS are not only attributable to groundwater extraction. Other land-use practices can also affect the occurrence of LS. In this study, radar interferometric techniques and machine learning (ML) models were used for the prediction, susceptibility zoning, and prioritization of influential variables in the occurrence of LS in the Bakhtegan basin. The LS rate was characterized by applying an interferometric synthetic aperture radar (InSAR). The recursive feature elimination (RFE) method was used to detect and select the dominant combination of indicators to prepare an LS susceptibility map. Three ML models, including random forest (RF), k-nearest neighbors (KNN), and classification and regression trees (CART), were used to develop predictive models. All three models had acceptable performance. Among the ML models, the RF model performed the best (i.e., Nash–Sutcliffe efficiency, Kling–Gupta efficiency, correlation coefficient, and percent bias metrics of 0.76, 0.78, 0.88, and 0.70 for validating phase, respectively). The analysis conducted on all three ML model outputs showed that high and very high LS susceptibility classes were located on or near irrigated agricultural land. The results indicate that the leading cause of land LS in the study region is not due to groundwater withdrawals. Instead, the distance from dams and the proximity to anticlines, faults, and mines are the most important identifiers of LS susceptibility. Additionally, the highest probability of LS susceptibility was found at distances less than 18 km from synclines, 6 to 13 km from anticlines, 23 km from dams, and distances less than 20 to more than 144 km from mines. The validated methods presented in this study are reproducible, transferrable, and recommended for mapping LS susceptibility in semiarid and arid climate zones with similar environmental conditions.

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Section: Causes Of Land Subsidencementioning

confidence: 99%

Land Subsidence Susceptibility Mapping Using Interferometric Synthetic Aperture Radar (InSAR) and Machine Learning Models in a Semiarid Region of Iran

Gharechaee

Samani

Sigaroodi

et al. 2023

Land

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“…The northern coastline regions of Java have been soliciting the attention of many studies because a large portion of land in at least ten cities is subsiding [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] (Fig. 1).…”

Section: Background and Summarymentioning

confidence: 99%

“…Monitoring efforts to study the spatial extent of land subsidence and its rates in these cities have been continuously made using land-based and space-borne techniques [1][2][3][4][6][7][8][9][10][11]15,16,27,30 . Out of the ten cities, land subsidence in Jakarta and Semarang has been the most intensively studied with a long monitoring history.…”

Section: Background and Summarymentioning

confidence: 99%

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GNSS land subsidence observations along the northern coastline of Java, Indonesia

Susilo¹,

Salman

Hermawan

et al. 2023

Sci Data

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Land subsidence in cities along the northern coastline of Java has been at a worrying level. Monitoring efforts using geodetic data reveal that Jakarta, Pekalongan, Semarang, and Demak subside at least ~9x faster than the present-day rate of global sea level rise, which affects the cities’ future urban viability. In this study, we publish a time series of the precise 3D displacements observed by twenty continuous Global Navigation Satellite System (GNSS) stations between 2010 and 2021. These are the first open-to-the-public and rigorously processed GNSS datasets that are useful for accurately quantifying land subsidence in the densely populated sinking cities in Java. The data also provides a way to tie other geodetic observations, such as Interferometric Synthetic Aperture Radar (InSAR), to a global reference frame in an attempt to build worldwide observations of coastal land subsidence.

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“…Globally cities like Jakarta, Middle East, Bangkok, Cochin, Delhi, Osaka, Houston, San Jose, Shanghai, Newyork (1.6mm/year), Tokyo, and Venice are the observed LS zones having major shrinking, sinking, and subsiding zones, [10,11]. Coastal towns like (Kolkata, & Khulna) within the Ganga Brahmaputra Delta (GBMD), the Nile Delta, the Mekong River Delta (Hanoi), the California Bay coasts (Mexico City), and the cities underneath oil and carbon basins in Iran, and Northeast China are undergoing substantial LS [12,13,14,15,16,17].…”

Section: Review Of Literaturementioning

confidence: 99%

The Geohazard as Land Subsidence in Anthropocene, India

Mishra

2023

JGEESI

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Land subsidence events, natural or anthropogenic, will be future national challenges for India. The positioning of the Indian plate in the globe, and human activities on the frustum compel land subsidence everywhere, on mountains, in deltas, riverbeds and in settlements. Many mega deltas of the globe including India are sinking, shrinking, and subsiding which shall invite catastrophes. The present search uses the available datasets in print media, books, information technology and electronic media. The survey responses sourced from websites were physically ground verified, etc. Considering Bhuban data, Arc GIS software and Microsoft Excel, India's hazard, organic and inorganic maps are prepared to alert our forthcoming ancestors. Using those maps the zonal map of geo-hazards in various states of India can be prepared and future action plans can be shorted out. Studies reveal that geo-hazards in various zones of India are different. Hazards on mountains are land slide, torrential rain, and land subsidence, whereas the calamities along the coasts, and deltas are cyclones, coastal erosions, floods, and deltaic subsidence. With surging human activities, the frequency and intensity of geo-hazards are swelling presently. It is high time to plan to appease the vulnerability of the rising devastation to attain sustainability of SDG Targets 11.5, and 13 which will moderate the adverse effects of such disasters.

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Geodetic evidence of land subsidence in Cirebon, Indonesia

Cited by 4 publications

References 48 publications

Land Subsidence Susceptibility Mapping Using Interferometric Synthetic Aperture Radar (InSAR) and Machine Learning Models in a Semiarid Region of Iran

Land Subsidence Susceptibility Mapping Using Interferometric Synthetic Aperture Radar (InSAR) and Machine Learning Models in a Semiarid Region of Iran

GNSS land subsidence observations along the northern coastline of Java, Indonesia

The Geohazard as Land Subsidence in Anthropocene, India

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