Fast simulation and prediction of urban pluvial floods using a deep convolutional neural network model

Liao, Yong; Chen, Xiaohong; Lai, Chengguang

doi:10.1016/j.jhydrol.2023.129945

Cited by 50 publications

(3 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Weighted_Sequence t = Attention_Weights t • X t (19) where x t represents an element of the input sequence at time step t, Dense denotes a fully connected layer, W represents the weight matrix, and b represents the bias term.…”

Section: Attentionmentioning

confidence: 99%

“…The increase in impervious surfaces in cities accelerates the runoff yield and concentration in urban areas; therefore, the problem of river channel overflow triggered by heavy rainfall is often accompanied by deeper water accumulation, wider inundation areas, and faster flood flow velocities, leading to a shorter formation time for urban floods [19][20][21][22]. Constructing an urban waterlogging warning model is an effective measure for reducing losses from waterlogging disasters.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Sun,

Zhu,

Zhang

et al. 2024

Water

View full text Add to dashboard Cite

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.

show abstract

Section: Attentionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…Freshwater is critical for local, regional, and global biodiversity [1,2]; ecosystem productivity [3,4]; and human well-being [5,6]. However, inland waters, including urban reservoirs, face severe pollution and threats due to human activities such as industrialization and urbanization [7][8][9]. Urban reservoirs, situated in heavily urbanized areas, are particularly susceptible to untreated industrial, agricultural, aquacultural, and domestic wastewater discharge.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Machine Learning Model to Estimate Urban Water Quality Parameters Using Unmanned Aerial Vehicle Multispectral Imagery

Lei,

Jiang,

Deng

et al. 2024

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Urban reservoirs contribute significantly to human survival and ecological balance. Machine learning-based remote sensing techniques for monitoring water quality parameters (WQPs) have gained increasing prominence in recent years. However, these techniques still face challenges such as inadequate band selection, weak machine learning model performance, and the limited retrieval of non-optical active parameters (NOAPs). This study focuses on an urban reservoir, utilizing unmanned aerial vehicle (UAV) multispectral remote sensing and ensemble machine learning (EML) methods to monitor optically active parameters (OAPs, including Chla and SD) and non-optically active parameters (including CODMn, TN, and TP), exploring spatial and temporal variations of WQPs. A framework of Feature Combination and Genetic Algorithm (FC-GA) is developed for feature band selection, along with two frameworks of EML models for WQP estimation. Results indicate FC-GA’s superiority over popular methods such as the Pearson correlation coefficient and recursive feature elimination, achieving higher performance with no multicollinearity between bands. The EML model demonstrates superior estimation capabilities for WQPs like Chla, SD, CODMn, and TP, with an R2 of 0.72–0.86 and an MRE of 7.57–42.06%. Notably, the EML model exhibits greater accuracy in estimating OAPs (MRE ≤ 19.35%) compared to NOAPs (MRE ≤ 42.06%). Furthermore, spatial and temporal distributions of WQPs reveal nitrogen and phosphorus nutrient pollution in the upstream head and downstream tail of the reservoir due to human activities. TP, TN, and Chla are lower in the dry season than in the rainy season, while clarity and CODMn are higher in the dry season than in the rainy season. This study proposes a novel approach to water quality monitoring, aiding in the identification of potential pollution sources and ecological management.

show abstract

A Systematic Literature Review on Regression Machine Learning for Urban Flood Hazard Mapping

El Baida,

Boushaba,

Chourak

et al. 2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Fast simulation and prediction of urban pluvial floods using a deep convolutional neural network model

Cited by 50 publications

References 84 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

An Ensemble Machine Learning Model to Estimate Urban Water Quality Parameters Using Unmanned Aerial Vehicle Multispectral Imagery

A Systematic Literature Review on Regression Machine Learning for Urban Flood Hazard Mapping

Contact Info

Product

Resources

About