Research on particle swarm optimization in LSTM neural networks for rainfall-runoff simulation

Xu, Yan; Hu, Caihong; Wu, Qiang; Jian, Shengqi; Li, Zhichao; Chen, Youjian; Zhang, Guodong; Zhang, Zhaoxi; Wang, Shuli

doi:10.1016/j.jhydrol.2022.127553

Cited by 162 publications

(50 citation statements)

References 30 publications

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“…Ji [29] studied the LSTM and modified the SWAT model by adding the glacier module to the permafrost region; the results indicated that the prediction accuracy of the neural network model was better than that of the other models. Yuanhao Xu [55] constructed a precipitation-driven PSO-LSTM runoff model to simulate an improved physical process-based SAC-SMA model.…”

Section: Prospect Of Hydrologic Application Of Pso-lstm and Bilstm Mo...mentioning

confidence: 99%

Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model

Yang

Maihemuti

Simayi

et al. 2022

Water

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The simulation and prediction of glacially derived runoff are significant for water resource management and sustainable development in water-stressed arid regions. However, the application of a hydrological model in such regions is typically limited by the intricate runoff production mechanism, which is associated with snow and ice melting, and sparse monitoring data over glacierized headwaters. To address these limitations, this study develops a set of mathematical models with a certain physical significance and an efficient particle swarm optimization algorithm by applying long- and short-term memory networks on the glacierized Muzati River basin. First, the trends in the runoff, precipitation, and air temperature are analyzed from 1990 to 2015, and differences in their correlations in this period are exposed. Then, Particle Swarm Optimization–Long Short-Term Memory (PSO-LSTM) and Bi-directional Long Short-Term Memory (BiLSTM) models are combined and applied to the precipitation and air temperature data to predict the glacially derived runoff. The prediction accuracy is validated by the observed runoff at the river outlet at the Pochengzi hydrological station. Finally, two other types of models, the RF (Random Forest) and LSTM (Long Short-Term Memory) models, are constructed to verify the prediction results. The results indicate that the glacially derived runoff is strongly correlated with air temperature and precipitation. However, in the study region over the past 26 years, the air temperature was not obviously increasing, and the precipitation and glacially derived runoff were significantly decreasing. The test results show that the PSO-LSTM and BiLSTM runoff prediction models perform better than the RF and LSTM models in the glacierized Muzati River basin. In the validation period, among all models, the PSO-LSTM model has the smallest mean absolute error and root-mean-square error and the largest coefficient of determination of 6.082, 8.034, and 0.973, respectively. It is followed by the BiLSTM model having a mean absolute error, root-mean-square error, and coefficient of determination of 6.751, 9.083, and 0.972, respectively. These results imply that both the particle swarm optimization algorithm and the bi-directional structure can effectively enhance the prediction accuracy of the baseline LSTM model. The results presented in this study can provide a deeper understanding and a more appropriate method of predicting the glacially derived runoff in glacier-fed river basins.

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Section: Prospect Of Hydrologic Application Of Pso-lstm and Bilstm Mo...mentioning

confidence: 99%

Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model

Yang

Maihemuti

Simayi

et al. 2022

Water

View full text Add to dashboard Cite

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“…The particle swarm optimization (PSO) is a well-known algorithm, which is widely applied in optimization problems, including the parameters calibration of machine learning algorithms in order to improve their performance in hydrological applications [40][41][42]. PSO is a population-based technique that was motivated by studying the social behavior of fish and birds in finding the shortest route to find the food [43].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Precipitation Forecasting in Northern Bangladesh Using a Hybrid Machine Learning Model

et al. 2022

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Precipitation forecasting is essential for the assessment of several hydrological processes. This study shows that based on a machine learning approach, reliable models for precipitation prediction can be developed. The tropical monsoon-climate northern region of Bangladesh, including the Rangpur and Sylhet division, was chosen as the case study. Two machine learning algorithms were used: M5P and support vector regression. Moreover, a novel hybrid model based on the two algorithms was developed. The performance of prediction models was assessed by means of evaluation metrics and graphical representations. A sensitivity analysis was also carried out to assess the prediction accuracy as the number of exogenous inputs reduces and lag times increases. Overall, the hybrid model M5P-SVR led to the best predictions among used models in this study, with R2 values up to 0.87 and 0.92 for the stations of Rangpur and Sylhet, respectively.

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“…Long and short term memory neural networks (LSTM) are widely used for flood forecasting by continuously storing useful information by memory neurons for time series prediction. However, the choice of hyperparameters for LSTM models has a large impact on the prediction performance of the models [14][15]. Monolithic models usually have the problems of poor generalization ability and low prediction accuracy, and hybrid models can effectively solve the limitations of monolithic models.…”

Section: Introductionmentioning

confidence: 99%

Flood Forecasting Method Based on Improved VMD-FOS-QR-RBL

Liu

Li-hu

et al. 2023

IEEE Access

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To improve the accuracy, reliability and validity of flood prediction models, this study proposes a regularized broad learning (RBL) model based on an improved variational mode decomposition (VMD). Firstly, grey correlation analysis was used to improve the endpoint effect of the VMD and the particle swarm optimisation (PSO) algorithm was used to optimise the VMD parameters. Then, using orthogonal triangular decomposition (QR), redefining the hidden layer output of BL model and adding forgettable online sequence learning mechanism (FOS) to construct online sequence BL (FOS-QR-RBL), which can significantly improve the computational efficiency of BL model. Finally, a flood forecasting method based on improved VMD-FOS-QR-RBL was constructed by combining the FOS-QR-RBL with the improved VMD and applying it to regional flood forecasting. The experimental results show that the computational efficiency of FOS-QR-RBL is improved by 35% and 23.68% compared with RBL and QR-RBL, respectively. The mean absolute error (MAE) of the coupling model of VMD and FOS-QR-RBL is reduced by 80.30% and 84.10% respectively, and the nash efficiency coefficient (Ens) is increased by 15.51% and 28.16% respectively, compared with that of the coupling model of FOS-QR-RBL with ensemble empirical mode decomposition (EEMD) and adaptive noise complete ensemble empirical mode decomposition (CEEMDAN). The results of the optimal operation based on VMD-FOS-OR-RBL show that the model can effectively reduce the economic losses caused by regional flooding.

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Research on particle swarm optimization in LSTM neural networks for rainfall-runoff simulation

Cited by 162 publications

References 30 publications

Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model

Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model

Precipitation Forecasting in Northern Bangladesh Using a Hybrid Machine Learning Model

Flood Forecasting Method Based on Improved VMD-FOS-QR-RBL

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