Surrogate modeling of joint flood risk across coastal watersheds

Bass, Benjamin; Bedient, Philip B.

doi:10.1016/j.jhydrol.2018.01.014

“…Coastal flood risk attributed to storm surge and rainfall (through pluvial or fluvial processes) has been reported in many regions around the world, including Asia (Lian et al 2013, Ikeuchi et al 2017, Xu et al 2018, Ghosh et al 2019, Europe (Svensson and Jones 2004, Klerk et al 2015, Van den Hurk et al 2015, Bevacqua et al 2017, Hu et al 2019, North America (Wahl et al 2015, Bass and Bedient 2018, Shao et al 2019, North Africa (Zellou and Rahali 2019) and Australia (Zheng et al 2013, Kumbier et al 2018, Wu et al 2018. There is strong evidence that the joint occurrence of extreme storm surge and heavy rainfall is related to large scale weather patterns, such as tropical cyclones, ex-tropical cyclones, frontal systems and East Coast Lows in Australia (McInnes et al 2002, Van den Hurk et al 2015, Wahl et al 2015, Kumbier et al 2018, Wu et al 2018.…”

Section: Introductionmentioning

confidence: 99%

Impact of ENSO on dependence between extreme rainfall and storm surge

Wu

¹

,

Leonard

²

2019

Environ. Res. Lett.

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Dependence between extreme rainfall and storm surge can have significant implications for coastal floods, which are often caused by joint occurrence of these flood drivers (through pluvial or fluvial processes). The effect of multiple drivers leading to a compound flood event poses higher risk than those caused by a single flood-driving process. There is strong evidence that compound floods caused by joint occurrence of extreme storm surge and heavy rainfall are related to meteorological forcing (e.g. large scale pressure systems and wind) and climate phenomena (e.g. the El Niño Southern Oscillation or ENSO). Therefore, understanding how climate phenomena affect the co-occurrence of coastal flood drivers is an important step towards understanding future coastal flood risk under climate change. Here we examine the impact of one of the most important climate phenomena-ENSO-on dependence between storm surge and rainfall in Australia, using both observed surge and modelled surge from a linked ocean-climate modelthe Regional Ocean Modeling System. Our results show that ENSO has a significant impact on the dependence between extreme rainfall and storm surge, thus flood risk resulted from these drivers. The overall dependence is largely driven by La Niña in Australia, with increased dependence observed during La Niña along most of the Australian coastline. However, there can be increased dependence during El Niño in some locations. The results demonstrate dependence is contributed by unequally-weighted mechanisms due to the interaction between climate phenomena and local features, indicating the need for greater understanding of composition of compound flood risk. Where climate phenomena are anticipated to change into the future, it is possible to use integrated process-driven models to establish a better understanding of whether extremes are more likely to co-occur and exacerbate compound flood risk.

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“…Bass and Bedient [218] proposed a hybrid model of surrogate-ML for long-term flood prediction suitable for TCs. The methods used included ANN integrated with principal component analysis (PCA), Kriging integrated with PC, and Kriging.…”

Section: Long-term Flood Prediction Using Hybrid ML Methodsmentioning

confidence: 99%

Flood Prediction Using Machine Learning, Literature Review

Mosavi¹,

Öztürk²,

Chau³

2018

Preprint

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Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models has been contributing to risk reduction, policy suggestion, minimizing loss of human life and reducing the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods have highly contributed in the advancement of prediction systems providing better performance and cost effective solutions. Due to the vast benefits and potential of ML, its popularity has dramatically increased among hydrologists. Researchers through introducing the novel ML methods and hybridization of the existing ones have been aiming at discovering more accurate and efficient prediction models. The main contribution is to demonstrate the state of the art of ML models in flood prediction and give an insight over the most suitable models. The literature where ML models are benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed have been particularly investigated to provide an extensive overview on various ML algorithms usage in the field. The performance comparison of ML models presents an in-depth understanding about the different techniques within the framework of a comprehensive evaluation and discussion. As the result, the paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported the most effective strategy in improvement of the ML methods. This survey can be used as a guideline for the hydrologists as well as climate scientists to assist them choosing the proper ML method according to the prediction task conclusions.

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“…The proposed EPS has a promising ability of generation and prediction accuracy. Bass and Bedient [196] proposed a hybrid model of surrogate-ML for long-term flood prediction suitable for TCs. The methods used include ANN integrated with Principal Components Analysis (PCA), Kriging integrated with PC, and Kriging.…”

Section: Long-term Flood Prediction Methods Using Single ML Methodsmentioning

confidence: 99%

Flood Prediction Using Machine Learning, Literature Review

Mosavi¹,

Öztürk²,

Kwok-wing³

2018

Preprint

View full text Add to dashboard Cite

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models has been contributing to risk reduction, policy suggestion, minimizing loss of human life and reducing the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods have highly contributed in the advancement of prediction systems providing better performance and cost effective solutions. Due to the vast benefits and potential of ML, its popularity has dramatically increased among hydrologists. Researchers through introducing the novel ML methods and hybridization of the existing ones have been aiming at discovering more accurate and efficient prediction models. The main contribution is to demonstrate the state of the art of ML models in flood prediction and give an insight over the most suitable models. The literature where ML models are benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed have been particularly investigated to provide an extensive overview on various ML algorithms usage in the field. The performance comparison of ML models presents an in-depth understanding about the different techniques within the framework of a comprehensive evaluation and discussion. As the result, the paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported the most effective strategy in improvement of the ML methods. This survey can be used as a guideline for the hydrologists as well as climate scientists to assist them choosing the proper ML method according to the prediction task conclusions.

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Surrogate modeling of joint flood risk across coastal watersheds

Cited by 64 publications

References 23 publications

Impact of ENSO on dependence between extreme rainfall and storm surge

Impact of ENSO on dependence between extreme rainfall and storm surge

Flood Prediction Using Machine Learning, Literature Review

Flood Prediction Using Machine Learning, Literature Review

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