An improved model of regional flood disaster risk assessment based on remote sensing data

Duan, Guangyao; Zhao, Wenji; Hu, Zhuowei; Fang, Dan

doi:10.1109/geoinformatics.2011.5980776

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/rs16081413/s1, Figure S1 S1: Classification criteria of Proximity Level to river systems; Table S2: Statistics on the flood evolution process over the years 2015-2021; Table S3: Multicollinearity results of factors; Table S4: Model prediction accuracy of land use; Table S5: The validation of response performance of the framework to extreme and regular floods in Huaihe River Basin. References [88][89][90][91][92] are also cited in Supplementary Materials file.…”

Section: Supplementary Materialsmentioning

confidence: 99%

A Novel Flood Risk Analysis Framework Based on Earth Observation Data to Retrieve Historical Inundations and Future Scenarios

Yao,

Yang,

Wang

et al. 2024

Remote Sensing

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Global warming is exacerbating flood hazards, making the robustness of flood risk management a critical issue. Without considering future scenarios, flood risk analysis built only on historical knowledge may not adequately address the coming challenges posed by climate change. A comprehensive risk analysis framework based on both historical inundations and future projections to tackle uncertainty is still lacking. In this view, a scenario-based, data-driven risk analysis framework that for the first time integrates recent historical floods and future risk trends is here presented, consisting of flood inundation-prone and high-risk zones. Considering the Poyang Lake Eco-Economic Zone (PLEEZ) in China as the study area, we reproduced historical inundation scenarios of major flood events by using Sentinel-1 imagery from 2015 to 2021, and used them to build the risk baseline model. The results show that 11.7% of the PLEEZ is currently exposed to the high-risk zone. In the SSP2-RCP4.5 scenario, the risk would gradually decrease after peaking around 2040 (with a 19.3% increase in high-risk areas), while under the traditional fossil fuel-dominated development pathway (SSP5-RCP8.5), the risk peak would occur with a higher intensity about a decade earlier. The attribution analysis results reveal that the intensification of heavy rainfall is the dominant driver of future risk increase and that the exploitation of unused land such as wetlands induces a significant increase in risk. Finally, a hierarchical panel of recommended management measures was developed. We hope that our risk analysis framework inspires newfound risk awareness and provides the basis for more effective flood risk management in river basins.

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Section: Supplementary Materialsmentioning

confidence: 99%

A Novel Flood Risk Analysis Framework Based on Earth Observation Data to Retrieve Historical Inundations and Future Scenarios

Yao,

Yang,

Wang

et al. 2024

Remote Sensing

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“…In Hossain et al, 16 a 5-day forecast of river levels inside Bangladesh was presented. The risk model for a short period (2 or 3 days) is given in Ma et al 17 In Duan et al, 18 a model of regional flood risk assessment with factors including rainfall, topography, water density, and population density is presented, and gross domestic product (GDP) was selected to build the model. Other prediction models were also studied.…”

Section: Related Workmentioning

confidence: 99%

Classification of incoming calls for the capital city of Slovenia smart city 112 public safety system using open Internet of Things data

Grasic

Kos

Boshkoska

2018

International Journal of Distributed Sensor Networks

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There are many natural conditions in the world, such as floods and earthquakes that cause major accidents. The actual question is how to anticipate and predict such events, which enables us to prepare ourselves in advance. We are interested in the advance classification of incoming calls to the smart city 112 public safety system for Ljubljana, the capital city of Slovenia, and the whole country. We used open Internet of Things data for Ljubljana and the whole of Slovenia for the years 2013-2016. Using different classification methods, different models were obtained and compared with each other. We have provided a report on how accurate the forecasts are and what classification result differences occur when different models or data of the capital city or the whole of Slovenia are used. The results obtained are a sound basis for further work on improving the classification in advance of incoming calls to the smart city 112 public safety system.

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“…The Flooding disasters in Songhua River basin is evaluated by Duan Guangyao etc. using the technology of GIS and RS [5] . The flood and drought disasters are evaluated by LiuYaBin etc.…”

Section: Introductionmentioning

confidence: 99%

The Risk Assessment of Cultivated Land Damaged by Flooding Disasters in China

Liu

Wang²,

et al. 2015

AMR

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Flooding disasters not only threat the people's life and property, also affect the safety of environment resources ecosystem, which could make the destructive damage to the cultivated land resources both at quantity and quality. The cultivated land, once damaged, need to invest huge human, material and financial resources to restore its agricultural productivity. Based on analyst the formative factors of cultivated land damaged by flooding disasters, this paper selecting the restrictive factors such as, rainfall, relief amplitude, flooding disasters frequency and water system (including rivers and lakes),combing with the comprehensive evaluation method and putting forward the corresponding countermeasures. The risk of cultivated land damaged by flooding disasters in China is divided into severe risk, moderate risk and light risk level three class and nine subclasses totally. The result shows that: severe risk class covers an area of 194400 km2, accounting for 15.13% of the cultivated land; Moderate risk class covers an area of 407400 km2, accounting for 31.71% of the cultivated land area; Mild risk class covers an area of 682900 km2, accounting for 53.16% of the cultivated land area. Making a risk grading evaluation to the cultivated land damaged by flooding disasters provides the basic theory basis for flooding disaster prevention, disaster relief, land consolidation planning and the establishment of basic farmland construction standards.

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An improved model of regional flood disaster risk assessment based on remote sensing data

Cited by 5 publications

References 2 publications

A Novel Flood Risk Analysis Framework Based on Earth Observation Data to Retrieve Historical Inundations and Future Scenarios

A Novel Flood Risk Analysis Framework Based on Earth Observation Data to Retrieve Historical Inundations and Future Scenarios

Classification of incoming calls for the capital city of Slovenia smart city 112 public safety system using open Internet of Things data

The Risk Assessment of Cultivated Land Damaged by Flooding Disasters in China

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