Improving long-term streamflow prediction in a poorly gauged basin using geo-spatiotemporal mesoscale data and attention-based deep learning: A comparative study

Ghobadi, Fatemeh; Kang, Doosun

doi:10.1016/j.jhydrol.2022.128608

Cited by 41 publications

(7 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the application of multi-step ahead (MSA) multivariate not only increases complexity but also intricates the curse of dimensionality prediction, thereby exacerbating the situation. The predictability of MSA multivariate prediction against MSA univariate prediction has been discussed in several studies [16,26]. MSA multivariate prediction is a complex forecasting approach that considers multiple data series, with each series representing a different variable or feature [16,17,27].…”

Section: Research Background and Motivationmentioning

confidence: 99%

“…The predictability of MSA multivariate prediction against MSA univariate prediction has been discussed in several studies [16,26]. MSA multivariate prediction is a complex forecasting approach that considers multiple data series, with each series representing a different variable or feature [16,17,27]. In particular, MSA multivariate prediction models are more susceptible to dimensionality with rising number of variables.…”

Section: Research Background and Motivationmentioning

confidence: 99%

“…To replicate this complex process, hydrological models have been crafted using diverse approaches, encompassing physics-based and numerical methods, as well as various data-driven techniques [13,14]. Hydrological models are broadly classified into two categories: physical-driven and data-driven models [15][16][17]. Physical models face limitations in dealing with straightforward hydrological processes because of the intricate relationships between environmental variables and streamflow [18].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Feature Extraction from Satellite-Derived Hydroclimate Data: Assessing Impacts on Various Neural Networks for Multi-Step Ahead Streamflow Prediction

Ghobadi,

Tayerani Charmchi,

Kang

2023

Sustainability

Self Cite

View full text Add to dashboard Cite

Enhancing the generalization capability of time-series models for streamflow prediction using dimensionality reduction (DR) techniques remains a major challenge in water resources management (WRM). In this study, we investigated eight DR techniques and their effectiveness in mitigating the curse of dimensionality, which hinders the performance of machine learning (ML) algorithms in the field of WRM. Our study delves into the most non-linear unsupervised representative DR techniques, including principal component analysis (PCA), kernel PCA (KPCA), multi-dimensional scaling (MDS), isometric mapping (ISOMAP), locally linear embedding (LLE), t-distributed stochastic neighbor embedding (t-SNE), Laplacian eigenmaps (LE), and autoencoder (AE), examining their effectiveness in multi-step ahead (MSA) streamflow prediction. In this study, we conducted a conceptual comparison of these techniques. Subsequently, we focused on their performance in four different case studies in the USA. Moreover, we assessed the quality of the transformed feature spaces in terms of the MSA streamflow prediction improvement. Through our investigation, we gained valuable insights into the performance of different DR techniques within linear/dense/convolutional neural network (CNN)/long short-term memory neural network (LSTM) and autoregressive LSTM (AR-LSTM) architectures. This study contributes to a deeper understanding of suitable feature extraction techniques for enhancing the capabilities of the LSTM model in tackling high-dimensional datasets in the realm of WRM.

show abstract

Section: Research Background and Motivationmentioning

confidence: 99%

Section: Research Background and Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Feature Extraction from Satellite-Derived Hydroclimate Data: Assessing Impacts on Various Neural Networks for Multi-Step Ahead Streamflow Prediction

Ghobadi,

Tayerani Charmchi,

Kang

2023

Sustainability

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, deep structure of LSTM causes its shortcomings such as over-fitting and time lag, which affects the predic tion accuracy (Xu et al 2022). Therefore, Ghobadi et al (2022) proposed that a Transformer based on self-attention is used for water level prediction, demonstrating its special ability to predict water level time series.…”

Section: Introductionmentioning

confidence: 99%

Short term prediction of water level based on deep learning during the flood season, in the downstream area of The Three Gorges Reservoir

