Place vulnerability assessment based on the HOP model in the middle and lower reaches of the Yangtze River

Guo, Naijing; Tang, Xiaolan; Ren, Yujie; Ma, Kayan; Fang, Jinxiang

doi:10.1007/s10708-019-10092-4

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…On the methodology for constructing the indicator system, there are four main types: directly based on research needs and data availability, on the basis of PSR model [29] [30], the DPSIR model [31] [32], and based on the SFPHD framework [33]. The study area is mainly concentrated in the upper Minjiang River basin [34] [35], the Yellow River's middle and lower reaches [36]- [38], the Yangtze River basin [39] [42], and Beijing Municipality in China [43].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Condition of the Human Habitat Ecosystem in Anhui Province and Regional Spatial and Temporal Distribution Study

Luo,

Guo

2024

Pol. J. Environ. Stud.

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Due to the increasingly serious problems of China's human habitat ecosystem, on the basis of PESTEL paradigm, this study develops a human habitat ecological environment evaluation system with 6 criteria levels and 33 indicator layers, with Anhui Province as a representative. It also combines entropy weight method-TOPSIS, grey correlation analysis method and ArcGIS to assess the quality and spatial-temporal distribution features of the habitat ecological environment in the province of Anhui from 2012 to 2021. The results are presented below (1) Analysed from a temporal perspective, in Anhui Province, the overall quality of human habitat ecological environment improved significantly between 2012 and 2021, and the comprehensive index increased by 178.95%. (2) From the spatial dimension, Huangshan City and Maanshan City in the south of Anhui Province have the highest level of habitat ecological environment, while Huaibei City and Bozhou City perform the worst. (3) In terms of different indicators, the indicator with the strongest interlink with the humankind habitat ecosystem is the completed investment in the project of treating wastewater (PO5), and the indicator with the weakest correlation is the Total amount of wastewater discharged (TE1). The variations in time and space in the evolution and development of the quality of the humankind habitat in the province of Anhui are obvious, and differentiated and targeted measures to improve the human habitat ecological environment should be implemented to effectively promote the excellent creation of Anhui Province.

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Section: Introductionmentioning

confidence: 99%

Evaluation of the Condition of the Human Habitat Ecosystem in Anhui Province and Regional Spatial and Temporal Distribution Study

Luo,

Guo

2024

Pol. J. Environ. Stud.

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“…This condition causes difficulty in implementing hydrological and hydraulic models because the model's accuracy cannot be proven. Instead, scholars implement the watershed physical vulnerability theory by accommodating the physical factors that influence physical flood vulnerability [10].…”

Section: Introductionmentioning

confidence: 99%

“…Feloni et al [12] used one dataset with variations in the weighting of the criteria in the multi-criteria decision-making method to find the best method of weighting the criteria. Deepak et al [10] and Hussain et al [13] also used one dataset to find the effect of flood vulnerability criteria on physical flood vulnerability. Using one dataset, the assessment results should only represent one physical condition in the study area.…”

Section: Introductionmentioning

confidence: 99%

Flood Vulnerability Evaluation and Prediction Using Multi-temporal Data: A Case in Tangerang, Indonesia

Santosa¹,

Martono²,

Purwana³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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Land-use change has an impact on growing physical flood vulnerability. Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) approaches are increasingly being used for flood vulnerability assessments. However, none has used timeseries land cover data for evaluation and rainfall over various return periods for prediction simultaneously, especially in Indonesia. Therefore, this study aims to evaluate and predict physical flood vulnerability using time-series land cover data and rainfall data over various return periods. Eight criteria were considered in the assessment: elevation, topographic wetness index, slope, distance to the river, distance downstream, soil type, rainfall, and land cover. The criteria weights were determined using the AHP method based on expert judgment. The multi-criteria model was built and validated using flood inundation data. Based on the validated model, the effect of land cover changes on flood vulnerability was evaluated. The flood vulnerability changes were also predicted based on rainfall over various return periods. The evaluation and prediction models have shown reliable findings. The criterion elevation and distance to the river significantly influenced the physical flood vulnerability by 41% and 20%. The evaluation model showed a strong correlation between the built-up area and the area with high flood vulnerability (r 2 = 0.96). Furthermore, the model predicted an inundation area expansion for rainfall over various return periods. Further research using spatial data with higher resolution and more advanced validation techniques is needed to improve the model accuracy.

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Understanding household flood resilience in Tangerang, Indonesia, using a composite indicator method

Santosa,

Martono,

Purwana

et al. 2023

Nat Hazards

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Place vulnerability assessment based on the HOP model in the middle and lower reaches of the Yangtze River

Cited by 5 publications

References 11 publications

Evaluation of the Condition of the Human Habitat Ecosystem in Anhui Province and Regional Spatial and Temporal Distribution Study

Evaluation of the Condition of the Human Habitat Ecosystem in Anhui Province and Regional Spatial and Temporal Distribution Study

Flood Vulnerability Evaluation and Prediction Using Multi-temporal Data: A Case in Tangerang, Indonesia

Understanding household flood resilience in Tangerang, Indonesia, using a composite indicator method

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