Development of an enhanced bidirectional recurrent neural network combined with time-varying filter-based empirical mode decomposition to forecast weekly reference evapotranspiration

Karbasi, Masoud; Jamei, Mehdi; Ali, Mumtaz; Malik, Anurag; Chu, Xuefeng; Farooque, Aitazaz Ahsan; Yaseen, Zaher Mundher

doi:10.1016/j.agwat.2023.108604

Cited by 11 publications

(1 citation statement)

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“…The simpler internal structure of the RNN model renders it more suitable for flood forecasting tasks. Karbasi et al [55] proposed a hybrid technique using time-varying filter-based empirical modal decomposition (TVF-EMD) and DL to predict weekly reference evapotranspiration and compared four machine learning methods, namely the bidirectional recurrent neural network (BiRNN), multi-layer perceptual neural network (MLP), RF, and extreme gradient-boosting (XGBoost) methods, in terms of their prediction performance. Their results demonstrated that the TVF-BiRNN model achieved the highest accuracy in simulating weekly reference evapotranspiration at the Redcliffe and Gold Coast stations (Redcliffe: R = 0.93, MAPE = 9.20%, RMSE = 3.88 mm/week; Gold Coast: R = 0.87, MAPE = 11.54%, RMSE = 4.12 mm/week).…”

Section: Rnns For Predictionmentioning

confidence: 99%

A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning

Zhao,

Wang,

Bai

et al. 2024

Water

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Artificial intelligence has undergone rapid development in the last thirty years and has been widely used in the fields of materials, new energy, medicine, and engineering. Similarly, a growing area of research is the use of deep learning (DL) methods in connection with hydrological time series to better comprehend and expose the changing rules in these time series. Consequently, we provide a review of the latest advancements in employing DL techniques for hydrological forecasting. First, we examine the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in hydrological forecasting, along with a comparison between them. Second, a comparison is made between the basic and enhanced long short-term memory (LSTM) methods for hydrological forecasting, analyzing their improvements, prediction accuracies, and computational costs. Third, the performance of GRUs, along with other models including generative adversarial networks (GANs), residual networks (ResNets), and graph neural networks (GNNs), is estimated for hydrological forecasting. Finally, this paper discusses the benefits and challenges associated with hydrological forecasting using DL techniques, including CNN, RNN, LSTM, GAN, ResNet, and GNN models. Additionally, it outlines the key issues that need to be addressed in the future.

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Section: Rnns For Predictionmentioning

confidence: 99%