Flood prediction using deep learning models
Deep learning has recently appeared as one of the best reliable approaches for forecasting time series. Even though there are numerous data-driven models for flood prediction, most studies focus on prediction using a single flood variable. The creation of various data-driven models may require unfea...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
The Science and Information (SAI) Organization
2022
|
| Online Access: | http://eprints.utem.edu.my/id/eprint/26434/2/PAPER_112-FLOOD_PREDICTION_USING%20DEEP_LEARNING_MODELS%20CO%20AUTHOR.PDF http://eprints.utem.edu.my/id/eprint/26434/ https://thesai.org/Downloads/Volume13No9/Paper_112-Flood_Prediction_using%20Deep_Learning_Models.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Deep learning has recently appeared as one of the best reliable approaches for forecasting time series. Even though there are numerous data-driven models for flood prediction, most studies focus on prediction using a single flood variable. The creation of various data-driven models may require unfeasible computing resources when estimating multiple flood variables. Furthermore, the trends of several flood variables can only be revealed by analysing long-term historical observations, which conventional data-driven models do not adequately support. This study proposed a time series model with layer normalization and Leaky ReLU activation function in multivariable long-term short memory (LSTM), bidirectional long-term short memory (BILSTM) and deep recurrent neural network (DRNN). The proposed models were trained and evaluated by using the sensory historical data of river water level and rainfall in the east coast state of Malaysia. It were then, compared to the other six deep learning models. In terms of prediction accuracy, the experimental results also demonstrated that the deep recurrent neural network model with layer normalization and Leaky ReLU activation function performed better than other models. |
|---|