High Perplexity Mountain Flood Level Forecasting in Small Watersheds Based on Compound Long Short-Term Memory Model and Multimodal Short Disaster-Causing Factors
Mountain flood water levels exhibit high variability and complexity, making them challenging to predict, and gathering long-term data of disaster-causing factors is difficult in small watersheds, the available disaster-causing variables are short-term multimodal data. In order to improve the accurac...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10990183/ |
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| Summary: | Mountain flood water levels exhibit high variability and complexity, making them challenging to predict, and gathering long-term data of disaster-causing factors is difficult in small watersheds, the available disaster-causing variables are short-term multimodal data. In order to improve the accuracy and real-time performance of mountain flood forecasting, we import Deep Learning (DL) model for mountain flood level prediction utilizing compound time series Long Short-Term Memory (LSTM) model and multimodal short data. On the basis of LSTM model, Convolutional Neural Networks (CNN) module is added to increase the short-term window prediction ability, and Attention module is further added to improve the prediction ability of complex water level changes, forming the compound LSTM, including LSTM-CNN and LSTM-CNN-Attention model. The data of multimodal short disaster-causing factors includes hydrology, meteorology, geography, etc., by integrating the short duration time series data for compound LSTM’s input data. This study evaluates the performance of advanced models on three test data set from representative small watersheds in Zhejiang Province, China, highlighting the effectiveness of the compound LSTM model in these specific areas. The findings emphasize the benefits of utilizing the compound LSTM model for flood forecasting. In particular, the LSTM-CNN-Attention model demonstrates enhanced accuracy and real-time processing capabilities. |
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| ISSN: | 2169-3536 |