An ensemble neural network model for real-time prediction of urban floods

Berkhahn, Simon; Fuchs, Lothar; Neuweiler, Insa

doi:10.1016/j.jhydrol.2019.05.066

Cited by 160 publications

(115 citation statements)

References 28 publications

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“…To reduce the storage requirement and the ANN model training time, the study area is subdivided into 50 × 50 squared grids, each grid having its own independent ANN (the output layer has 1400 elements) ( Supplementary Figure 1). A similar strategy was used by Berkhahn et al (2019) for an ANN for flood prediction having rainfall as input.…”

Section: Methodology Resilient Backpropagation Algorithm For Artificimentioning

confidence: 99%

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Prediction of Maximum Flood Inundation Extents With Resilient Backpropagation Neural Network: Case Study of Kulmbach

Lin

Leandro

et al. 2020

Front. Earth Sci.

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In many countries, floods are the leading natural disaster in terms of damage and losses per year. Early prediction of such events can help prevent some of those losses. Artificial neural networks (ANN) show a strong ability to deal quickly with large amounts of measured data. In this work, we develop an ANN for outputting flood inundation maps based on multiple discharge inputs with a high grid resolution (4 m × 4 m). After testing different neural network training algorithms and network structures, we found resilience backpropagation to perform best. Furthermore, by introducing clustering for preprocessing discharge curves before training, the quality of the prediction could be improved. Synthetic flood events are used for the training and validation of the ANN. Historical events were additionally used for further validation with real data. The results show that the developed ANN is capable of predicting the maximum flood inundation extents. The mean squared error in more than 98 and 86% of the total area is smaller than 0.2 m 2 in the prediction of synthetic events and historical events, respectively.

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Section: Methodology Resilient Backpropagation Algorithm For Artificimentioning

confidence: 99%

“…For flood inundation forecast, Simon Berkhahn et al (2019) trained an ANN with synthetic events of spatial rainfall data for 2D urban pluvial inundation. Chang M.J. et al (2018) applied a mix of SVM and GIS analysis to expand point forecasts to flooded areas at a sub-catchment scale.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Maximum Flood Inundation Extents With Resilient Backpropagation Neural Network: Case Study of Kulmbach

Lin

Leandro

et al. 2020

Front. Earth Sci.

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“…Research on the time distribution of ponding depth is a trend and a major challenge for flood warning. Serval studies have assessed multistep-ahead flood forecasts using time series methods [53], [64]. Chang et al [49] used the time series method of three neural network models to predict the water level in the floodwater storage pond for 10-to 60-minahead forecasts.…”

Section: Discussionmentioning

confidence: 99%

“…Urban floods are the result of the comprehensive effect of climate variables and underlying surface conditions (including rainfall, topography, river network, land use and pipe network) [37], in which rainfall is the driving factor of urban flood [52]. Due to the limited change of the underlying surface conditions in the city in the short term, rainfall is the direct reason of urban flooding [53]. For fixed water accumulation points, an internal relationship between rainfall and water accumulation is observed.…”

Section: Selection Of Sensitivity Index For Depth Prediction Of Acmentioning

confidence: 99%

Real-Time Prediction of the Water Accumulation Process of Urban Stormy Accumulation Points Based on Deep Learning

Zhou

Wang

2020

IEEE Access

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Influenced by climate change and urbanization, urban flood frequently occurs and represents a serious challenge for many cities. Therefore, it is necessary to generate refined predictions of urban floods, such as the prediction of water accumulation processes at water accumulation points, which is of great significance for supporting water-related managers to reduce flood losses. In this study, 16 combination schemes of rainfall sensitivity indicators were used to determine the optimal scheme for predicting the depth of accumulated water, and the gradient boosting decision tree (GBDT) algorithm in deep learning was used to build a prediction model of the accumulation process of urban stormy accumulation points. Among the 16 schemes, the relative error of scheme 1 is 15.39%, and the qualified rate is 92.86%. This scheme exhibits the highest accuracy for the prediction results of water accumulation depth. Given this finding, the GBDT algorithm was used to construct a regression prediction model of the water accumulation process based on the collected historical rainfall water accumulation data of 50 water accumulation points. The results demonstrated that the GBDT regression prediction model has a mean relative error of 19.77%, a qualified rate of 82.00%, and a peak average relative error of 5.48%, which verify the validity and applicability of the model for the real-time prediction of the process of water accumulation. INDEX TERMS Urban flood, Deep learning, Water accumulation, Real-time prediction.

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“…Tehrany et al (2014) used a novel ensemble weights-of-evidence and support vector machine models to map the flood susceptibility. Berkhahn et al (2019) found that ensemble approaches for the artificial neural network help to overcome the overfitting. Other models, such as random forest, fuzzy neuro, were also combined with the ensemble approaches (Razavi Termeh et al, 2018;Choubin et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Ensemble learning of daily river discharge modeling for two watersheds with different climates

Zhang²,

Liu

et al. 2020

Atmospheric Science Letters

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In order to reduce the uncertainties and improve the river discharge modeling accuracy, several topography-based hydrological models (TOPMODEL), generated by different combinations of parameters, were incorporated into an ensemble learning framework with the boosting method. Both the Baohe River Basin (BRB) with humid climate, and the Linyi River Basin (LRB) with semiarid climate were chosen for model testing. Observed daily precipitation, pan evaporation and stream flow data were used for model development and testing. Different Nash-Sutcliffe efficiency coefficients, the coefficient of determination and the Root Mean Square Error were adopted to implement a comprehensive assessment on model performances. Testing results indicated that ensemble learning method could improve the modeling accuracy by comparing with the best single TOPMODEL. During the validation periods, the boosting method could increase the modeling accuracy by 9 and 16% for BRB and LRB, respectively. The ensemble method significantly narrowed the gap of model performances over watersheds with different climatic conditions. Hence, using the ensemble learning to enhance the feasibility of hydrological models for different climatic regions is promising.

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An ensemble neural network model for real-time prediction of urban floods

Cited by 160 publications

References 28 publications

Prediction of Maximum Flood Inundation Extents With Resilient Backpropagation Neural Network: Case Study of Kulmbach

Prediction of Maximum Flood Inundation Extents With Resilient Backpropagation Neural Network: Case Study of Kulmbach

Real-Time Prediction of the Water Accumulation Process of Urban Stormy Accumulation Points Based on Deep Learning

Ensemble learning of daily river discharge modeling for two watersheds with different climates

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