A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting

Jahangir, Mohammad Sina; You, John J.; Quilty, John

doi:10.1016/j.jhydrol.2023.129269

Cited by 12 publications

(2 citation statements)

References 96 publications

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“…The application of AI algorithms, and DL in particular, is growing in the geosciences and especially in the hydrosciences (Nourani et al, 2014(Nourani et al, , 2023Rajaee et al, 2019), thanks to the increase in computational resources, but also to the growing availability of global datasets for different hydrological variables (Addor et al, 2017;Kratzert et al, 2023), which are making it possible to better address issues related to the understanding and management of hydrological systems (Muñoz-60 Carpena et al, 2023).This has been confirmed in several recent studies that have highlighted the potential of deep learning tools for hydrological simulations (Fang et al, 2022;Klotz et al, 2022;Kratzert et al, 2019Kratzert et al, , 2021Nourani et al, 2021) and forecasting tasks (Jahangir et al, 2023;Momeneh https://doi.org/10.5194/egusphere-2024-794 Preprint. Discussion started: 13 May 2024 c Author(s) 2024.…”

Section: Introduction 35mentioning

confidence: 90%

Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what’s the best way to leverage regionalised information?

Chidepudi,

Massei,

Jardani

et al. 2024

Preprint

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Abstract. In this study, we used deep learning models with recurrent structure neural networks to simulate large-scale groundwater level (GWL) fluctuations in northern France. We developed a multi-station collective training for GWL simulations, using both “dynamic” variables (i.e. climatic) and static aquifer characteristics. This large-scale approach offers the possibility of incorporating dynamic and static features to cover more reservoir heterogeneities in the study area. Further, we investigated the performance of relevant feature extraction techniques such as clustering and wavelet transform decomposition, intending to simplify network learning using regionalised information. Several modelling performance tests were conducted. Models specifically trained on different types of GWL, clustered based on the spectral properties of the data, performed significantly better than models trained on the whole dataset. Clustering-based modelling reduces complexity in the training data and targets relevant information more efficiently. Applying multi-station models without prior clustering can lead the models to learn the dominant station behavior preferentially, ignoring unique local variations. In this respect, wavelet pre-processing was found to partially compensate clustering, bringing out common temporal and spectral characteristics shared by all available time series even when these characteristics are “hidden” because of too small amplitude. When employed along with prior clustering, thanks to its capability of capturing essential features across all time scales (high and low), wavelet decomposition used as a pre-processing technique provided significant improvement in model performance, particularly for GWLs dominated by low-frequency variations. This study advances our understanding of GWL simulation using deep learning, highlighting the importance of different model training approaches, the potential of wavelet preprocessing, and the value of incorporating static attributes.

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Section: Introduction 35mentioning

confidence: 90%

Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what’s the best way to leverage regionalised information?

Chidepudi,

Massei,

Jardani

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Pingping Luo, et al [65] propose that accuracy and computational efficiency are two key areas, that hinder the improvement of urban flood model quality. Mohammad Sina Jahangir, et al [66] propose the model performance sensitive to precipitation forecasting accuracy. DL models 's accuracy can be improved by optimization the input data and hyperparameter [67].…”

Section: Related Workmentioning

confidence: 99%

Uncertainty Forecasting Model for Mountain Flood Based on Bayesian Deep Learning

Wang,

2024

IEEE Access

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Due to the characteristics of strong suddenness, high harmfulness, and frequent occurrence of mountain flood disasters in small watersheds, the accuracy and reliability of mountain flood forecasting are insufficient in small watersheds. This paper studies key theories and technologies, that is the uncertainty forecasting model based on hydrologic physical mechanism. We design the Bayesian Deep Learning (DL) forecasting models, it is suitable for the transfer of spatiotemporal factors caused by mountain floods to disaster probability. The models include Bayesian Linear and Bayesian Long Short-Term Memory (LSTM) model, we hope to achieve an acceptable balance between reliability (uncertainty confidence coverage) and accuracy (confidence interval width). Meanwhile, we extract effective information from multi-source and multi-dimensional hazard factors' big data. The experiment shows the differences between Bayesian DL models, the models have long-term probability forecasting ability at both, but Bayesian LSTM is superior to Bayesian Linear in terms of reliability, accuracy and computational consumption.

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Deep dependence in hydroclimatological variables

Lee,

Kim

2024

Appl Intell

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A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting

Cited by 12 publications

References 96 publications

Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what’s the best way to leverage regionalised information?

Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what’s the best way to leverage regionalised information?

Uncertainty Forecasting Model for Mountain Flood Based on Bayesian Deep Learning

Deep dependence in hydroclimatological variables

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