A hybrid deep learning model for rainfall in the wetlands of southern Iraq

Alqahtani, Fehaid; Abotaleb, Mostafa; Subhi, Alhumaima Ali; El-kenawy, El-Sayed M.; Abdelhamid, Abdelaziz A.; Alakkari, Khder Mohammed; Badr, Amr; Al-Mahdawi, H.K.; Ibrahim, Abdelhameed; Kadi, Ammar

doi:10.1007/s40808-023-01754-x

Cited by 5 publications

(1 citation statement)

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“…The forgetting gate and the input gate are combined into an "update gate", which can use the output result as a memory state and continuously pass it backwards in a loop without giving out additional memory states. This improves the training speed of the network while ensuring accuracy [10]. Compared with the three gates of the LSTM recurrent neural network, the GRU recurrent neural network only has an update gate and a reset gate.…”

Section: Gru Recurrent Neural Networkmentioning

confidence: 99%

Research on the purification mechanism of heavy metal pollution by biochar composites driven by degree learning

Dai,

Jia,

Zhan

et al. 2024

E3S Web Conf.

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This paper proposes an innovative approach by integrating deep learning technology, specifically employing the GRU recurrent neural network model based on the Seagull optimization algorithm, to enhance the accuracy of predicting biochar performance. The Seagull optimization algorithm, inspired by seagull predatory behavior, is adept at efficiently identifying optimal model parameters, thereby improving the model’s generalization ability and robustness. The GRU recurrent neural network, designed for sequence data processing, proves to be instrumental in capturing dynamic and nonlinear interactions between biochar and heavy metals. This, in turn, contributes to heightened prediction accuracy and model interpretability. The article unfolds in a structured manner, beginning with an introduction to the biochar preparation method and its characteristics. It then delves into an analysis of the sources and hazards of heavy metal pollution. Following this, the paper explains the principles and advantages of deep learning technology, providing a comprehensive foundation for the subsequent discussion. The construction and verification process of the proposed model is then detailed, concluding with the presentation of experimental results and in-depth analysis. In essence, this research introduces a pioneering idea and methodology for optimizing biochar design and effectively controlling heavy metal pollution, presenting a fresh perspective on addressing these environmental challenges.

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Section: Gru Recurrent Neural Networkmentioning

confidence: 99%