Huiliang Wang
Biographic Data
| ID | 9438284 |
|---|---|
| NAME | Huiliang Wang |
| GIVEN NAMES | Huiliang |
| FAMILY NAME | Wang |
| SIGNATURE | WANG H |
| AFFILIATIONS | Zhengzhou University |
| ORCID | 0000-0003-0057-455X |
| VERIFIED | Yes |
| TOTAL WORKS | 10 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 10 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Urban Flood Prediction Model Based on Explainable Deep Learning and Attention Mechanism
Deep learning models are widely used for urban flood prediction, but current research lacks a clear explanation of how indicator weight changes affect model accuracy. This study incorporated the attention mechanism between the convolutional and fully connected layers of the convolutional neural network (CNN) to enable the model to focus on critical flood-inducing factors, and employed the particle swarm optimization (PSO) to optimize the key hype…
Joint warning mechanism of urban flood considering comprehensive risk and emergency rescues
Reducing the losses to urban floods with a refined material allocation framework
Comprehensive resilience evaluation of water resources supply-demand system based on structure-function-operation mechanism under extreme drought
Risk prediction based on oversampling technology and ensemble model optimized by tree-structured parzed estimator
Study on adaptive regulation based on water supply-demand system structure and water use desirability under extreme drought
Threshold and real-time initiation mechanism of urban flood emergency response under combined disaster scenarios
Threshold effect of data amount and grid size on urban land use type identification using multi-source data fusion
Rapid prediction of urban flood based on disaster-breeding environment clustering and Bayesian optimized deep learning model in the coastal city
Study on the spatial distribution of water resource value in the agricultural system of the Yellow River Basin
To analyze the spatial distribution characteristics of water resource value in the agricultural system of the Yellow River Basin, this paper takes the Yellow River Basin as its research object and studies the spatial distribution characteristics and influencing factors of water resource value in the agricultural system using the emergy theory and method, the spatial autocorrelation analysis method, and the spatial regression model. The results sh…
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Study on the spatial distribution of water resource value in the agricultural system of the Yellow River Basin
To analyze the spatial distribution characteristics of water resource value in the agricultural system of the Yellow River Basin, this paper takes the Yellow River Basin as its research object and studies the spatial distribution characteristics and influencing factors of water resource value in the agricultural system using the emergy theory and method, the spatial autocorrelation analysis method, and the spatial regression model. The results sh…
Threshold effect of data amount and grid size on urban land use type identification using multi-source data fusion
Rapid prediction of urban flood based on disaster-breeding environment clustering and Bayesian optimized deep learning model in the coastal city
Risk prediction based on oversampling technology and ensemble model optimized by tree-structured parzed estimator
Study on adaptive regulation based on water supply-demand system structure and water use desirability under extreme drought
Threshold and real-time initiation mechanism of urban flood emergency response under combined disaster scenarios
Joint warning mechanism of urban flood considering comprehensive risk and emergency rescues
Reducing the losses to urban floods with a refined material allocation framework
Comprehensive resilience evaluation of water resources supply-demand system based on structure-function-operation mechanism under extreme drought
Urban Flood Prediction Model Based on Explainable Deep Learning and Attention Mechanism
Deep learning models are widely used for urban flood prediction, but current research lacks a clear explanation of how indicator weight changes affect model accuracy. This study incorporated the attention mechanism between the convolutional and fully connected layers of the convolutional neural network (CNN) to enable the model to focus on critical flood-inducing factors, and employed the particle swarm optimization (PSO) to optimize the key hype…
Flood Risk Assessment and Management (8 works) · Environmental Science (6 works) · Geography (6 works) · Computer Science (5 works) · Flood myth (5 works) · Artificial Intelligence (3 works) · Data mining (3 works) · Economics (3 works) · Engineering (3 works) · Hydrological Forecasting Using AI (3 works)