Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/56527
Título: Allocation of flood drainage rights in the middle and lower reaches of the Yellow River based on deep learning and flood resilience
Palavras-chave: Flood drainage rights
Flood resilience strategy
Deep learning
Variational autoencoder
Harmonious evaluation
Yellow River - China
Enchentes - Controle
Drenagem pluvial
Inundações - Estratégia resiliente
Aprendizado profundo
Autocodificador variacional
Data do documento: Dez-2022
Editor: Elsevier
Citação: ZHANG, K. et al. Allocation of flood drainage rights in the middle and lower reaches of the Yellow River based on deep learning and flood resilience. Journal of Hydrology, Amsterdam, v. 615, 128560, Dec. 2022. DOI: https://doi.org/10.1016/j.jhydrol.2022.128560.
Resumo: Climate change has increased the intensity and frequency of storms in many world regions, calling for new flood planning and management strategies. The concept of flood drainage rights (FDR), or the legal rights of regions to drain floodwaters into river reaches, is used in watershed planning in China. Quantifying the allocation of FDR remains challenging, where some previous methods have resulted in unreasonable or impractical allocation plans due to incomplete consideration of driving factors or the use of unscientific allocation methods. This study explores the allocation plan of FDR in the middle and lower reaches of the Yellow River Watershed in China. Climatic variability and change have caused frequent flooding in portions of the basin, with significant societal and economic implications. First, we comprehensively analyzed factors driving FDR for regions in the watershed. Following the conceptual flood resilience strategy currently being advocated for the region, we considered natural, socioeconomic, governance, resilience, and resistance factors that influence the complex allocation of FDR and established a qualitative indicator system to reflect the complexity of these driving factors. Second, we quantified FDR values for flood-prone regions in the middle and lower river reaches of this major river basin. We introduced a specific deep learning method, called the variational autoencoder (VAE) model, to quantify FDR allocation, providing a robust solution to the challenge of the multi-objective, high-dimensional, nonlinear, and non-normal distribution of factors driving FDR allocation. Next, using data from 2005 to 2019, this model was applied to the study area. The allocation of FDR (summing to 100%) across five flood-prone provinces of the watershed includes Inner Mongolia (9.36%), Shaanxi (10.00%), Shanxi (10.95%), Henan (32.58%), and Shandong (37.12%). Using the harmony evaluation method based on harmony theory, we compared the new VAE allocation method with three conventional allocation methods. The VAE allocation method has the highest degree of harmony, suggesting that it is the most practical for FDR allocation and is a reasonable allocation for flood management. Such FDR can work with water conservancy engineering facilities as part of a comprehensive management system toward protecting public health, minimizing economic losses, and preserving functions of development and natural lands within floodplains of the Yellow River.
URI: https://doi.org/10.1016/j.jhydrol.2022.128560
http://repositorio.ufla.br/jspui/handle/1/56527
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