SITUZu-xiang,FENGWan-en,ZHONGQi-sheng,et al.Urban Flood Prediction Using Hybrid Deep Learning Model Based on Spatio-temporal Feature Fusion[J].China Water & Wastewater,2024,40(17):131-136.
Urban Flood Prediction Using Hybrid Deep Learning Model Based on Spatio-temporal Feature Fusion
China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU]
volume:
第40卷
Number:
第17期
Page:
131-136
Column:
Date of publication:
2024-09-01
- Abstract:
- The traditional physical models, while capable of accurately solving two-dimensional surface inundation calculations, are hindered by a complex construction process, high calibration difficulty, and low calculation efficiency. These limitations impede real-time flood prediction. As a crucial subset of artificial intelligence, deep learning has robust data processing and learning capabilities, offering a transformative and innovative technical approach for flood prediction. Consequently, a hybrid deep learning model for urban flood prediction was proposed based on spatio-temporal feature fusion technology. The SegNet-GRU hybrid model was developed by fully integrating the advantages of convolutional neural network and recurrent neural network in learning spatial and temporal data. The model demonstrated high accuracy in predicting the evolution process of the maximum water depth and surface water ponding under varying rainfall scenarios within the study area, with good prediction accuracy (mean absolute error, root-mean-square error, Nash-Sutcliffe efficiency coefficient, and Klin-Gupta efficiency coefficient at 0.008 5, 0.030 6, 0.962 7 and 0.694 9 respectively) as well as significantly improved processing speed (nearly 160 times faster than one- and two-dimensional models).
Last Update:
2024-09-01