Assessment of Aggregation Frameworks for Composite Indicators in Measuring Flood Vulnerability to Climate Change

Choi, Hyun Il

doi:10.1038/s41598-019-55994-y

Cited by 18 publications

(9 citation statements)

References 29 publications

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“…Gerlitz et al 2017;Birkmann et al 2022). We restricted our analysis to hazards and NCP indicators because of the subjectivity of selecting and combining socio-economic factors that may not be relevant to local society needs (Beccari 2016;Il Choi 2019). Following the IPCC framework (Field and Barros 2014), we define a hazard as "the potential occurrence of a natural or human-induced physical event or trend that may cause loss of life, injury, or other health impacts, as well as damage and loss to property, infrastructure, livelihoods, service provision, ecosystems and environmental resources".…”

Section: Introductionmentioning

confidence: 99%

Nature-based solutions for climate change adaptation are not located where they are most needed across the Alps

et al. 2022

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Climate change impacts mountains socio-ecosystems by increasing certain natural disasters and changing Nature's Contributions to People (NCP). Nature-based Solutions (NbS) are increasingly implemented to help local communities adapt to climatic hazards. However, the relevance of their location in relation to those hazards and local NCP has hardly been addressed. In the PORTAL project (Pathways of Transformation in the Alps), we identified and mapped a portfolio of 97 NbS for climate change adaptation in the European Alps. Most NbS addressed drought or soil instability, and aimed to provide multiple NCP simultaneously such as wood production and protective function against landslides. We analysed whether NbS are located where they are the most needed, according to both current and future intensity of the hazards they aim to address and to supply-flow-demand indicators of the NCP they aim to provide. We found that the location of NbS is overall not related to current supply-flow-demand indicators of most NCP, nor to intensity of hazards. Nevertheless, NbS addressing droughts and floods are located in areas where these hazards are more intense, but do not match higher values for NCP indicators. Conversely, NbS aiming to produce wood and to provide protective function against landslides are located in areas with greater levels of these NCP, regardless of the intensity of hazards. These results suggest that hazards and NCP indicators are not the main drivers of NbS implementation. We argue that integrating local climate conditions and current NCP flows is needed to underpin a macro-regional strategy for planning NbS implementation.

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Section: Introductionmentioning

confidence: 99%

Nature-based solutions for climate change adaptation are not located where they are most needed across the Alps

et al. 2022

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“…This multiplicative approach of integrated flood risk assessment was also adopted in many studies (e.g., Allen et al, 2016; Rakib et al, 2017) as it aggregates items of different scales and units. It was also suggested to use the multiplicative equation for the flood risk assessment due to CC, as it allows lesser compensation for low value indicators, whereas the additive approach provides a constant trade‐off among the components (Choi, 2019). Finally, hazard, exposure, vulnerability, and risk were classified into five categories: 0–20, 21–40, 41–60, 61–80, and 81–100 for very low, low, medium, high and very high, respectively.…”

Section: Methodsmentioning

confidence: 99%

Integrated assessment of flood risk in Arial Khan floodplain of Bangladesh under changing climate and socioeconomic conditions

Chyon

Biswas

Mondal

et al. 2023

J Flood Risk Management

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In the assessment of flood risk, the future flood hazard due to climate change is often tied to the present socioeconomic conditions. This makes an implicit assumption that the drivers of risk, other than the hazard, remain constant with time. Therefore, such risk assessment does not provide a realistic outlook for devising plausible mitigation strategies and plans. In this study, flood risk was assessed from an integrated perspective by considering both physical hazard, and socioeconomic exposure and vulnerability-all changing with time.The flood hazard in the Arial Khan River floodplain in the southcentral Bangladesh was simulated with a two-dimensional hydrodynamic model, and the exposure and vulnerability were projected using different statistical techniques. Principal component analysis was conducted to assign weights to the indicators of hazard, exposure, sensitivity, and adaptive capacity. The results show that the flood depth, duration, and extent would increase from the baseline to 2080s under regional concentration pathway (RCP) 2.6 and RCP 8.5 scenarios. The sensitivity and vulnerability would decrease, reflecting an improved adaptive capacity. The low-risk areas could increase from 62% in the baseline to 85%-91% in 2080s depending on the RCPs. The approach followed can be applied elsewhere in developing countries, particularly in riverine floodplain settings. K E Y W O R D S climate change, flood exposure, integrated risk assessment, physical hazard, vulnerability projection 1 | INTRODUCTION Climate change (CC) is one of the world's most pressing environmental concerns, posing threats to both socioeconomic development and human survival. According to the sixth assessment report (AR6) of the Intergovernmental Panel on Climate Change (IPCC), the global surface temperature might rise by more than 2 C by the end of this century for the regional concentration pathway (RCP) 6.0 and RCP 8.5 emission scenarios (IPCC, 2021).

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“…The minmax transformation given to eq. ( 1) is a simple way to satisfy the commensurate measures of each variable [34].…”

Section: A Variable Selection and Data Formattingmentioning

confidence: 99%

Modeling Multioutput Response Uses Ridge Regression and MLP Neural Network with Tuning Hyperparameter through Cross Validation

Nugroho¹,

Handoyo²,

Hsieh³

et al. 2022

IJACSA

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The multiple regression model is very popular among researchers in both field of social and science because it is easy to interpret and have a well-established theoretical framework. However, the multioutput multiple regression model is actually widely applied in the engineering field because in the industrial world there are many systems with multiple outputs. The ridge regression model and the Multi-Layer Perceptron (MLP) neural network model are representations of the predictive linear regression model and predictive non-linear regression model that are widely applied in the world of practice. This study aims to build multi-output models of a ridge regression model and an MLP neural network whose hyperparameters are determined by a grid search algorithm through the cross-validation method. The hyperparameter that produces the smallest RMSE value in the validation data is chosen as the hyperparameter to train both models on the training data. The hyperparameter in question is a combination of learning algorithms and alpha values (ridge regression), a combination of the number of hidden nodes and gamma values (MLP neural network). In the ridge regression model for alpha in the range between 0.1 and 0.7, the smallest RMSE is obtained for all learning algorithms used. While the MLP neural network model specifically obtained a combination of the number of nodes = 18 and gamma = 0.1 which produces the smallest RMSE. The ridge regression model with selected hyperparameters has better performance (in the RMSE and R2 value) than the MLP neural network model with selected hyperparameters, both on training and testing data.

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Assessment of Aggregation Frameworks for Composite Indicators in Measuring Flood Vulnerability to Climate Change

Cited by 18 publications

References 29 publications

Nature-based solutions for climate change adaptation are not located where they are most needed across the Alps

Nature-based solutions for climate change adaptation are not located where they are most needed across the Alps

Integrated assessment of flood risk in Arial Khan floodplain of Bangladesh under changing climate and socioeconomic conditions

Modeling Multioutput Response Uses Ridge Regression and MLP Neural Network with Tuning Hyperparameter through Cross Validation

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