Impacts of River Engineering on Multi-Decadal Water Discharge of the Mega-Changjiang River

Ma, Baosong; Pang, Wenhong; Lou, Yaying; Mei, Xuefei; Wang, Jie; Gu, Jie; Dai, Zhijun

doi:10.3390/su12198060

Cited by 4 publications

(1 citation statement)

References 41 publications

(54 reference statements)

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“…River discharge forecasting is considered a pivotal task in various fields of water resource management i.e., flood control, irrigation planning, and hydropower production [1][2][3][4][5][6][7]. River discharge has a significant impact on the physical, chemical, and biological activities in the river contributing high correlation to the fluvial ecosystem [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Exploring Temporal Dynamics of River Discharge using Univariate Long Short-Term Memory (LSTM) Recurrent Neural Network at East Branch of Delaware River

Mehedi¹,

Khosravi²,

Yazdan³

et al. 2022

Preprint

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River flow prediction is a pivotal task in the field of water resource management during the era of rapid climate change. The highly dynamic and evolving nature of the climatic variables e.g., precipitation has a significant impact on the temporal distribution of the river discharge in recent days making the discharge forecasting even more complicated for diversified water-related issues e.g., flood prediction and irrigation planning. To predict the discharge, various physics-based numerical models are used using numerous hydrologic parameters. Extensive lab-based investigation and calibration are required to reduce the uncertainty involved in those parameters. However, in the age of data-driven predictions, several deep learning algorithms showed satisfactory performance in dealing with sequential data. In this research, Long Short-term Memory (LSTM) neural network regression model is trained using over 80 years of daily data to forecast the discharge time series up to 3 days ahead of time. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors and Root Mean Squared Error (RMSE) value of 0.09. Higher performance is achieved through the increase in the number of epochs and hyper parameter tuning. This model can be transferred to other locations with proper feature engineering and optimization to perform univariate predictive analysis and potentially be used to perform real-time river discharge prediction.

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Section: Introductionmentioning

confidence: 99%

Exploring Temporal Dynamics of River Discharge using Univariate Long Short-Term Memory (LSTM) Recurrent Neural Network at East Branch of Delaware River

Mehedi¹,

Khosravi²,

Yazdan³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

Synergistic evolution and attribution analysis of water-sediment in the middle and lower reaches of the Yangtze River

Guo,

Wang,

Jiao

et al. 2024

Journal of Hydrology: Regional Studies

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Exploring Temporal Dynamics of River Discharge Using Univariate Long Short-Term Memory (LSTM) Recurrent Neural Network at East Branch of Delaware River

et al. 2022

View full text Add to dashboard Cite

River flow prediction is a pivotal task in the field of water resource management during the era of rapid climate change. The highly dynamic and evolving nature of the climatic variables, e.g., precipitation, has a significant impact on the temporal distribution of the river discharge in recent days, making the discharge forecasting even more complicated for diversified water-related issues, e.g., flood prediction and irrigation planning. In order to predict the discharge, various physics-based numerical models are used using numerous hydrologic parameters. Extensive lab-based investigation and calibration are required to reduce the uncertainty involved in those parameters. However, in the age of data-driven predictions, several deep learning algorithms showed satisfactory performance in dealing with sequential data. In this research, Long Short-term Memory (LSTM) neural network regression model is trained using over 80 years of daily data to forecast the discharge time series up to seven days ahead of time. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors, and R2 value of 0.93 with one day lead time. Higher performance is achieved through the increase in the number of epochs and hyperparameter tuning. This model can be transferred to other locations with proper feature engineering and optimization to perform univariate predictive analysis and potentially be used to perform real-time river discharge prediction.

show abstract

Impacts of River Engineering on Multi-Decadal Water Discharge of the Mega-Changjiang River

Cited by 4 publications

References 41 publications

Exploring Temporal Dynamics of River Discharge using Univariate Long Short-Term Memory (LSTM) Recurrent Neural Network at East Branch of Delaware River

Exploring Temporal Dynamics of River Discharge using Univariate Long Short-Term Memory (LSTM) Recurrent Neural Network at East Branch of Delaware River

Synergistic evolution and attribution analysis of water-sediment in the middle and lower reaches of the Yangtze River

Exploring Temporal Dynamics of River Discharge Using Univariate Long Short-Term Memory (LSTM) Recurrent Neural Network at East Branch of Delaware River

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