Green Roof Hydrological Modelling With GRU and LSTM Networks

Xie, Haowen; Randall, Mark; Chau, Kwok-wing

doi:10.1007/s11269-022-03076-6

Cited by 20 publications

(2 citation statements)

References 65 publications

(44 reference statements)

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“…Their findings revealed that the peak flow accuracy fell within a 90% prediction probability interval, with predictions approaching 100%, underscoring the model's robust adaptability to flood uncertainty. In a separate study, Xie et al [96] adopted two networks, a GRU and an LSTM model, to carry out a hydrological simulation of green roofs. Their results confirmed that as the length of the time window (the memory length, i.e., the time step of input data) increased, both models achieved a higher overall prediction accuracy, suggesting the utility of GRUs and LSTM in modeling hydrological processes on green roofs.…”

Section: Grus 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: Grus 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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show abstract

“…The GRU replaces the three gates in LSTM with two, i.e., the update gate z t and reset gate r t . The update gate and reset gate control information that flows into memory and information that flows out of memory, respectively [61]. These gates are basically vectors that determine the information passed on to the output and can be trained to keep past information or discard unnecessary information that does not contribute to the prediction.…”

Section: Gated Recurrent Unitmentioning

confidence: 99%

A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

Mienye

Sun

2023

IEEE Access

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Credit cards play an essential role in today's digital economy, and their usage has recently grown tremendously, accompanied by a corresponding increase in credit card fraud. Machine learning (ML) algorithms have been utilized for credit card fraud detection. However, the dynamic shopping patterns of credit card holders and the class imbalance problem have made it difficult for ML classifiers to achieve optimal performance. In order to solve this problem, this paper proposes a robust deep-learning approach that consists of long short-term memory (LSTM) and gated recurrent unit (GRU) neural networks as base learners in a stacking ensemble framework, with a multilayer perceptron (MLP) as the meta-learner. Meanwhile, the hybrid synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN) method is employed to balance the class distribution in the dataset. The experimental results showed that combining the proposed deep learning ensemble with the SMOTE-ENN method achieved a sensitivity and specificity of 1.000 and 0.997, respectively, which is superior to other widely used ML classifiers and methods in the literature.

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Introduction to Neural Networks

Momtazi

Abbasiantaeb²

2022

Question Answering Over Text and Knowledge Base

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Green Roof Hydrological Modelling With GRU and LSTM Networks

Cited by 20 publications

References 65 publications

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

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

A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

Introduction to Neural Networks

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