Long-term probabilistic streamflow forecast model with “inputs–structure–parameters” hierarchical optimization framework

Mo, Ran; Xu, Bin; Zhong, Ping‐an; Dong, Yunlong; Han, W. Y.; Hao, Yue; Zhu, Jieshun; Wang, Huili; Wang, Guoqing; Zhang, Jianyun

doi:10.1016/j.jhydrol.2023.129736

Cited by 9 publications

(2 citation statements)

References 54 publications

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“…However, the interannual variation law of runoff decomposed by VMD has changed, which means that the L value of the hybrid model cannot be set to 12 directly, and the optimal value of L needs to be determined by repeated debugging. Likewise, considering that the effect of atmospheric circulation on runoff has a lag time [48,49], the optimal value of L needs to be determined by repeated debugging.…”

Section: Vmd-pca-lstmmentioning

confidence: 99%

Runoff Prediction in Different Forecast Periods via a Hybrid Machine Learning Model for Ganjiang River Basin, China

Wang,

Tang,

Zou

et al. 2024

Water

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Accurate forecasting of monthly runoff is essential for efficient management, allocation, and utilization of water resources. To improve the prediction accuracy of monthly runoff, the long and short memory neural networks (LSTM) coupled with variational mode decomposition (VMD) and principal component analysis (PCA), namely VMD-PCA-LSTM, was developed and applied at the Waizhou station in the Ganjiang River Basin. The process begins with identifying the main forecasting factors from 130 atmospheric circulation indexes using the PCA method and extracting the stationary components from the original monthly runoff series using the VMD method. Then, the correlation coefficient method is used to determine the lag of the above factors. Lastly, the monthly runoff is simulated by combining the stationary components and key forecasting factors via the LSTM model. Results show that the VMD-PCA-LSTM model effectively addresses the issue of low prediction accuracy at high flows caused by a limited number of samples. Compared to the single LSTM and VMD-LSTM models, this comprehensive approach significantly enhances the model’s predictive accuracy, particularly during the flood season.

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Section: Vmd-pca-lstmmentioning

confidence: 99%

Runoff Prediction in Different Forecast Periods via a Hybrid Machine Learning Model for Ganjiang River Basin, China

Wang,

Tang,

Zou

et al. 2024

Water

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“…ANNs have been shown to perform accurately in various fields of water resources. Streamflow forecasting (Chitsaz et al 2016;Yaseen et al 2018;Araghinejad et al 2018;Thakur et al, 2020, Ghobadi & Kang;Mo et al, 2023;Tyson et al, 2023), predicting groundwater levels (Khalil et al 2015;Naderianfar et al 2017;Sahoo et al, 2017 ;Sattari et al, 2018;Dadhich et al, 2021;Navale & Mhaske, 2023), drought forecasting (Hosseini-Moghari and Araghinejad 2015;Mokhtarzad et al 2017;Khan et al, 2020;Alawsi et al, 2022), flood forecasting (Latt and Wittenberg 2014;Alexander and Thampi 2018;Dtissibe et al, 2020;Wang et al, 2022), sediment estimation (Banihabib and Emami 2017;Zounemat-Kermani et al 2018;Banadkooki et al, 2020;Yadav et al, 2022) and evaporation modeling (Antonopoulos 2016;Nourani et al, 2020;Arya Azar et al, 2023) are the most common uses of ANN in hydrology. In many studies, R-R modeling is conducted by employing an ANN.…”

Section: Introductionmentioning

confidence: 99%

Application of Gridded Precipitation Datasets for Simulating Discharge along with Hybrid Machine Learning Methods: An Operational Approach for Poorly Gauged Basins

Morovati

2023

Preprint

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In many regions, there is no long-term discharge data which do not include any gaps. In this work, we have tried to overcome these limitations with the use of gridded precipitation datasets and data-driven modeling. To this end, the Multilayer Perceptron Neural Network (MLPNN), as a Rainfall-Runoff (R-R) model was taken into account to simulate the discharge of the Karkheh basin in Iran. Precipitation data was extracted from Asian Precipitation-Highly Resolved Observational Data Integration Toward Evaluation (APHRODITE), Global Precipitation Climatology Center (GPCC) and Climatic Research Unit (CRU) datasets. MLPNN training was implemented using the Levenberg-Marquardt (LM) algorithm and the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) were used to pre-process input data for the MLPNN as well. Two scenarios were considered for R-R modeling. In Scenario1 (S1), the model was calibrated via in situ data and the dataset data was used in the testing phase. In Scenario 2 (S2), the model was calibrated and examined separately based on each dataset. The results showed that in S1, APHRODITE outperformed the other two datasets. All dataset functions were improved in S2. To sum up, the best performance of APHRODITE, GPCC, and CRU is related to hybrid applications of S2-PCA-NSGA-II, S2-SVD-NSGA-II, and S2-SVD-NSGA-II, respectively. Our results indicate that, the main error found in the gridded precipitation dataset is related to bias error which will be disappeared automatically when the model is calibrated using gridded precipitation datasets, suggesting that the bias correction or re-calibration of existing models are required. The results illustrate high potential of gridded precipitation dataset and data-driven models in runoff simulation or filling the gaps existed in observed data.

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Study on runoff forecasting and error correction driven by atmosphere–ocean-land dataset

Chang,

Guo,

Liu

et al. 2025

Expert Systems with Applications

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Long-term probabilistic streamflow forecast model with “inputs–structure–parameters” hierarchical optimization framework

Cited by 9 publications

References 54 publications

Runoff Prediction in Different Forecast Periods via a Hybrid Machine Learning Model for Ganjiang River Basin, China

Runoff Prediction in Different Forecast Periods via a Hybrid Machine Learning Model for Ganjiang River Basin, China

Application of Gridded Precipitation Datasets for Simulating Discharge along with Hybrid Machine Learning Methods: An Operational Approach for Poorly Gauged Basins

Study on runoff forecasting and error correction driven by atmosphere–ocean-land dataset

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