Flood hydrograph coincidence analysis for mainstream and its tributaries

Huang, Kangdi; Chen, Lu; Zhou, Jianzhong; Zhang, Mengjie; Singh, Vijay P.

doi:10.1016/j.jhydrol.2018.08.007

Cited by 46 publications

(24 citation statements)

References 51 publications

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“…During the historical years, extreme flood events, especially for the years 1870, 1954, 1998, 2010, and 2016, have caused heavy casualties and property losses. For example, in 2016, the whole Yangtze River basin suffered from a monstrous flood, which led to economic losses of 146.9 billion Chinese Yuan and affected nearly 60.74 million people [32,33]. Accordingly, flood forecasting is an essential task for modern flood prevention and disaster relief of the upper Yangtze River.…”

Section: Study Area and Datamentioning

confidence: 99%

Flood Forecasting Based on an Improved Extreme Learning Machine Model Combined with the Backtracking Search Optimization Algorithm

Chen

Sun

Zhou

et al. 2018

Water

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Flood forecasting plays an important role in flood control and water resources management. Recently, the data-driven models with a simpler model structure and lower data requirement attract much more attentions. An extreme learning machine (ELM) method, as a typical data-driven method, with the advantages of a faster learning process and stronger generalization ability, has been taken as an effective tool for flood forecasting. However, an ELM model may suffer from local minima in some cases because of its random generation of input weights and hidden layer biases, which results in uncertainties in the flood forecasting model. Therefore, we proposed an improved ELM model for short-term flood forecasting, in which an emerging dual population-based algorithm, named backtracking search algorithm (BSA), was applied to optimize the parameters of ELM. Thus, the proposed method is called ELM-BSA. The upper Yangtze River was selected as a case study. Several performance indexes were used to evaluate the efficiency of the proposed ELM-BSA model. Then the proposed model was compared with the currently used general regression neural network (GRNN) and ELM models. Results show that the ELM-BSA can always provide better results than the GRNN and ELM models in both the training and testing periods. All these results suggest that the proposed ELM-BSA model is a promising alternative technique for flood forecasting.

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Section: Study Area and Datamentioning

confidence: 99%

Flood Forecasting Based on an Improved Extreme Learning Machine Model Combined with the Backtracking Search Optimization Algorithm

Chen

Sun

Zhou

et al. 2018

Water

Self Cite

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“…The duration of the flood hydrograph plays a key role in determination of the DFH. In this study, the hydrograph lasts for the whole flood season (1 June to 30 September) [56][57][58]. All typical flood hydrographs mentioned above were used to derive the DFHs.…”

Section: Design Flood Hydrographsmentioning

confidence: 99%

Optimal Operation of Cascade Reservoirs for Flood Control of Multiple Areas Downstream: A Case Study in the Upper Yangtze River Basin

Zhou

Sun

Chen

et al. 2018

Water

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The purpose of a flood control reservoir operation is to prevent flood damage downstream of the reservoir and the safety of the reservoir itself. When a single reservoir cannot provide enough storage capacity for certain flood control points downstream, cascade reservoirs should be operated together to protect these areas from flooding. In this study, for efficient use of the reservoir storage, an optimal flood control operation model of cascade reservoirs for certain flood control points downstream was proposed. In the proposed model, the upstream reservoirs with the optimal operation strategy were considered to reduce the inflow of the reservoir downstream. For a large river basin, the flood routing and time-lag cannot be neglected. So, dynamic programming (DP) combined with the progressive optimality algorithm (POA) method, DP-POA, was proposed. Thus, the innovation of this study is to propose a two-stage optimal reservoir operation model with a DP-POA algorithm to solve the problem of optimal co-operation of cascade reservoirs for multiple flood control points downstream during the flood season. The upper Yangtze River was selected as a case study. Three reservoirs from upstream to downstream, Xiluodu, Xiangjiaba and the Three Gorges reservoirs (TGR) in the upper Yangtze River, were taken into account. Results demonstrate that the two-stage optimization algorithm has a good performance in solving the cascade reservoirs optimization problem, because the inflow of reservoir downstream and the division volumes were largely reduced. After the optimal operation of Xiluodu and Xiangjiaba reservoirs, the average reduction of flood peak for all these 13 typical flood hydrographs (TFHs) is 13.6%. Meanwhile, the cascade reservoirs can also store much more storm water during a flood event, and the maximum volumes stored in those two reservoirs upstream in this study can reach 25.2 billion m3 during a flood event. Comprising the proposed method with the current operation method, results demonstrate that the flood diversion volumes at the flood control points along the river decrease significantly.

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“…As instabilidades atmosféricas severas geram grandes volumes de chuva em pouco tempo, e junto a isso, a baixa drenagem dos espaços urbanos e a ocupação de áreas inadequadas são os responsáveis por tais eventos. Trabalhos que identifiquem a frequência e intensidade desses eventos extremos auxiliam na tomada de decisão e no planejamento urbano regional (BERTILSSON et al, 2018;HUANG et al, 2018;JAMALI et al, 2018;MUSTAFA et al, 2018).…”

Section: Introductionunclassified

Frequência, Intensidade E Variabilidade Pluviométrica Na Mesorregião Sudoeste Paranaense

Caldana

Martelócio

Rudke

et al. 2019

ABClima

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A variabilidade climática exerce influência significativa sobre as atividades humanas. Os eventos extremos de precipitação causam danos severos tanto no espaço urbano quanto no rural. O aumento da temperatura média da superfície global, em conjunto com eventos extremos de precipitação causam grandes incertezas na dinâmica dos impactos das condições de tempo e clima, e como se planejar para prevenir os impactos é um tema que se torna cada vez mais crucial. Dessa forma, estudos que evidenciem a variabilidade climática em diferentes escalas são fundamentais para o planejamento das atividades humanas. O estado do Paraná se encontra em uma região de transição climática com grande variação dos elementos meteorológicos, sendo essencial a compreensão destes fenômenos devido a seus possíveis impactos. Neste sentido, o objetivo deste trabalho foi analisar a variabilidade pluviométrica, a intensidade e frequência da precipitação na Mesorregião Sudoeste Paranaense - MRSPR associados ao fenômeno El Niño Oscilação Sul - ENOS. Para isso utilizou-se as escalas temporais anual, mensal e diária com o recorte temporal de 1976 a 2017. Através dos resultados, identificou-se grande discrepância nas alturas pluviométricas anuais, mensais e diárias, com parte expressiva dos eventos extremos e maiores alturas de precipitação ocorrendo em períodos de El Niño, enquanto os períodos secos predominam em condições de La Niña.

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Flood hydrograph coincidence analysis for mainstream and its tributaries

Cited by 46 publications

References 51 publications

Flood Forecasting Based on an Improved Extreme Learning Machine Model Combined with the Backtracking Search Optimization Algorithm

Flood Forecasting Based on an Improved Extreme Learning Machine Model Combined with the Backtracking Search Optimization Algorithm

Optimal Operation of Cascade Reservoirs for Flood Control of Multiple Areas Downstream: A Case Study in the Upper Yangtze River Basin

Frequência, Intensidade E Variabilidade Pluviométrica Na Mesorregião Sudoeste Paranaense

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