Runoff Predictions in a Semiarid Watershed by Convolutional Neural Networks Improved with Metaheuristic Algorithms and Forced with Reanalysis and Climate Data

Aoulmi, Yamina; Marouf, Nadir; Rasouli, Kabir; Panahi, Mahdi

doi:10.1061/jhyeff.heeng-5920

Cited by 2 publications

(1 citation statement)

References 64 publications

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“…With the rapid advancement of artificial intelligence technology, numerous deep learning algorithms have emerged, and comprehensive forecasting models based on intelligent methods and numerical weather prediction have been proposed. These models involve various optimization algorithms such as Chaos Optimization Algorithm 1 , bald eagle search optimization algorithm 2 , Particle Swarm Optimization (PSO) 3 , and artificial neural network models 4 , 5 , which have deepened their intersectionality with hydrology.…”

Section: Introductionmentioning

confidence: 99%

Monthly runoff prediction based on a coupled VMD-SSA-BiLSTM model

Zhang

Wang

et al. 2023

Sci Rep

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The accurate prediction of monthly runoff in the lower reaches of the Yellow River is crucial for the rational utilization of regional water resources, optimal allocation, and flood prevention. This study proposes a VMD-SSA-BiLSTM coupled model for monthly runoff volume prediction, which combines the advantages of Variational Modal Decomposition (VMD) for signal decomposition and preprocessing, Sparrow Search Algorithm (SSA) for BiLSTM model parameter optimization, and Bi-directional Long and Short-Term Memory Neural Network (BiLSTM) for exploiting the bi-directional linkage and advanced characteristics of the runoff process. The proposed model was applied to predict monthly runoff at GaoCun hydrological station in the lower Yellow River. The results demonstrate that the VMD-SSA-BiLSTM model outperforms both the BiLSTM model and the VMD-BiLSTM model in terms of prediction accuracy during both the training and validation periods. The Root-mean-square deviation of VMD-SSA-BiLSTM model is 30.6601, which is 242.5124 and 39.9835 lower compared to the BiLSTM model and the VMD-BiLSTM model respectively; the mean absolute percentage error is 5.6832%, which is 35.5937% and 6.3856% lower compared to the other two models, respectively; the mean absolute error was 19.8992, which decreased by 136.7288 and 25.7274 respectively; the square of the correlation coefficient (R2) is 0.93775, which increases by 0.53059 and 0.14739 respectively; the Nash–Sutcliffe efficiency coefficient was 0.9886, which increased by 0.4994 and 0.1122 respectively. In conclusion, the proposed VMD-SSA-BiLSTM model, utilizing the sparrow search algorithm and bidirectional long and short-term memory neural network, enhances the smoothness of the monthly runoff series and improves the accuracy of point predictions. This model holds promise for the effective prediction of monthly runoff in the lower Yellow River.

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Section: Introductionmentioning

confidence: 99%

Monthly runoff prediction based on a coupled VMD-SSA-BiLSTM model

Zhang

Wang

et al. 2023

Sci Rep

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Improving Hybrid Models for Precipitation Forecasting by Combining Nonlinear Machine Learning Methods

Parviz

Rasouli

Haghighi

2023

Water Resour Manage

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Precipitation forecast is key for water resources management in semi-arid climates. The traditional hybrid models simulate linear and nonlinear components of precipitation series separately. But they do not still provide accurate forecasts. This research aims to improve hybrid models by using an ensemble of linear and nonlinear models. Preprocessing configurations and each of the Gene Expression Programming (GEP), Support Vector Regression (SVR), and Group Method of Data Handling (GMDH) models were used as in the traditional hybrid models. They were compared against the proposed hybrid models with a combination of all these three models. The performance of the hybrid models was improved by different methods. Two weather stations of Tabriz and Rasht in Iran with respectively annual and monthly time steps were selected to test the improved models. The results showed that Theil’s coefficient, which measures the inequality degree to which forecasts differ from observations, improved by 9% and 15% for SVR and GMDH relative to GEP for the Tabriz station. The applied error criteria indicated that the proposed hybrid models have a better representation of observations than the traditional hybrid models. Mean square error decreased by 67% and Nash Sutcliffe increased by 5% in the Rasht station when we combined the three machine learning models using genetic algorithm instead of SVR. Generally, the representation of the nonlinear models within the improved hybrid models showed better performance than the traditional hybrid models. The improved models have implications for modeling highly nonlinear systems using the full advantages of machine learning methods.

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Runoff Predictions in a Semiarid Watershed by Convolutional Neural Networks Improved with Metaheuristic Algorithms and Forced with Reanalysis and Climate Data

Cited by 2 publications

References 64 publications

Monthly runoff prediction based on a coupled VMD-SSA-BiLSTM model

Monthly runoff prediction based on a coupled VMD-SSA-BiLSTM model

Improving Hybrid Models for Precipitation Forecasting by Combining Nonlinear Machine Learning Methods

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