A multi-strategy-mode waterlogging-prediction  framework for urban flood depth

Zhang, Zongjia; Liang, Jun; Zhou, Yueyue; Huang, Zhejun; Jiang, Jie; Liu, Junguo; Yang, Lili

doi:10.5194/nhess-22-4139-2022

Cited by 15 publications

(9 citation statements)

References 43 publications

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“…It is essential to carefully evaluate the performance of different ensemble models and select the one that provides the best trade-off between bias and variance, accuracy, diversity, stability, generalization, and computational cost [67,91,92,159]. The final stage would evaluate and validate the performance of the selected ensemble model using appropriate evaluation metrics and statistical tests, such as the mean absolute error (MAE) [21,83,160], root-mean-squared error (RMSE) [106,161], correlation coefficient (CC) [49,83,106,161], and coefficient of determination (R-squared) [42,43,[161][162][163]. The following section covers some of the fundamental concepts that are considered when evaluating a neural network ensemble for storm surge prediction.…”

Section: Model Selection and Evaluationmentioning

confidence: 99%

“…Hydrodynamic modeling has also been extensively used to investigate the spatial and temporal variability of storm surges. Hydrodynamic models are widely utilized to describe coastal ocean processes and near-shore circulation and to simulate future scenarios of possible storm surge flooding [21]. These models are well-developed to account for the inherent uncertainties associated with sea level rise and storm surges.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ensemble Neural Networks for the Development of Storm Surge Flood Modeling: A Comprehensive Review

Nezhad,

Barooni,

Velioglu Sogut

et al. 2023

JMSE

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This review paper focuses on the use of ensemble neural networks (ENN) in the development of storm surge flood models. Storm surges are a major concern in coastal regions, and accurate flood modeling is essential for effective disaster management. Neural network (NN) ensembles have shown great potential in improving the accuracy and reliability of such models. This paper presents an overview of the latest research on the application of NNs in storm surge flood modeling and covers the principles and concepts of ENNs, various ensemble architectures, the main challenges associated with NN ensemble algorithms, and their potential benefits in improving flood forecasting accuracy. The main part of this paper pertains to the techniques used to combine a mixed set of predictions from multiple NN models. The combination of these models can lead to improved accuracy, robustness, and generalization performance compared to using a single model. However, generating neural network ensembles also requires careful consideration of the trade-offs between model diversity, model complexity, and computational resources. The ensemble must balance these factors to achieve the best performance. The insights presented in this review paper are particularly relevant for researchers and practitioners working in coastal regions where accurate storm surge flood modeling is critical.

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Section: Model Selection and Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ensemble Neural Networks for the Development of Storm Surge Flood Modeling: A Comprehensive Review

Nezhad,

Barooni,

Velioglu Sogut

et al. 2023

JMSE

View full text Add to dashboard Cite

show abstract

“…In order to improve the simulation efficiency and solve the problem of insufficient types of data available in the study area, more and more scholars focus their attention on machine learning for simulating and predicting urban waterlogging. For example, based on regional comprehensive data, a data-driven model framework is constructed to predict the depth of target sites when urban flood disasters occur (Seleem et al 2023;Zahura et al 2020;Zhang et al 2022). Based on the forecast and analysis of flood sensitivity in urban catchment area, urban disaster-bearing capacity can be strengthened by constructing coupling or integration model (Y. T. Li and Hong 2023;Tang et al 2022;Zhao et al 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Based on the forecast and analysis of flood sensitivity in urban catchment area, urban disaster-bearing capacity can be strengthened by constructing coupling or integration model (Y. T. Li and Hong 2023;Tang et al 2022;Zhao et al 2020). Some scholars have constructed a multi-strategy model of waterlogging prediction to make a short-term forecast of the overall situation of waterlogging in the future combining with historical precipitation and waterlogging depth (Zhang et al 2022). Unfortunately, the lack of high-quality data and the lack of interpretability of the model's internal mechanisms have brought about an unavoidable negative impact on this type of model (Hou et al 2021;Yan et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Urban Waterlogging Prediction Based on Time Lag Correlation Analysis and Multi-model Coupling

