Multicriterial method of AHP analysis for the identification of coastal vulnerability regarding the rise of sea level: case study in Ilha Grande Bay, Rio de Janeiro, Brazil

Araujo, Júlia; Dias, Fábio Ferreira

doi:10.1007/s11069-021-04573-4

Cited by 9 publications

(1 citation statement)

References 43 publications

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“…Various techniques, encompassing physical, statistical, and decision‐making approaches, are applied in the field of FSM including hydraulic and hydrologic modeling, bivariate analysis, and utilization of the multi‐criteria decision making (MCDM) methods (Arabameri et al, 2021; Araujo & Dias, 2021; Costache & Tien Bui, 2020). Commonly, physical hydrodynamic models are used to simulate pluvial flooding.…”

Section: Introductionmentioning

confidence: 99%

A comparative spatial analysis of flood susceptibility mapping using boosting machine learning algorithms in Rathnapura, Sri Lanka

Kurugama,

Kazama,

Hiraga

et al. 2024

J Flood Risk Management

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Identifying flood‐prone areas is essential for preventing floods, reducing risks, and making informed decisions. A spatial database with 595 flood inventory and 13 flood predictors were used to implement five boosting algorithms: gradient boosting machine (GBM), extreme gradient boosting, categorical boosting, logit boost, and light gradient boosting machine (LGBM) to map flood susceptibility in Rathnapura while evaluating trained model's generalizing ability and assessing the feature importance in flood susceptibility mapping (FSM). The model performance was evaluated using the F1‐score, kappa index, and area under curve (AUC) method. The findings revealed that all the models were effective in identifying the overall flood susceptibility trends while LightGBM model had superior results (F1‐score = 0.907, Kappa value = 0.813 and AUC = 0.970), securing the top scores across all performance metrics compared to the other models (for testing dataset). Based on kappa evaluation, most of the models had finer performance (AUC min = 0.737) while LightGBM had moderate performance for predictions beyond the training region. According to the results, regions with lower altitudes and topographic roughness values, moderate rainfall, and proximity to rivers are more susceptible to flooding. This framework can be adapted for rapid FSM in data‐deficient regions.

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Section: Introductionmentioning

confidence: 99%

A comparative spatial analysis of flood susceptibility mapping using boosting machine learning algorithms in Rathnapura, Sri Lanka

Kurugama,

Kazama,

Hiraga

et al. 2024

J Flood Risk Management

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A quantitative priority-based approach using a multi-criteria decision-making model for sustainable coastal city development

Dilman Gokkaya,

Demir,

Aksoy

et al. 2024

Environ Dev Sustain

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Throughout history, water availability has been an essential factor in shaping the development, culture, transportation, economic activities, and access to food in cities. The socioeconomic and cultural development of a city or country with a coast depends on the active use of the coast and the impact of the coast on urban life. In this context, for the development of coastal cities, it is necessary to determine coastal city criteria and specify their strategies in line with these criteria in order to increase coastal qualities. This research determined Bursa, located northwest of Turkey, as the study area. However, factors such as the absence of a coastal plan for the city and the inability of the coast to integrate into urban life have weakened the coastal city’s qualities of it. For this reason, this study aims to define Bursa’s coastal city criteria and determine strategies to increase the coastal city quality. The coastal city criteria developed depending on the examples of coastal cities in the world were evaluated by surveying local people and experts. The results of these surveys were digitized according to their priorities using Analytical Hierarchical Process (AHP), one of the multi-criteria decision-making (MCDM) methods. The results of this study determined the highest priority criteria among the three main and nine sub-coastal city criteria in evaluating the coastal city in Bursa. This study proposes a numerical method that develops effective and sustainable coastal design and planning strategies for Turkey and underdeveloped countries.

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Flood susceptibility prediction using MaxEnt and frequency ratio modeling for Kokcha River in Afghanistan

Qasimi,

Isazade,

Berndtsson

2023

Nat Hazards

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Multicriterial method of AHP analysis for the identification of coastal vulnerability regarding the rise of sea level: case study in Ilha Grande Bay, Rio de Janeiro, Brazil

Cited by 9 publications

References 43 publications

A comparative spatial analysis of flood susceptibility mapping using boosting machine learning algorithms in Rathnapura, Sri Lanka

A comparative spatial analysis of flood susceptibility mapping using boosting machine learning algorithms in Rathnapura, Sri Lanka

A quantitative priority-based approach using a multi-criteria decision-making model for sustainable coastal city development

Flood susceptibility prediction using MaxEnt and frequency ratio modeling for Kokcha River in Afghanistan

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