Metaheuristic evolutionary deep learning model based on temporal convolutional network, improved aquila optimizer and random forest for rainfall-runoff simulation and multi-step runoff prediction

Qiao, Xueying; Tian, Peng; Sun, Na; Zhang, Chu; Liu, Qianlong; Zhang, Yue; Wang, Yuhan; Nazir, Muhammad Shahzad

doi:10.1016/j.eswa.2023.120616

Cited by 24 publications

(2 citation statements)

References 32 publications

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“…To mitigate the impact of redundant features on data-driven model prediction accuracy, this study employs Random Forest (RF) for initial feature selection of the model input factors. Random Forest is an ensemble learning algorithm that uses decision trees as base learners and is widely applied in regression prediction and high-dimensional feature selection [42,49]. In this study, the calculation of feature importance in Random Forest utilizes the out-of-bag (OOB) data's classification accuracy as the evaluation criterion.…”

Section: Model Input Selectionmentioning

confidence: 99%

Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction

Xie,

Chen,

et al. 2023

Water

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Hydrological forecasting plays a crucial role in mitigating flood risks and managing water resources. Data-driven hydrological models demonstrate exceptional fitting capabilities and adaptability. Recognizing the limitations of single-model forecasting, this study introduces an innovative approach known as the Improved K-Nearest Neighbor Multi-Model Ensemble (IKNN-MME) method to enhance the runoff prediction. IKNN-MME dynamically adjusts model weights based on the similarity of historical data, acknowledging the influence of different training data features on localized predictions. By combining an enhanced K-Nearest Neighbor (KNN) algorithm with adaptive weighting, it offers a more powerful and flexible ensemble. This study evaluates the performance of the IKNN-MME method across four basins in the United States and compares it to other multi-model ensemble methods and benchmark models. The results underscore its outstanding performance and adaptability, offering a promising avenue for improving runoff forecasting.

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Section: Model Input Selectionmentioning

confidence: 99%

Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction

Xie,

Chen,

et al. 2023

Water

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“…To address the gaps in previous studies, this paper also provides several innovative points and contributions. On one hand, prior researchers have dedicated their efforts to studying model precision comparison and combination models [30,31]. However, there is limited research exploring the optimization of the back propagation algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Factor Combination Model for Medium to Long-Term Runoff Prediction Based on Improved BP Neural Network

Yan,

Gao,

Wen

et al. 2023

Water

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Taking a certain coastal area of Jiangsu province as the research background, this study scientifically predicts the runoff on the medium and long-term time scale according to the changes of various climate factors such as atmospheric circulation, sea surface temperature, and solar activity in the first half of the year. A lag correlation is established between various related climate factors and the monthly runoff process in the research area for the previous 1–6 months. Selecting advantageous factors and constructing a significant factor set. Using the improved BP (Back-Propagation) artificial neural network model and combining it with the sensitivity analysis method, a specific number of 8-factor combinations are selected from the set of significant factors for medium and long-term runoff prediction. After that, the prediction results are compared with the forecasting effects of two multi-factor combination runoff simulation schemes formed by stepwise regression and Spearman rank correlation methods. The study concluded that the multi-factor combination simulation effect formed through sensitivity analysis was the best. The 20% standard forecast qualification rate of the three schemes is not significantly different. The Mean Absolute Relative Error of the multi-factor combination training and validation periods simulated through sensitivity analysis is the smallest among the three schemes, which are 36.61% and 38.01%, respectively. The Nash Efficiency Coefficient in the validation period is 0.45, which is far better than other schemes and has better generalization ability. The Standard Deviation of Relative Error in the training and validation periods is much smaller than other schemes, and the dispersion of relative errors is the smallest.

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Network traffic grant classification based on 1DCNN-TCN-GRU hybrid model

Mo,

Qi,

Liu

2024

Appl Intell

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Metaheuristic evolutionary deep learning model based on temporal convolutional network, improved aquila optimizer and random forest for rainfall-runoff simulation and multi-step runoff prediction

Cited by 24 publications

References 32 publications

Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction

Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction

A Multi-Factor Combination Model for Medium to Long-Term Runoff Prediction Based on Improved BP Neural Network

Network traffic grant classification based on 1DCNN-TCN-GRU hybrid model

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