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

Wu, Zening; Zhou, Yihong; Wang, Shuangfeng

doi:10.1109/access.2020.3017277

Cited by 26 publications

(15 citation statements)

References 62 publications

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“…The training and testing process of sample data is the basis of constructing the prediction model of the inundation process. To input the process data of these variables into the model, the data processing method of equal distance splitting and reorganizing was used to process the sample data, which is referred to in previous research (Wu et al, 2020). On this basis, calculate the sensitivity index (rainfall, rainfall duration, rainfall peak, position coefficient, rain intensity variance and peak multiplier) of each rainfall process after splitting and reorganizing.…”

Section: Training Datasetmentioning

confidence: 99%

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Real-time prediction and ponding process early warning method at urban flood points based on different deep learning methods

Zhou

Jiang

et al. 2022

Preprint

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Accurate prediction of urban floods is regarded as one of the critical means to prevent urban floods and reduce the losses caused by floods. However, due to the uncertainty and complexity of urban floods and waterlogging, it is very difficult to use simulation models to quickly and accurately predict and warn of urban floods. Therefore, it is necessary to develop new methods to support the rapid and accurate prediction of urban floods and waterlogging. In this study, a refined prediction and early warning method for urban flood and waterlogging processes based on deep learning methods is proposed from three aspects: research feasibility, method applicability, and method system. The spatial autocorrelation of rain and ponding points is analyzed by Moran's I. For each ponding point, the relationship model between the rainfall process and ponding process is constructed based on different deep learning methods, and the results are analyzed and verified by a statistical evaluation method. The results show that the gradient boosting decision tree algorithm has the highest accuracy and efficiency (with a root mean square error of 0.001) for ponding process prediction and is regarded as the most suitable method for ponding process prediction. Finally, the real-time prediction and early warning of urban floods and waterlogging processes driven by rainfall forecast data are realized, and the results are verified by the measured data. In addition, the results of the sensitivity analysis show that the rainfall peak and location coefficient have the greatest impact on ponding.

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Section: Training Datasetmentioning

confidence: 99%

“…The above parameters are optimized by the grid search algorithm. A complete mathematical and technical description of GARP model can be found in Friedman., 2001, Wu et al, 2020.…”

Section: Gbdtmentioning

confidence: 99%

Real-time prediction and ponding process early warning method at urban flood points based on different deep learning methods

Zhou

Jiang

et al. 2022

Preprint

Self Cite

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“…Sensors 2020, 20, x FOR PEER REVIEW 3 of 20 models, which is often employed to forecast ET [39] and urban flood [40,41], but rarely in GWLA. Furthermore, the multi-stage machine learning algorithm may have more powerful expressive performance than a single algorithm in downscaling GRACE products.…”

Section: Study Areamentioning

confidence: 99%

“…The RF algorithm has been utilized to downscale GRACE observations and obtained satisfactory results in some areas [ 37 , 38 ]. As a kind of ensemble machine learning algorithm, GBDT performs well in constructing non-linear regression models, which is often employed to forecast ET [ 39 ] and urban flood [ 40 , 41 ], but rarely in GWLA. Furthermore, the multi-stage machine learning algorithm may have more powerful expressive performance than a single algorithm in downscaling GRACE products.…”

Section: Introductionmentioning

confidence: 99%

Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain

Zhang

Zheng

Yin

et al. 2020

Sensors

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The launch of GRACE satellites has provided a new avenue for studying the terrestrial water storage anomalies (TWSA) with unprecedented accuracy. However, the coarse spatial resolution greatly limits its application in hydrology researches on local scales. To overcome this limitation, this study develops a machine learning-based fusion model to obtain high-resolution (0.25°) groundwater level anomalies (GWLA) by integrating GRACE observations in the North China Plain. Specifically, the fusion model consists of three modules, namely the downscaling module, the data fusion module, and the prediction module, respectively. In terms of the downscaling module, the GRACE-Noah model outperforms traditional data-driven models (multiple linear regression and gradient boosting decision tree (GBDT)) with the correlation coefficient (CC) values from 0.24 to 0.78. With respect to the data fusion module, the groundwater level from 12 monitoring wells is incorporated with climate variables (precipitation, runoff, and evapotranspiration) using the GBDT algorithm, achieving satisfactory performance (mean values: CC: 0.97, RMSE: 1.10 m, and MAE: 0.87 m). By merging the downscaled TWSA and fused groundwater level based on the GBDT algorithm, the prediction module can predict the water level in specified pixels. The predicted groundwater level is validated against 6 in-situ groundwater level data sets in the study area. Compare to the downscaling module, there is a significant improvement in terms of CC metrics, on average, from 0.43 to 0.71. This study provides a feasible and accurate fusion model for downscaling GRACE observations and predicting groundwater level with improved accuracy.

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“…She and You (2019) coupled the radial basis function (RBF) and NARX to build a model for predicting urban drainage curves, which has great potential in urban runoff prediction and management. Wu et al (2020) used 16 combinations of rainfall sensitivity indicators to determine the optimal plan for predicting the depth of stagnant water and used the gradient boosting decision tree (GBDT) algorithm in deep learning to construct the urban storm gathering point of the stagnant process prediction model, generating refined forecasts of urban floods. Lee et al (2020) used machine learning methods combined with design rainfall-runoff analysis (DRRA) and watershed characteristics to estimate appropriate design floods for unmeasured watersheds.…”

Section: Introductionmentioning

confidence: 99%

A Rapid Prediction Model of Urban Flood Inundation in a High-Risk Area Coupling Machine Learning and Numerical Simulation Approaches

Yan

Feng

et al. 2021

Int J Disaster Risk Sci

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Climate change has led to increasing frequency of sudden extreme heavy rainfall events in cities, resulting in great disaster losses. Therefore, in emergency management, we need to be timely in predicting urban floods. Although the existing machine learning models can quickly predict the depth of stagnant water, these models only target single points and require large amounts of measured data, which are currently lacking. Although numerical models can accurately simulate and predict such events, it takes a long time to perform the associated calculations, especially two-dimensional large-scale calculations, which cannot meet the needs of emergency management. Therefore, this article proposes a method of coupling neural networks and numerical models that can simulate and identify areas at high risk from urban floods and quickly predict the depth of water accumulation in these areas. Taking a drainage area in Tianjin Municipality, China, as an example, the results show that the simulation accuracy of this method is high, the Nash coefficient is 0.876, and the calculation time is 20 seconds. This method can quickly and accurately simulate the depth of water accumulation in high-risk areas in cities and provide technical support for urban flood emergency management.

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

Cited by 26 publications

References 62 publications

Real-time prediction and ponding process early warning method at urban flood points based on different deep learning methods

Real-time prediction and ponding process early warning method at urban flood points based on different deep learning methods

Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain

A Rapid Prediction Model of Urban Flood Inundation in a High-Risk Area Coupling Machine Learning and Numerical Simulation Approaches

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