Real-Time Streamflow Forecasting Framework, Implementation and Post-Analysis Using Deep Learning

Xiang, Zhongrun; Demir, İbrahim

doi:10.31223/x5bw6r

Cited by 6 publications

(6 citation statements)

References 35 publications

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“…We consider this work as a significant step towards creating better rainfall maps for hydrological modeling needs such as flood forecasting (Sit et (Gao et al, 2021). As for future directions of this research, we aim to extend this study for better interpolation accuracy as well as faster computation times by building more efficient neural networks to support operational flood forecasting needs (Xiang and Demir, 2022b). In order to train better models, various loss functions could be utilized, such as a loss function where the metrics we used in this are combined by differentiable weights.…”

Section: Discussionmentioning

confidence: 99%

TempNet – Temporal Super Resolution of Radar Rainfall Products with Residual CNNs

Sit¹,

Seo²,

Demir³

2022

Preprint

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The temporal and spatial resolution of rainfall data is crucial for environmental modeling studies in which its variability in space and time is considered as a primary factor. Rainfall products from different remote sensing instruments (e.g., radar, satellite) have different space-time resolutions because of the differences in their sensing capabilities and post-processing methods. In this study, we developed a deep learning approach that augments rainfall data with increased time resolutions to complement relatively lower resolution products. We propose a neural network architecture based on Convolutional Neural Networks (CNNs) to improve the temporal resolution of radar-based rainfall products and compare the proposed model with an optical flow-based interpolation method and CNN-baseline model. The methodology presented in this study could be used for enhancing rainfall maps with better temporal resolution and imputation of missing frames in sequences of 2D rainfall maps to support hydrological and flood forecasting studies.

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Section: Discussionmentioning

confidence: 99%

TempNet – Temporal Super Resolution of Radar Rainfall Products with Residual CNNs

Sit¹,

Seo²,

Demir³

2022

Preprint

Self Cite

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show abstract

“…In addition to LSTM and GRU, a variant of the Seq2Seq model (Xiang and Demir, 2022a) is also employed as a baseline method in this study. The Seq2Seq model follows an encoder-decoder architecture and utilizes multiple TimeDistributed layers with a final dense layer.…”

Section: Seq2seq Modelmentioning

confidence: 99%

Enhancing Hydrological Modeling with Transformers: A Case Study for 24-Hour Streamflow Prediction

Demiray,

Sit,

Mermer

et al. 2023

Preprint

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In this paper, we address the critical task of 24-hour streamflow forecasting using advanced deep-learning models, with a primary focus on the Transformer architecture which has seen limited application in this specific task. We compare the performance of five different models, including Persistence, LSTM, Seq2Seq, GRU, and Transformer, across four distinct regions. The evaluation is based on three performance metrics: Nash-Sutcliffe Efficiency (NSE), Pearson’s r, and Normalized Root Mean Square Error (NRMSE). Additionally, we investigate the impact of two data extension methods: zero-padding and persistence, on the model's predictive capabilities. Our findings highlight the Transformer's superiority in capturing complex temporal dependencies and patterns in the streamflow data, outperforming all other models in terms of both accuracy and reliability. The study's insights emphasize the significance of leveraging advanced deep learning techniques, such as the Transformer, in hydrological modeling and streamflow forecasting for effective water resource management and flood prediction.

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“…Workloads worsen the memory bottleneck that lowers the overall performance of systems where deep learning models are run since neural network workloads continue to have big memory footprints and significant computational requirements to attain improved accuracy (Inci 2022;Inci et al, 2022a). As deep learning models have become more proficient in many tasks, developing smaller neural network models in terms of trainable parameters has attracted interest from many researchers in the field (Inci et al, 2022b) to support operational needs in flood forecasting (Krajewski et al, 2021;Xiang and Demir, 2022) and inundation mapping (Hu and Demir, 2021;Li et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

EfficientRainNet: Smaller Neural Networks Based on EfficientNetV2 for Rainfall Nowcasting

Sit¹,

Seo²,

Demiray³

et al. 2023

Preprint

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Rainfall nowcasting provides short-term, high-resolution information on the location, intensity, and timing of rainfall, which is crucial for weather forecasting, flood warning, and emergency response. This can help people and organizations make informed decisions to mitigate the impact of severe weather events and reduce the risk of damage and loss of life. There are many attempts at tackling the problem at hand, whether it be numerical models or statistical models that also comprise deep neural networks. Even though nowcast models are quite accurate nowadays and the problem has a saturated literature, current approaches mostly focus on improving the nowcast performance while the computational burden keeps increasing. In this study, we propose EfficientRainNet, which is a convolutional neural network architecture that is based on mobile inverted residual linear bottleneck blocks with a few alterations. We show that EfficientRainNet is able to produce comparable results to those of encoder-decoder convolutional GRUs with only a fraction of the trainable parameters over a radar rainfall dataset for the State of Iowa. Also, for the most part, EfficientRainNet performs better than baselines using persistence- and optical flow-based nowcasting, along with another computational efficiency-focused neural network architecture, Small Attention UNet.

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Real-Time Streamflow Forecasting Framework, Implementation and Post-Analysis Using Deep Learning

Cited by 6 publications

References 35 publications

TempNet – Temporal Super Resolution of Radar Rainfall Products with Residual CNNs

TempNet – Temporal Super Resolution of Radar Rainfall Products with Residual CNNs

Enhancing Hydrological Modeling with Transformers: A Case Study for 24-Hour Streamflow Prediction

EfficientRainNet: Smaller Neural Networks Based on EfficientNetV2 for Rainfall Nowcasting

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