Urban inundation rapid prediction method based on multi-machine learning algorithm and rain pattern analysis

Chen, Guangzhao; Hou, Jingming; Liu, Yuan; Xue, Shuhong; Wu, Hao; Wang, Tian; Lv, Jiahao; Jing, Jing; Yang, Shaoxiong

doi:10.1016/j.jhydrol.2024.131059

Cited by 10 publications

(2 citation statements)

References 39 publications

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“…Xie et al constructed 14,278 ANN models involving deep learning techniques, achieving fast prediction of inundation depth for each grid cell within study areas of 7.9 km 2 [31]. Chen et al combined hydrodynamic models with machine-learning algorithms and proposed a rapid urban inundation prediction method [32]. Despite the promising results achieved using artificial intelligence in flood inundation prediction, most studies have focused on predicting the maximum flood inundation depth rather than its dynamic evolution process.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Mitigation Effect of Urban River Channel Flood Diversion on Waterlogging Disasters Based on Deep Learning

Sun,

Zhu,

Zhang

et al. 2024

Water

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In recent years, urban waterlogging disasters have become increasingly prominent. Physically based urban waterlogging simulation models require considerable computational time. Therefore, rapid and accurate simulation and prediction of urban pluvial floods are important for disaster prevention and mitigation. For this purpose, we explored an urban waterlogging prediction method based on a long short-term memory neural network model that integrates an attention mechanism and a 1D convolutional neural network (1DCNN–LSTM–Attention), using the diversion of the Jinshui River in Zhengzhou, China, as a case study. In this method, the 1DCNN is responsible for extracting features from monitoring data, the LSTM is capable of learning from time-series data more effectively, and the Attention mechanism highlights the impact of features on input effectiveness. The results indicated the following: (1) The urban waterlogging rapid prediction model exhibited good accuracy. The Pearson correlation coefficient exceeded 0.95. It was 50–100 times faster than the InfoWorks ICM model. (2) Diversion pipelines can meet the design flood standard of a 200-year return period, aligning with the expected engineering objectives. (3) River channel diversion significantly reduced the extent of inundation. Under the 30-year return period rainfall scenario, the maximum inundation area decreased by 1.46 km2, approximately equivalent to 205 international standard soccer fields.

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Section: Introductionmentioning

confidence: 99%

Analyzing the Mitigation Effect of Urban River Channel Flood Diversion on Waterlogging Disasters Based on Deep Learning

Sun,

Zhu,

Zhang

et al. 2024

Water

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“…These approaches are advantageous, particularly in scenarios where process-based models may be constrained by high modeling costs, data scarcity, or the need for supplementary analytical capabilities to interpret complex datasets [11]. Several water and hydrology studies have already used ML for research applications such as sediment transport, rainfall-runoff simulation, water distribution networks, water quality analysis, and flood inundation mapping [12][13][14]. The use of machine learning models like ANN, SVM, and Random Forests (RFs) has increased in hydrology and water resource modeling.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Models for Improving Discharge Prediction in Ungauged Watershed: A Case Study in East DuPage, Illinois

Asadollahi,

Magar,

Poudel

et al. 2024

Geographies

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Accurate flood prediction models and effective flood preparedness rely on thoroughly understanding rainfall–runoff dynamics. Similarly, effective rainfall–runoff models account for multiple interrelated parameters for robust runoff prediction. Process-based physical models offer valuable insights into hydrological processes, but their effectiveness can be hindered by data limitations or difficulties in acquiring specific data. Motivated by the frequent flooding events and limited data availability in the East Branch DuPage watershed, Illinois, this study addresses a critical gap in research by investigating effective discharge prediction methods. In this study, two significant machine learning (ML) models, artificial neural network (ANN) and support vector machine (SVM), were employed for discharge prediction. Historical data spanning from 2006 to 2021 were utilized to assess the performance of the models. Hyperparameter tuning was performed on the models to optimize their performance, and root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), coefficient of determination (R2), and the normalized root mean squared error (NRMSE) were used as evaluation metrics. Although both machine learning models demonstrated strong performance, the analysis revealed that the ANN model emerged as the more reliable option for predicting discharge in the watershed. Crucially, the ANN model surpassed the SVM model’s performance, achieving superior accuracy in predicting peak discharge events within the study area. Our findings have the potential to assist decision-makers and communities in implementing more dependable flood mitigation strategies, particularly in regions where hydrology data are limited.

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LSTM Model-Based Rapid Prediction Method of Urban Inundation with Rainfall Time Series

Pan,

Hou,

Gao

et al. 2024

Water Resour Manage

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Urban inundation rapid prediction method based on multi-machine learning algorithm and rain pattern analysis

Cited by 10 publications

References 39 publications

Analyzing the Mitigation Effect of Urban River Channel Flood Diversion on Waterlogging Disasters Based on Deep Learning

Analyzing the Mitigation Effect of Urban River Channel Flood Diversion on Waterlogging Disasters Based on Deep Learning

Application of Machine Learning Models for Improving Discharge Prediction in Ungauged Watershed: A Case Study in East DuPage, Illinois

LSTM Model-Based Rapid Prediction Method of Urban Inundation with Rainfall Time Series

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