A Spatially Explicit Multi-Criteria Analysis Method on Solving Spatial Heterogeneity Problems for Flood Hazard Assessment

Xiao, Yong; Yi, Shanzhen; Tang, Zhongqian

doi:10.1007/s11269-018-1993-6

Cited by 20 publications

(6 citation statements)

References 37 publications

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“…The details of the model are described by Yu and Coulthard [43]. Therefore, we only focus on the major features of the model, in which the discrete form was given by: q t+∆t = q t − gh t ∆t( ∆(h t +z) ∆x ) (1 + gh t ∆tn 2 q t /h 10/3 t ) (6) where q is the flow per unit width, g is the acceleration due to gravity, z is the bed elevation, h is the water depth, and n is the Manning's roughness coefficient. With reference to a previous study, roughness is represented in this model by the floodplain Manning coefficient n.…”

Section: Flood Inundation Analysismentioning

confidence: 99%

“…From 1950 to 2004, China experienced 125 significant floods, affecting 1.465 billion people and resulting in losses amounting to USD 11.675 billion [5]. Flooding costs account for 3.15% of China's annual gross national economic output [6]. Over the past few decades, the combined effects of climate change and urbanization have led to a rise in the occurrence of extreme rainfall, jeopardizing the lives and property of residents and causing increasing losses [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Flood Risk Assessment of Areas under Urbanization in Chongqing, China, by Integrating Multi-Models

Li,

Gao,

Yin

et al. 2024

Remote Sensing

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In the context of urbanization, frequent flood event have become the most common natural disasters, posing a significant challenge to human society. Considering the effects of urbanization on flood risk is critical for flood risk reduction and reasonable land planning strategies at the city scale. This study proposes an integrated approach based on remote sensing data using CA, Markov, and simplified hydrodynamic (FloodMap) models to accurately and effectively assess flood risk under urbanization. Taking Chongqing City as a case study, this paper analyzes the temporal and spatial variations in land use/land cover (LULC) in 2010, 2015, and 2018 and predicts the LULC for 2030, based on historic trends. Flood risk is assessed by combining the hazard, exposure, and modified vulnerability. The results suggest that the area of built-up land will increase significantly from 19.56% in 2018 to 25.21% in 2030. From 2010 to 2030, the area of medium and high inundation depths will increase by 10 and 16 times, respectively. Flood damage varies remarkably according to the LULC and return period. The expected annual damage (EAD) has been estimated to increase from USD 68 million in 2010 to USD 200 million in 2030. Flood risk is proportional to population and is significantly inversely proportional to socioeconomic level. The approach used here can provide a comprehensive understanding of flood risk and is significant for land-use policymaking and the management of flood control facilities.

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Section: Flood Inundation Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flood Risk Assessment of Areas under Urbanization in Chongqing, China, by Integrating Multi-Models

Li,

Gao,

Yin

et al. 2024

Remote Sensing

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“…AHP [3,12,15,16,[20][21][22]27,29,32,35,36,43,47,48,63,64,75,82,[85][86][87]89,90,92,94,97,98,102,103,105,108,113,114,116,[118][119][120][121][122]124,128,133] [ 42,65,74,96,101,111,117,123] [30] [17,…”

Section: Preparedness Recovery Responsementioning

confidence: 99%

Big Data in Criteria Selection and Identification in Managing Flood Disaster Events Based on Macro Domain PESTEL Analysis: Case Study of Malaysia Adaptation Index

Abdullah

Zainol

Thian

et al. 2022

BDCC

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The impact of Big Data (BD) creates challenges in selecting relevant and significant data to be used as criteria to facilitate flood management plans. Studies on macro domain criteria expand the criteria selection, which is important for assessment in allowing a comprehensive understanding of the current situation, readiness, preparation, resources, and others for decision assessment and disaster events planning. This study aims to facilitate the criteria identification and selection from a macro domain perspective in improving flood management planning. The objectives of this study are (a) to explore and identify potential and possible criteria to be incorporated in the current flood management plan in the macro domain perspective; (b) to understand the type of flood measures and decision goals implemented to facilitate flood management planning decisions; and (c) to examine the possible structured mechanism for criteria selection based on the decision analysis technique. Based on a systematic literature review and thematic analysis using the PESTEL framework, the findings have identified and clustered domains and their criteria to be considered and applied in future flood management plans. The critical review on flood measures and decision goals would potentially equip stakeholders and policy makers for better decision making based on a disaster management plan. The decision analysis technique as a structured mechanism would significantly improve criteria identification and selection for comprehensive and collective decisions. The findings from this study could further improve Malaysia Adaptation Index (MAIN) criteria identification and selection, which could be the complementary and supporting reference in managing flood disaster management. A proposed framework from this study can be used as guidance in dealing with and optimising the criteria based on challenges and the current application of Big Data and criteria in managing disaster events.

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“…Malczewski (2011) used the range sensitivity principle to develop a local version of GIS-WLC model (see also Carter andRinner 2014, Tang et al 2018). Subsequently, a local ordered weighted averaging (OWA) was proposed by Malczewski and Liu (2014), Xiao et al (2018) and Jiao et al (2019). Şalap-Ayça andJankowski (2016, 2018), Jankowski (2018) and Taha et al (2019) advanced GIS-MCA by developing local forms of reference point methods.…”

Section: Spatially Explicit Mcamentioning

confidence: 99%

Emerging trends and research frontiers in spatial multicriteria analysis

Malczewski

Jankowski

2020

International Journal of Geographical Information Science

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A majority of research on Spatial Multicriteria Analysis (SMCA) has been spatially implicit. Typically, SMCA uses conventional (aspatial) multicriteria methods for analysing and solving spatial problems. This paper examines emerging trends and research frontiers related to the paradigm shift from spatially implicit to spatially explicit multicriteria analysis. The emerging trend in SMCA has been spatially explicit conceptualizations of multicriteria problems focused on multicriteria analysis with geographically varying outcomes and local multicriteria analysis. The research frontiers align with conceptual and structural elements of SMCA and pertain to, among others, theoretical frameworks, problem structuring, model parameter derivation, decision problem contextualization, scale representation, treatment of uncertainties, and the very meaning of decision support. The paper also identifies research directions and challenges associated with developing spatially explicit multicriteria methods and integrating concepts and approaches from two distinct fields: GIS and multicriteria analysis.

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A Spatially Explicit Multi-Criteria Analysis Method on Solving Spatial Heterogeneity Problems for Flood Hazard Assessment

Cited by 20 publications

References 37 publications

Flood Risk Assessment of Areas under Urbanization in Chongqing, China, by Integrating Multi-Models

Flood Risk Assessment of Areas under Urbanization in Chongqing, China, by Integrating Multi-Models

Big Data in Criteria Selection and Identification in Managing Flood Disaster Events Based on Macro Domain PESTEL Analysis: Case Study of Malaysia Adaptation Index

Emerging trends and research frontiers in spatial multicriteria analysis

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