Mao,

Xiong,

et al. 2024

Preprint

View full text Add to dashboard Cite

Accurately predicting river water levels is crucial for managing water resources and controlling floods. In this study, we propose a water level prediction model based on a deep learning method (Transformer model) to improve the accuracy and efficiency of predicting inland river water levels. Water level data from seven hydrological stations were collected from the downstream area of the Three Gorges Reservoir, which confirmed the effectiveness of the model. the proposed model was improved by three main algorithms: the wavelet thresholding denoising (WTD) algorithm, the maximum information coefficient (MIC) algorithm, and the linear exponential (LIMEX) loss function. We are dedicated to improving the accuracy of predicting peak water levels during flood seasons. The results show that the proposed MIC-TF-LINEX model has achieved superior performance in predicting water levels compared to other models, such as traditional Transformer, Back Propagation (BP) Neural Network, and Bi-directional Long Short Term Memory (BILSTM). Furthermore, extending the forecast period will also affect the accuracy of the water level forecasting model. When the prediction duration is 8 hours, the R2 value is 0.9989, the MAE is 0.1020, the MSE is 0.0166, and the MAPE is 0.0060. When the prediction timeframe is within 56 hours, the MSE of the prediction result is still less than 0.1 meters. This study provides a highly accurate and well-suited method for predicting floods.

show abstract

“…Secondly, deep learning models often lack intuitive interpretability [34,35]. Although LSTM has good accuracy in predicting flow data over time, the inability to verify the predicted results hinders the practical application of deep learning methods [36,37].…”

Section: Introductionmentioning

confidence: 99%

Water Flow Prediction Based on Improved Spatiotemporal Attention Mechanism of Long Short-Term Memory Network

Hu,

Yu,

Yan

et al. 2024

Water

View full text Add to dashboard Cite

The prediction of water plant flow should establish relationships between upstream and downstream hydrological stations, which is crucial for the early detection of flow anomalies. Long Short-Term Memory Networks (LSTMs) have been widely applied in hydrological time series forecasting. However, due to the highly nonlinear and dynamic nature of hydrological time series, as well as the intertwined coupling of data between multiple hydrological stations, the original LSTM models fail to simultaneously consider the spatiotemporal correlations among input sequences for flow prediction. To address this issue, we propose a novel flow prediction method based on the Spatiotemporal Attention LSTM (STA-LSTM) model. This model, based on an encoder–decoder architecture, integrates spatial attention mechanisms in the encoder to adaptively capture hydrological variables relevant to prediction. The decoder combines temporal attention mechanisms to better propagate gradient information and dynamically discover key encoder hidden states from all time steps within a window. Additionally, we construct an extended dataset, which preprocesses meteorological data with forward filling and rainfall encoding, and combines hydrological data from multiple neighboring pumping stations with external meteorological data to enhance the modeling capability of spatiotemporal relationships. In this paper, the actual production data of pumping stations and water plants along the East-to-West Water Diversion Project are taken as examples to verify the effectiveness of the model. Experimental results demonstrate that our STA-LSTM model can better capture spatiotemporal relationships, yielding improved prediction performance with a mean absolute error (MAE) of 3.57, a root mean square error (RMSE) of 4.61, and a mean absolute percentage error (MAPE) of 0.001. Additionally, our model achieved a 3.96% increase in R2 compared to the baseline model.

show abstract

Improving long-term streamflow prediction in a poorly gauged basin using geo-spatiotemporal mesoscale data and attention-based deep learning: A comparative study

Cited by 41 publications

References 51 publications

Feature Extraction from Satellite-Derived Hydroclimate Data: Assessing Impacts on Various Neural Networks for Multi-Step Ahead Streamflow Prediction

Feature Extraction from Satellite-Derived Hydroclimate Data: Assessing Impacts on Various Neural Networks for Multi-Step Ahead Streamflow Prediction

Short term prediction of water level based on deep learning during the flood season, in the downstream area of The Three Gorges Reservoir

Water Flow Prediction Based on Improved Spatiotemporal Attention Mechanism of Long Short-Term Memory Network

Contact Info

Product

Resources

About