Lei,

Liu,

Long

et al. 2023

Preprint

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As extreme weather becomes more frequent and the damage caused by urban waterlogging is increasing, it is important to establish a fast and accurate model of waterlogging disasters. However, the smartization of most cities starts relatively late, and the types and quality of monitoring data are uneven. Therefore, there has been a focus on researching and developing a reasonable, fast, and accurate urban waterlogging prediction model that can effectively utilize limited data. Based on this situation, a method of time lag correlation analysis considering the mechanism of regional physics (PTLC) is proposed in this paper. Combined with spatial decoupling, a prior analysis is provided for model prediction. At the same time, a deep learning model (Poar_LSTM) with automatic optimization function is proposed and coupled with hydrodynamic model (Poar_DHC). Based on the verification of Doumen area in Fuzhou, the typical rainfall process from 2021 to 2022 is reviewed. The results indicate that Poar_LSTM shows obvious advantages in the river level prediction during the same rainfall period. The Nash efficiency coefficients in the verification reach 0.969 and 0.971 respectively. Different data-driven models have little influence on the overall prediction effect of waterlogging coupling model. Poar_DHC has the highest accuracy in the prediction of underground liquid level. According to the different rainfalls, PTLC plays a good guiding role in the prior analysis and later evaluation of the early prediction of the model. This study can provide a scientific reference for the analysis of flood mechanism and the rapid and accurate prediction of rainstorms.

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“…Encouragingly, more and more scholars have begun to study the waterlogging depth prediction recently. Zhang et al [ 18 ] proposed a multi-strategy-mode-waterlogging-prediction framework for predicting waterlogging depth based on time series prediction and machine learning regression algorithm. Wu et al [ 19 ] proposed a regression model constructed with deep learning algorithm, named Gradient Boosting Decision Tree(GBDT), to predict the depth of urban flooded areas.…”

Section: Introductionmentioning

confidence: 99%

A noval approach based on TCN-LSTM network for predicting waterlogging depth with waterlogging monitoring station

Yao,

Cai,

Qian

et al. 2023

PLoS ONE

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As a result of climate change and rapid urbanization, urban waterlogging commonly caused by rainstorm, is becoming more frequent and more severe in developing countries. Urban waterlogging sometimes results in significant financial losses as well as human casualties. Accurate waterlogging depth prediction is critical for early warning system and emergency response. However, the existing hydrological models need to obtain more abundant hydrological data, and the model construction is complicated. The waterlogging depth prediction technology based on object detection model are highly dependent on image data. To solve the above problem, we propose a novel approach based on Temporal Convolutional Networks and Long Short-Term Memory networks to predicting urban waterlogging depth with Waterlogging Monitoring Station. The difficulty of data acquisition is small though Waterlogging Monitoring Station and TCN-LSTM model can be used to predict timely waterlogging depth. Waterlogging Monitoring Station is developed which integrates an automatic rain gauge and a water gauge. The rainfall and waterlogging depth can be obtained by periodic sampling at some areas with Waterlogging Monitoring Station. Precise hydrological data such as waterlogging depth and rainfall collected by Waterlogging Monitoring Station are used as training samples. Then training samples are used to train TCN-LSTM model, and finally a model with good prediction effect is obtained. The experimental results show that the difficulty of data acquisition is small, the complexity is low and the proposed TCN-LSTM hybrid model can properly predict the waterlogging depth of the current regional. There is no need for high dependence on image data. Meanwhile, compared with machine learning model and RNN model, TCN-LSTM model has higher prediction accuracy for time series data. Overall, the low-cost method proposed in this study can be used to obtain timely waterlogging warning information, and enhance the possibility of using existing social networks and traffic surveillance video systems to perform opportunistic waterlogging sensing.

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A multi-strategy-mode waterlogging-prediction framework for urban flood depth

Cited by 15 publications

References 43 publications

Ensemble Neural Networks for the Development of Storm Surge Flood Modeling: A Comprehensive Review

Ensemble Neural Networks for the Development of Storm Surge Flood Modeling: A Comprehensive Review

Urban Waterlogging Prediction Based on Time Lag Correlation Analysis and Multi-model Coupling

A noval approach based on TCN-LSTM network for predicting waterlogging depth with waterlogging monitoring station

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