Land Subsidence Hazard and Building Collapse Risk in the Coastal City of Lagos, West Africa

Ohenhen, Leonard; Shirzaei, M.

doi:10.1029/2022ef003219

Cited by 19 publications

(11 citation statements)

References 48 publications

(89 reference statements)

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“…refs. 29 , 30 , 43 , 44 ). We employed the angular distortion ( β ) to assess the hazard of differential subsidence for the US east coast (E q.…”

Section: Resultsmentioning

confidence: 99%

“…Following Cigna and Tapete ( 29 ), we define four categories indicative of the increasing hazard severity, viz: low (

13000

), medium (

1300011500

), high (

115001500

), and very high (

1500

). High β values identify areas of high subsidence gradients between adjacent points and indicate a higher likelihood of damage ( 29 , 43 , 44 ).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Slowly but surely: Exposure of communities and infrastructure to subsidence on the US east coast

Ohenhen,

Shirzaei,

Barnard

2023

PNAS Nexus

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Coastal communities are vulnerable to multihazards, which are exacerbated by land subsidence. On the US east coast, the high density of population and assets amplifies the region's exposure to coastal hazards. We utilized measurements of vertical land motion rates obtained from analysis of radar datasets to evaluate the subsidence-hazard exposure to population, assets, and infrastructure systems/facilities along the US east coast. Here, we show that 2,000 to 74,000 km2 land area, 1.2 to 14 million people, 476,000 to 6.3 million properties, and >50% of infrastructures in major cities such as New York, Baltimore, and Norfolk are exposed to subsidence rates between 1 and 2 mm per year. Additionally, our analysis indicates a notable trend: as subsidence rates increase, the extent of area exposed to these hazards correspondingly decreases. Our analysis has far-reaching implications for community and infrastructure resilience planning, emphasizing the need for a targeted approach in transitioning from reactive to proactive hazard mitigation strategies in the era of climate change.

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“…refs. 29 , 30 , 43 , 44 ). We employed the angular distortion ( β ) to assess the hazard of differential subsidence for the US east coast (E q.…”

Section: Resultsmentioning

confidence: 99%

“…Following Cigna and Tapete ( 29 ), we define four categories indicative of the increasing hazard severity, viz: low (

13000

), medium (

1300011500

), high (

115001500

), and very high (

1500

). High β values identify areas of high subsidence gradients between adjacent points and indicate a higher likelihood of damage ( 29 , 43 , 44 ).…”

Section: Methodsmentioning

confidence: 99%

Slowly but surely: Exposure of communities and infrastructure to subsidence on the US east coast

Ohenhen,

Shirzaei,

Barnard

2023

PNAS Nexus

View full text Add to dashboard Cite

show abstract

“…Most crucially, unplanned built-up areas, particularly near coastal cities and towns, pose a threat to the region's socio-ecosystems 27 , 28 , 65 , 66 . Rising urbanization and increasing groundwater overexploitation due to the growing population, particularly in coastal cities, are causing subsiding land, building collapse, and increasing flood risk 65 – 67 .…”

Section: Discussionmentioning

confidence: 99%

Coastal vulnerability assessment of the West African coast to flooding and erosion

Dada,

Almar,

Morand

2024

Sci Rep

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Global coastal areas are at risk due to geomorphological issues, climate change-induced sea-level rise, and increasing human population, settlements, and socioeconomic activities. Here, the study examines the vulnerability of the West African (WA) coast using six satellite-derived geophysical variables and two key socioeconomic parameters as indicators of coastal vulnerability index (CVI). These geophysical and socioeconomic variables are integrated to develop a CVI for the WA coast. Then, the regional hotspots of vulnerability with the main indicators that could influence how the WA coast behaves and can be managed are identified. The results indicate that 64, 17 and 19% of WA coastal areas had high to very high CVI, moderate CVI, and low to very low CVI, respectively. The study reveals that while geophysical variables contribute to coastal vulnerability in WA, socioeconomic factors, particularly high population growth and unsustainable human development at the coast, play a considerably larger role. Some sections of the WA coast are more vulnerable and exposed than others, particularly those in the region's northwestern and Gulf of Guinea regions. Climate change and human presence may amplify the vulnerability in these vulnerable areas in the future. Hence, future coastal economic development plans should be based on a deep understanding of local natural conditions, resource status, and geophysical parameters to prevent negative coastal ecosystem transformation. It is also essential to establish a coastal management plan that would facilitate the development of desired actions and stimulate sustainable management of West African coastal areas.

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“…In recent years, land subsidence has been observed in major cities, including Beijing [5] and Shanghai [6]. Land subsidence not only has destructive effects on buildings, roads, and underground pipelines [7] [8] but also leads to surface drainage problems, changes in groundwater quality, and damage to natural ecosystems [9] [10].…”

Section: Introductionmentioning

confidence: 99%

Optimization of land subsidence prediction features based on machine learning and SHAP value with Sentinel-1 InSAR Data

Su,

Xu,

Xion

et al. 2024

Preprint

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Land subsidence has always been a concern of geoscience, and exploring the factors affecting land subsidence to predict future land subsidence is essential research. However, current research rarely has a scientific and unified feature screening process for land subsidence features. This study applies neural networks and SHAP values to land subsidence prediction. We used SHAP values instead of the traditional random forest (RF) to quantify land subsidence features and neural networks to predict the areas where land subsidence is likely to occur in the cities of Chongqing and Chengdu, encompassing the majority of the possible land subsidence scenarios in the future. The results show that the prediction of land subsidence using neural networks improves the model accuracy by 16% compared to the traditional method. After input features optimization, the performance improves by nearly 22%. We found that the feature optimization method based on SHAP values proposed in this study is more helpful for land subsidence prediction, and the factors affecting land subsidence derived from data analysis with complex terrain are also consistent with the results of previous studies. This feature optimization method can contribute to the input variable selection process for the land subsidence prediction model, improve accuracy, and provide solid theoretical support for preventing urban land subsidence.

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Land Subsidence Hazard and Building Collapse Risk in the Coastal City of Lagos, West Africa

Cited by 19 publications

References 48 publications

Slowly but surely: Exposure of communities and infrastructure to subsidence on the US east coast

Slowly but surely: Exposure of communities and infrastructure to subsidence on the US east coast

Coastal vulnerability assessment of the West African coast to flooding and erosion

Optimization of land subsidence prediction features based on machine learning and SHAP value with Sentinel-1 InSAR Data

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