Designing Deep-based Learning Flood Forecast Model with ConvLSTM Hybrid Algorithm

Moishin, Mohammed and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Prasad, Ramendra and Raj, Nawin and Abdulla, Shahab ORCID: https://orcid.org/0000-0002-1193-6969 (2021) Designing Deep-based Learning Flood Forecast Model with ConvLSTM Hybrid Algorithm. IEEE Access, 9. pp. 50982-50993. ISSN 2169-3536

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Abstract

Efficient, robust, and accurate early flood warning is a pivotal decision support tool that can help save lives and protect the infrastructure in natural disasters. This research builds a hybrid deep learning (ConvLSTM) algorithm integrating the predictive merits of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Network to design and evaluate a flood forecasting model to forecast the future occurrence of flood events. Derived from precipitation dataset, the work adopts a Flood Index (IF), in form of a mathematical representation, to capture the gradual depletion of water resources over time, employed in a flood monitoring system to determine the duration, severity, and intensity of any flood situation. The newly designed predictive model utilizes statistically significant lagged IF, improved by antecedent and real-time rainfall data to forecast the next daily IF value. The performance of the proposed ConvLSTM model is validated against 9 different rainfall datasets in flood prone regions in Fiji which faces flood-driven devastations almost annually. The results illustrate the superiority of ConvLSTM-based flood model over the benchmark methods, all of which were tested at the 1-day, 3-day, 7-day, and the 14-day forecast horizon. For instance, the Root Mean Squared Error (RMSE) for the study sites were 0.101, 0.150, 0.211 and 0.279 for the four forecasted periods, respectively, using ConvLSTM model. For the next best model, the RMSE values were 0.105, 0.154, 0.213 and 0.282 in that same order for the four forecast horizons. In terms of the difference in model performance for individual stations, the Legate-McCabe Efficiency Index (LME) were 0.939, 0.898, 0.832 and 0.726 for the four forecast horizons, respectively. The results demonstrated practical utility of ConvLSTM in accurately forecasting IF and its potential use in disaster management and risk mitigation in the current phase of extreme weather events.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Faculty/School / Institute/Centre: Current - USQ College (8 Jun 2020 -)
Date Deposited: 17 Mar 2021 02:23
Last Modified: 27 Apr 2021 04:53
Uncontrolled Keywords: ConvLSTM, Deep Learning, Flood Forecasting, Flood Index, Flood Risk Management
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4699 Other information and computing sciences > 469999 Other information and computing sciences not elsewhere classified
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Socio-Economic Objectives (2020): 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280104 Expanding knowledge in built environment and design
Identification Number or DOI: https://doi.org/10.1109/ACCESS.2021.3065939
URI: http://eprints.usq.edu.au/id/eprint/41574

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