A comparison of the analytical hierarchy process and the fuzzy logic approach for flood susceptibility mapping in a semi-arid ungauged basin (Biskra basin: Algeria)

Bouamrane, Ali; Derdous, Oussama; Dahri, Noura; Tachi, Salah Eddine; Boutebba, K.; Bouziane, Mohamed Tewfik

doi:10.1080/15715124.2020.1830786

Cited by 45 publications

(30 citation statements)

References 46 publications

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“…Data sources and detailed descriptions of each parameter are shown in Table 7. A detailed description of geophysical and vulnerability parameters are as follows: Elevation (G): Elevation is found as the dominant factor that has a great impact on flooding (Mojaddadi et al, 2017;Bouamrane et al, 2020). Generally, flood hazards are more likely to be found in low altitudes rather than high altitudes.…”

Section: Parameter Selection and Data Processingmentioning

confidence: 99%

“…Soil types were classified into five classes based on plant-available water holding capacity (UC Drought Management, 2021). Land use (G): Land-use influences infiltration, evapotranspiration, and runoff processes; therefore, it considerably impacts a river basin's hydrological cycle (Bouamrane, 2020). Like soil type, land use pattern significantly impacts water permeability capacity.…”

Section: Parameter Selection and Data Processingmentioning

confidence: 99%

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Flood Susceptibility Mapping using Fuzzy Analytical Hierarchy Process for Cedar Rapids, Iowa

Cikmaz¹,

Yildirim²,

Demir³

2022

Preprint

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Floods affect over 2.2 billion people worldwide, and their frequency is increasing at an alarming rate compared to other natural disasters. Presidential disaster declarations have issued increasingly almost every year in Iowa for the past 30 years, indicating that the state is on the rise of flood risk. While significant scientific and technological advancement is becoming available for many flood mitigation activities, their on-the-ground consequences are hampered, among other things, by the lack of tools to quickly integrate the growing data into accessible and usable flood mitigation decisions. A multi-disciplinary approach is required, in which the underlying hydrologic processes that cause floods are closely linked with watershed-level socio-economic functions using effective collaboration tools to ensure community participation in the co-production of mitigation plans while paying attention to socio-environmental justice principles. Considering the existing limitations and needs, we conducted a flood risk assessment by utilizing geophysical and socio-economic datasets for a case study in Cedar Rapids, Iowa. Flood risk outputs are generated based on three main risk groups: geophysical-based flood risk, socioeconomic risk, and combined flood risk. Our results indicate that high- and very-high-risk flood susceptibility zones are primarily located in central urban areas with lower elevations. According to overall results, a large area of Cedar Rapids consists of a medium risk level according to the flood risk map combined with the fuzzy AHP method. The results show that high and very high-risk areas are 16% of the examined area, medium, low and very low-risk areas correspond to 84%. Besides, nearly 40% of the population lives in high to very high flood risk zones.

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Section: Parameter Selection and Data Processingmentioning

confidence: 99%

Section: Parameter Selection and Data Processingmentioning

confidence: 99%

Flood Susceptibility Mapping using Fuzzy Analytical Hierarchy Process for Cedar Rapids, Iowa

Cikmaz¹,

Yildirim²,

Demir³

2022

Preprint

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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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“…After that, the most likely result for the specified input is taken. Common phenomena, such as environmental change, rarely have crisp limits and therefore cannot be approached in the conventional method by means of dual logic (weak or not: 0 or 1) [90]. As a result of fuzzy logic, units are allowed to have a partial association with a given category, which is allocated through bias formulas.…”

Section: Change Detectionmentioning

confidence: 99%

Detection of Urban and Environmental Changes via Remote Sensing

2021

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Rapid climate and environmental change at limited, regional, and general scales have been a major concern for researchers in a number of fields, such as topography, economy, environment, and sustainable development. Changes in land cover and land use have taken into account due to potential impacts on soil depletion, amplified runoff , water balance, and climate change. A detailed understanding of the characteristics of land exploitation and land structure is indispensable for the study of their influences on life and nature. In addition, urban extension is a major form of land extraction and land transformation, as it relates to the rise in population and the availability of financial services. Remote sensing records have been shown to be important for reporting and perceiving urban development and transition, and for providing critical information for future growth. Transformation and shift identification are the tools used to recognise distinctions in a land cover by tracking them at various times. In addition, various change identification and detection approaches are routinely tested with the goal of providing the greatest change detection deductions for a particular appliance. This review would aim to establish a practical plan that combines remote sensing techniques, on the one hand, and modelling approaches, on the other, to track land use, to cover changes, and to predict future trends.

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A comparison of the analytical hierarchy process and the fuzzy logic approach for flood susceptibility mapping in a semi-arid ungauged basin (Biskra basin: Algeria)

Cited by 45 publications

References 46 publications

Flood Susceptibility Mapping using Fuzzy Analytical Hierarchy Process for Cedar Rapids, Iowa

Flood Susceptibility Mapping using Fuzzy Analytical Hierarchy Process for Cedar Rapids, Iowa

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

Detection of Urban and Environmental Changes via Remote Sensing

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