Real-time prediction of the week-ahead flood index using hybrid deep learning algorithms with synoptic climate mode indices
Article
Article Title | Real-time prediction of the week-ahead flood index using hybrid deep learning algorithms with synoptic climate mode indices |
---|---|
ERA Journal ID | 124541 |
Article Category | Article |
Authors | Ahmed, A.A. Masrur, Akther, Shahida, Nguyen-Huy, Thong, Raj, Nawin, Jui (Student), S Janifer and S.Z., Farzana |
Journal Title | Journal of Hydro-Environment Research |
Journal Citation | 57, pp. 12-26 |
Number of Pages | 15 |
Year | 2024 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 1570-6443 |
1876-4444 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.jher.2024.09.001 |
Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S1570644324000522 |
Abstract | This paper aims to propose a hybrid deep learning (DL) model that combines a convolutional neural network (CNN) with a bi-directional long-short term memory (BiLSTM) for week-ahead prediction of daily flood index (IF) for Bangladesh. The neighbourhood component analysis (NCA) is assigned for significant feature selection with synoptic-scale climatic indicators. The results successfully reveal that the hybrid CNN-BiLSTM model outperforms the respective benchmark models based on forecasting capability, as supported by a minimal mean absolute error and high-efficiency metrics. With respect to IF prediction, the hybrid CNN-BiLSTM model shows over 98% of the prediction errors were less than 0.015, resulting in a low relative error and superiority performance against the benchmark models in this study. The adaptability and potential utility of the suggested model may be helpful in subsequent flood monitoring and may also be beneficial to policymakers at the federal and state levels. |
Keywords | flood index; Bangladesh; feature extraction; deep hybrid learning; climate indices |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 410199. Climate change impacts and adaptation not elsewhere classified |
460207. Modelling and simulation | |
Public Notes | The accessible file is the submitted version of the paper. Please refer to the URL for the published version. |
Byline Affiliations | Department of Climate Change, Energy, the Environment and Water, Canberra |
School of Mathematics, Physics and Computing | |
Leading University, Bangladesh | |
Centre for Applied Climate Sciences | |
Thanh Do University, Vietnam |
https://research.usq.edu.au/item/zq050/real-time-prediction-of-the-week-ahead-flood-index-using-hybrid-deep-learning-algorithms-with-synoptic-climate-mode-indices
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Nguyen-Huy, Thong. 2020. "Copula-based statistical modelling of synoptic-scale climate indices for quantifying and managing agricultural risks in Australia." Bulletin of the Australian Mathematical Society. 101 (1), pp. 166-169. https://doi.org/10.1017/S0004972719001217Drought Outlook Products Review
Cobon, David, Nguyen-Huy, Thong and Reardon-Smith, Kate. 2019. Drought Outlook Products Review. Toowoomba, Australia. University of Southern Queensland.Methodology for producing the Drought Monitor
Pudmenzky, Christa, Guillory, Laura, Cobon, David H. and Nguyen-Huy, Thong. 2020. Methodology for producing the Drought Monitor. Toowoomba, Queensland. University of Southern Queensland.Northern Australia Climate Program: supporting adaptation in rangeland grazing systems through more targeted climate forecasts, improved drought information and an innovative extension program
Cobon, David, Jarvis, Chelsea, Reardon-Smith, Kate, Guillory, Laura, Pudmenzky, Christa, Nguyen-Huy, Thong, Mushtaq, Shahbaz and Stone, Roger. 2021. "Northern Australia Climate Program: supporting adaptation in rangeland grazing systems through more targeted climate forecasts, improved drought information and an innovative extension program." The Rangeland Journal. 43 (3), pp. 87-100. https://doi.org/10.1071/RJ20074Novel hybrid deep learning model for satellite based PM10 forecasting in the most polluted Australian hotspots
Sharma, Ekta, Deo, Ravinesh C., Soar, Jeffrey, Prasad, Ramendra, Parisi, Alfio V. and Raj, Nawin. 2022. "Novel hybrid deep learning model for satellite based PM10 forecasting in the most polluted Australian hotspots." Atmospheric Environment. 279, pp. 1-13. https://doi.org/10.1016/j.atmosenv.2022.119111New double decomposition deep learning methods for river water level forecasting
Ahmed, A. A. Masrur, Deo, Ravinesh C., Ghahramani, Afshin, Feng, Qi, Raj, Nawin, Yin, Zhenliang and Yang, Linshan. 2022. "New double decomposition deep learning methods for river water level forecasting." Science of the Total Environment. 831, pp. 1-21. https://doi.org/10.1016/j.scitotenv.2022.154722Global disparities in agricultural climate index-based insurance research
Adeyinka, Adewuyi Ayodele, Kath, Jarrod, Nguyen-Huy, Thong, Mushtaq, Shahbaz, Souvignet, Maxime, Range, Matthias and Barratt, Jonathan. 2022. "Global disparities in agricultural climate index-based insurance research." Climate Risk Management. 35, pp. 1-15. https://doi.org/10.1016/j.crm.2022.100394Spatiotemporal Hybrid Random Forest Model for Tea Yield Prediction Using Satellite-Derived Variables
Jui, S. Janifer Jabin, Ahmed, A. A. Masrur, Bose, Aditi, Raj, Nawin, Sharma, Ekta, Soar, Jeffrey and Chowdhury, Md Wasique Islam. 2022. "Spatiotemporal Hybrid Random Forest Model for Tea Yield Prediction Using Satellite-Derived Variables." Remote Sensing. 14 (3), pp. 1-18. https://doi.org/10.3390/rs14030805Creating streamtubes based on mass conservative streamlines
Raj, Nawin and Li, Zhenquan. 2008. "Creating streamtubes based on mass conservative streamlines." International Journal of Mathematical, Physical and Engineering Sciences. 2 (1), pp. 41-45. https://doi.org/10.5281/zenodo.1081061Optimization algorithms as training approach with hybrid deep learning methods to develop an ultraviolet index forecasting model
Ahmed, A. A. Masrur, Ahmed, Mohammad Hafez, Saha, Sanjoy Kanti, Ahmed, Oli and Sutradhar, Ambica. 2022. "Optimization algorithms as training approach with hybrid deep learning methods to develop an ultraviolet index forecasting model." Stochastic Environmental Research and Risk Assessment. 36 (10), pp. 3011-3039. https://doi.org/10.1007/s00477-022-02177-3Prediction of Sea Level with Vertical Land Movement Correction Using Deep Learning
Raj, Nawin. 2022. "Prediction of Sea Level with Vertical Land Movement Correction Using Deep Learning." Mathematics. 10 (23). https://doi.org/10.3390/math10234533Drought outlook validation using ACCESS-S2 hindcast
Nguyen-Huy, Thong and Cobon, David. 2022. Drought outlook validation using ACCESS-S2 hindcast. Northern Australia Climate Program (NACP).Systemic risk management in farming - geographic distribution and diversity
Nguyen-Huy, Thong. 2022. "Systemic risk management in farming - geographic distribution and diversity." Future Drought Fund’s 2022 Science to Practice Forum. 07 - 08 Jun 2022 Australia.Development of Deep Learning Hybrid Models for Hydrological Predictions
Ahmed, Abul Abrar Masrur. 2022. Development of Deep Learning Hybrid Models for Hydrological Predictions. PhD by Publication Doctor of Philosophy. University of Southern Queensland. https://doi.org/10.26192/q7q5z