A novel hybrid XAJ-LSTM model for multi-step-ahead flood forecasting

Cui, Zhen; Zhou, Yanlai; Guo, Shenglian; Wang, Jun; Ba, Huanhuan; He, Shaokun

doi:10.2166/nh.2021.016

Cited by 29 publications

(10 citation statements)

References 64 publications

(87 reference statements)

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“…In Asia, especially China, Xinanjiang is the most dominant non-AI model for flood forecasting [71][72][73][74] and, at times, it is coupled with AI algorithms like ANNs for superior accuracy [75,76].…”

Section: Deterministic Modelsmentioning

confidence: 99%

Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review

Byaruhanga,

Kibirige,

Gokool

et al. 2024

Water

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Floods are recognised as one of the most destructive and costliest natural disasters in the world, which impact the lives and livelihoods of millions of people. To tackle the risks associated with flood disasters, there is a need to think beyond structural interventions for flood protection and move to more non-structural ones, such as flood early warning systems (FEWSs). Firstly, this study aimed to uncover how flood forecasting models in the FEWSs have evolved over the past three decades, 1993 to 2023, and to identify challenges and unearth opportunities to assist in model selection for flood prediction. Secondly, the study aimed to assist in model selection and, in return, point to the data and other modelling components required to develop an operational flood early warning system with a focus on data-scarce regions. The scoping literature review (SLR) was carried out through a standardised procedure known as Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The SLR was conducted using the electronic databases Scopus and Web of Science (WoS) from 1993 until 2023. The results of the SLR found that between 1993 and 2010, time series models (TSMs) were the most dominant models in flood prediction and machine learning (ML) models, mostly artificial neural networks (ANNs), have been the most dominant models from 2011 to present. Additionally, the study found that coupling hydrological, hydraulic, and artificial neural networks (ANN) is the most used ensemble for flooding forecasting in FEWSs due to superior accuracy and ability to bring out uncertainties in the system. The study recognised that there is a challenge of ungauged and poorly gauged rainfall stations in developing countries. This leads to data-scarce situations where ML algorithms like ANNs are required to predict floods. On the other hand, there are opportunities to use Satellite Precipitation Products (SPP) to replace missing or poorly gauged rainfall stations. Finally, the study recommended that interdisciplinary, institutional, and multisectoral collaborations be embraced to bridge this gap so that knowledge is shared for a faster-paced advancement of flood early warning systems.

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Section: Deterministic Modelsmentioning

confidence: 99%

Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review

Byaruhanga,

Kibirige,

Gokool

et al. 2024

Water

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show abstract

“…Other efforts to interpret the results of LSTM-based representations have included the incorporation of physical constraints such as mass conservation (Hoedt et al, 2021), feature contexts in some of the gates (Kratzert et al, 2019), post-analysis of the states 70 (Lees et al, 2022), and use of ML-based models coupled with conceptual models (Khandelwal et al, 2020;Cho and Kim, 2022;Cui et al, 2021). However, these previous approaches have not explicitly exploited the isomorphism between the structures of the https://doi.org/10.5194/egusphere-2023-666 Preprint.…”

Section: Previous Work On the Interpretability Issue 50mentioning

confidence: 99%

Towards Interpretable LSTM-based Modelling of Hydrological Systems

Fuente

Ehsani

Gupta

et al. 2023

Preprint

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Abstract. Several studies have demonstrated the ability of Long Short-Term Memory (LSTM) machine learning based modeling to outperform traditional spatially lumped process-based modeling approaches for streamflow prediction. However, due mainly to the structural complexity of the LSTM network (which includes gating operations and sequential processing of the data), difficulties can arise when interpreting the internal processes and weights in the model. Here, we propose and test a modification of LSTM architecture that represents internal system processes in a manner that is analogous to a hydrological reservoir. Our architecture, called HydroLSTM, simulates behaviors inherent in a dynamic system, such as sequential updating of the Markovian storage. Specifically, we modify how data is fed to the new representation to facilitate simultaneous access to past lagged inputs, thereby explicitly acknowledging the importance of trends and patterns in the data. We compare the performance of the HydroLSTM and LSTM architectures using data from 10 hydro-climatically varied catchments. We further examine how the new architecture exploits the information in lagged inputs, for 588 catchments across the USA. The HydroLSTM-based models require fewer cell states to obtain similar performance to their LSTM-based counterparts. Further, the weights patterns associated with lagged input variables are interpretable and consistent with regional hydroclimatic characteristics (snowmelt-dominated, recent rainfall-dominated, and historical rainfall-dominated). These findings illustrate how the hydrological interpretability of LSTM-based models can be enhanced by appropriate architectural modifications that are physically and conceptually consistent with our understanding of the system.

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“…Long and short term memory neural networks (LSTM) are widely used for flood forecasting by continuously storing useful information by memory neurons for time series prediction. However, the choice of hyperparameters for LSTM models has a large impact on the prediction performance of the models [14][15]. Monolithic models usually have the problems of poor generalization ability and low prediction accuracy, and hybrid models can effectively solve the limitations of monolithic models.…”

Section: Introductionmentioning

confidence: 99%

Flood Forecasting Method Based on Improved VMD-FOS-QR-RBL

Liu

Li-hu

et al. 2023

IEEE Access

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To improve the accuracy, reliability and validity of flood prediction models, this study proposes a regularized broad learning (RBL) model based on an improved variational mode decomposition (VMD). Firstly, grey correlation analysis was used to improve the endpoint effect of the VMD and the particle swarm optimisation (PSO) algorithm was used to optimise the VMD parameters. Then, using orthogonal triangular decomposition (QR), redefining the hidden layer output of BL model and adding forgettable online sequence learning mechanism (FOS) to construct online sequence BL (FOS-QR-RBL), which can significantly improve the computational efficiency of BL model. Finally, a flood forecasting method based on improved VMD-FOS-QR-RBL was constructed by combining the FOS-QR-RBL with the improved VMD and applying it to regional flood forecasting. The experimental results show that the computational efficiency of FOS-QR-RBL is improved by 35% and 23.68% compared with RBL and QR-RBL, respectively. The mean absolute error (MAE) of the coupling model of VMD and FOS-QR-RBL is reduced by 80.30% and 84.10% respectively, and the nash efficiency coefficient (Ens) is increased by 15.51% and 28.16% respectively, compared with that of the coupling model of FOS-QR-RBL with ensemble empirical mode decomposition (EEMD) and adaptive noise complete ensemble empirical mode decomposition (CEEMDAN). The results of the optimal operation based on VMD-FOS-OR-RBL show that the model can effectively reduce the economic losses caused by regional flooding.

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A novel hybrid XAJ-LSTM model for multi-step-ahead flood forecasting

Cited by 29 publications

References 64 publications

Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review

Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review

Towards Interpretable LSTM-based Modelling of Hydrological Systems

Flood Forecasting Method Based on Improved VMD-FOS-QR-RBL

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