Flood Risk Analysis of Different Climatic Phenomena during Flood Season Based on Copula-Based Bayesian Network Method: A Case Study of Taihu Basin, China

Luo, Yi; Dong, Zengchuan; Guan, Xike; Liu, Yuhuan

doi:10.3390/w11081534

Cited by 13 publications

(6 citation statements)

References 37 publications

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“…More than 2 billion people were affected by floods from 1998 to 2017 worldwide (Rehman et al 2019), those who live in floodplains, without efficient flood forecasting systems, are most vulnerable to floods. The Taihu Basin is a typical plain river network area in China, with a developed economy and dense population (Luo et al 2019). It is characterized by a flat terrain, extensive river networks, and numerous polder areas, which are vulnerable to flooding (Zhai et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Flood forecasting using an improved NARX network based on wavelet analysis coupled with uncertainty analysis by Monte Carlo simulations: a case study of Taihu Basin, China

Jiang

Dong

Wang³

et al. 2021

Journal of Water and Climate Change

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Reliable flood forecasting can provide scientific basis for flood risk assessment and water resources management, and the Taihu water level forecasting with high precision is essential for flood control in the Taihu Basin. To increase the prediction accuracy, a coupling model (DWT-iNARX) is established by combining the discrete wavelet transformation (DWT) with improved nonlinear autoregressive with exogenous inputs network (iNARX), for predicting the daily Taihu water level during the flood season under different forecast periods. And the DWT-iNARX model is compared with the back-propagation neural network (BP) and iNARX models to assess its capability in prediction. Meanwhile, we propose an uncertainty analysis method based on Monte Carlo simulations (MCS) for quantifying model uncertainty and performing probabilistic water level forecast. The results show that three models achieve good simulation results with higher accuracy when the forecast period is short, such as 1–3 days. In overall performance, iNARX and DWT-iNARX models show superiority in comparison with the BP model, while the DWT-iNARX model yields the best performance among all the other models. The research results can provide a certain reference for the water level forecast of the Taihu Lake.

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Section: Introductionmentioning

confidence: 99%

Flood forecasting using an improved NARX network based on wavelet analysis coupled with uncertainty analysis by Monte Carlo simulations: a case study of Taihu Basin, China

Jiang

Dong

Wang³

et al. 2021

Journal of Water and Climate Change

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“…The results showed that the developed SWAT-Copula-based method has can be employed in data-scarce regions for effective drought monitoring with the minimum observed inputs. Daneshkhah et al (2016) and Luo et al (2019) constructed a different flood risk management model via the Copulabased Bayesian network to analyze the flood risk. employed the copula theory for estimating joint probability distributions describing the dependence degree between drought conditions and crop yield anomalies of two major rainfed portions of cereal.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Calculated Drought Return Periods Using Tri-variate and Bivariate Copula Functions Under Climate Change Condition

Naeini

Akhoond‐Ali

Radmanesh

et al. 2021

Water Resour Manage

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Concerning the various effects of climate change on intensifying extreme weather phenomena all around the world, studying its possible consequences in the following years has attracted the attention of researchers. As the drought characteristics identified by drought indices are highly significant in investigating the possible future drought, the Copula function is employed in many studies. In this study, the two-and three-variable Copula functions were employed for calculating the return period of drought events for the historical, the near future, and the far future periods. The results of considering the two-and three-variable Copula functions were separately compared with the results of the calculated Due to the high correlation between drought characteristics, bivariate and trivariate of Copula functions were applied to evaluate the return periods of the drought.The most severe historical drought was selected as the benchmark, and the drought zoning map for the GCM models was drawn. The results showed that severe droughts can be experienced, especially in the upper area of the basin where the primary water resource is located. Also, the nature of the drought duration plays a decisive role in the results of calculating the return periods of drought events.

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“…One of the first and the most widely applied approaches in dealing with Hybrid BNs is the use of conditional Gaussian models (Friedman et al, 1998;Lauritzen, 1992), which is suitable for continuous variables with Gaussian distributions. In addition to Gaussian models, copula-based non-parametric BNs that can be applied to any continuous variables (Hanea et al, 2015), are gradually being adopted in environmental research (Couasnon et al, 2018;Luo et al, 2019).…”

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confidence: 99%

Determining the Shoreline Retreat Rate of Australia Using Discrete and Hybrid Bayesian Networks

et al. 2021

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Evaluating shoreline retreat rate (SRR) on different spatial‐temporal scales is critical for effective coastal management. Large‐scale evaluations typically rely on data‐driven methods such as Discrete Bayesian networks (BNs). However, these BNs require discretization of continuous variables which can lead to information loss. Here, we propose a new method, the Hybrid BN to incorporate continuous variables without discretization. Both Discrete and Hybrid BNs were developed and compared to evaluate large‐scale (continental scale) SRR in Australia, using Digital Earth Australia data set. These BNs used forcing parameters (e.g., waves, tide, sediment sink/source, and sea level rise [SLR]) and geomorphic settings (e.g., geomorphology, backshore profile, and surfzone slope) to predict SRR. Validation of the BNs showed that Hybrid BNs, which provide a more realistic assessment of the range of SRR, outperform in predicting continuous variables, when compared with Discrete BNs. However, Discrete and Hybrid BNs provide consistent qualitative findings for the SRR of Australia. Among forcing parameters, the sediment sink/source was found to be the most informative variable to indicate the shoreline retreat, followed by tide, SLR rate, and wave processes. In the scenario of an increased SLR rate, tropical tidal flats were predicted as the most at risk coasts in Australia. We found that BNs can reflect the impact of different factors on coastal evolution, and predict future shoreline change by exploring historical data. The performance of these models can be further improved when more data sets become available.

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Flood Risk Analysis of Different Climatic Phenomena during Flood Season Based on Copula-Based Bayesian Network Method: A Case Study of Taihu Basin, China

Cited by 13 publications

References 37 publications

Flood forecasting using an improved NARX network based on wavelet analysis coupled with uncertainty analysis by Monte Carlo simulations: a case study of Taihu Basin, China

Flood forecasting using an improved NARX network based on wavelet analysis coupled with uncertainty analysis by Monte Carlo simulations: a case study of Taihu Basin, China

Comparison of the Calculated Drought Return Periods Using Tri-variate and Bivariate Copula Functions Under Climate Change Condition

Determining the Shoreline Retreat Rate of Australia Using Discrete and Hybrid Bayesian Networks

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