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Short-term Hourly Streamflow Prediction with Graph Convolutional GRU Networks
[article]
2021
arXiv
pre-print
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short-term streamflow prediction. ...
This paper presents a Graph Convolutional GRUs based model to predict the next 36 hours of streamflow for a sensor location using the upstream river network. ...
As shown in the preliminary results, the StreamGConvGRU provides better performance than the persistence baseline and a Convolutional Bidirectional GRU model in our study region for short-term hourly streamflow ...
arXiv:2107.07039v1
fatcat:x7zhkqjxjzetdatd2hj7mfgna4
Short-Term Daily Univariate Streamflow Forecasting Using Deep Learning Models
2022
Advances in Meteorology
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Hence, in this study, we compared Stacked Long Short-Term Memory (S-LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and Gated Recurrent Unit (GRU) with the classical Multilayer Perceptron (MLP) ...
network for one-step daily streamflow forecasting. ...
Unit (GRU), Echo State Network (ESN), Convolutional Neural Network (CNN), and Temporal Convolutional Network (TCN). ...
doi:10.1155/2022/1860460
fatcat:zemabzwewzdi7b6ccehnek3b3i
A Review on Deep Sequential Models for Forecasting Time Series Data
2022
Applied Computational Intelligence and Soft Computing
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Three deep sequential models, namely, artificial neural network (ANN), long short-term memory (LSTM), and temporal-conventional neural network (TCNN) along with their applications for forecasting time ...
We showed a comprehensive comparison between such models in terms of application fields, model structure and activation functions, optimizers, and implementation, with a goal of learning more about the ...
of time series data, as well as the LSTM layers' efficacy in recognizing short-and long-term relationships. e initial experimental study demonstrated that combining the LSTM with extra convolutional layers ...
doi:10.1155/2022/6596397
fatcat:n6ufhsi7nza25hpj77csn7hrhu
A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources
[article]
2020
arXiv
pre-print
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The volume, variety, and velocity of water-related data are increasing due to large-scale sensor networks and increased attention to topics such as disaster response, water resources management, and climate ...
The study provides a comprehensive review of state-of-the-art deep learning approaches used in the water industry for generation, prediction, enhancement, and classification tasks, and serves as a guide ...
Kabir et al. (2020) proposed a wavelet-ANN to make the hourly streamflow predictions. ...
arXiv:2007.12269v1
fatcat:7vc2r76fozhtpcveli6h4uldie
A Simple Baseline for Adversarial Domain Adaptation-based Unsupervised Flood Forecasting
[article]
2022
arXiv
pre-print
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The performance of the FloodDAN is on par with supervised models that uses 450-500 hours of supervision. ...
During inference, we utilize the target domain rainfall encoder trained in the second stage and the prediction head trained in the first stage to get flood forecasting predictions. ...
Since flood forecasting is a sequential task, we compare temporal convolutional network (TCN), Long short-term memory (LSTM), Gated Recurrent Unit (GRU) and other models to find the most suitable encoder ...
arXiv:2206.08105v1
fatcat:r3hetbe4kbdm7c3j2tfw4atyzi
A comprehensive review of deep learning applications in hydrology and water resources
2020
Water Science and Technology
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The volume, variety, and velocity of water-related data are increasing due to large-scale sensor networks and increased attention to topics such as disaster response, water resources management, and climate ...
The study provides a comprehensive review of state-of-the-art deep learning approaches used in the water industry for generation, prediction, enhancement, and classification tasks, and serves as a guide ...
neural networks', 'deep learning', 'lstm', 'long short term memory', 'cnn', 'convolutional', 'gan', 'generative adversarial', 'rnn', 'recurrent neural', 'gru', and 'gated recurrent'. ...
doi:10.2166/wst.2020.369
pmid:33341760
fatcat:ybb4agmq5vaejczzxzfsstz7wm
Daily Streamflow Forecasting Based on the Hybrid Particle Swarm Optimization and Long Short-Term Memory Model in the Orontes Basin
2022
Water
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Therefore, in this study, a hybrid approach integrating long short-term memory networks (LSTM) and particle swarm algorithm (PSO) was proposed. ...
The comparison of daily streamflow predictions results revealed that the PSO-LSTM model provided promising accuracy results and presented higher performance compared with the benchmark and linear regression ...
[52] utilized the three popular DL models, which were deep neural network (DNN), temporal convolution neural network (TCN), and long short-term memory neural network (LSTM). ...
doi:10.3390/w14030490
fatcat:sded6xtqefar3pl3esunrghfui
Predictive modelling of global solar radiation with artificial intelligence approaches using MODIS satellites and atmospheric reanalysis data for Australia
2019
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prediction of both long-term (i.e., monthly averaged daily) as well as the short-term (i.e., daily and half-hourly) GSR. ...
Since short-term variabilities in the GSR incorporate stochastic and intermittent behaviours (such as periodic fluctuations, jumps and trends) due to the dynamicity of atmospheric variables, GSR predictions ...
Finally, the Convolutional Neural Network (CNN) integrated with a Long Short-Term Memory Network (LSTM) model is used to construct a hybrid CLSTM model which is tested to predict the half-hourly GSR values ...
doi:10.26192/9he0-h328
fatcat:h6xzloo6gjgl7olovgt5j6uaka
Leveraging Artificial Neural Networks for Modeling Hydrogeological Time Series
2022
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Untersucht werden hierbei Nonlinear Autoregressive Models with Exogenous Inputs (NARX), Long Short-Term Memory Networks [...] ...
Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous ...
RQ3 Is it possible to perform reasonable short-term predictions of GWLs with ANNs without any future input data? ...
doi:10.5445/ir/1000149192
fatcat:h3gpwpsk4ngefdotygo7s6oacy