Journal ArticleDOI
Research on Particle Swarm Optimization in LSTM Neural Networks for Rainfall-Runoff Simulation
Yuan Hao Xu,Caihong Hu,Qiang Wu,Sheng Jiang,Zhichao Li,Youjian Chen,Guo-jiang Zhang,Zhaoxi Zhang,Shuli Wang +8 more
TLDR
In this paper , a deep learning neural network model based on LSTM networks and particle swarm optimization (PSO) is proposed to improve the forecast accuracy and lead time of flooding.Abstract:
Flood forecasting is an essential non-engineering measure for flood prevention and disaster reduction. Many models have been developed to study the complex and highly random rainfall-runoff process. In recent years, artificial intelligence methods, such as the artificial neural network (ANN), have attempted to construct rainfall-runoff models. The more advanced deep learning methods of long short-term memory (LSTM) network have been proved to better predict hydrological time series. However, the selection of LSTM hyperparameters in the past mostly relied on the experience of the staff, which often led to failure to achieve the best performance. The aim of this study is to develop a method to improve flood forecast accuracy and lead time. A deep learning neural network model based on LSTM networks and particle swarm optimization (PSO) is proposed in this paper. The PSO algorithm was used to optimize the LSTM hyperparameter to improve the ability to learn data sequence features. The model focuses on the Jingle Watershed in the Fenhe River and the Lushi Watershed in the Luohe River and was used to predict flood processes using rainfall and runoff observation data from stations in the watersheds. We evaluated the performance of the model with the Nash Sutcliffe efficiency coefficient, root mean square error, and bias. The results show that the PSO-LSTM model outperforms the M-EIES, ANN, PSO-ANN, and LSTM at all stations in the watersheds. The PSO-LSTM model improves the flood forecasting accuracy at different lead times, especially for those exceeding 6 h, and has higher prediction accuracy and stability. The PSO-LSTM model could be used to improve accuracy in short-term flood forecast applications. read more
Citations
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Journal ArticleDOI
Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review
TL;DR: The meta-heuristic (MH) algorithms can accurately formulate the optimal estimation of DL components (such as hyper-parameter, weights, number of neurons, learning rate, etc.). as mentioned in this paper provides a comprehensive review of the optimization of ANNs and DLs using MH algorithms.
Journal ArticleDOI
A novel model for water quality prediction caused by non-point sources pollution based on deep learning and feature extraction methods
TL;DR: Wang et al. as mentioned in this paper proposed a novel deep learning model named SOD-VGG-LSTM with the simulation-observation difference (SOD) modular based on physical process, the visual geometry (VGG) modular reflecting spatial characteristics, and the long short-term memory (lSTM) modularbased on deep learning method was developed to improve the accuracy of the water quality prediction with NPS pollution.
Journal ArticleDOI
Precipitation Forecasting in Northern Bangladesh Using a Hybrid Machine Learning Model
TL;DR: In this article , two machine learning algorithms were used: M5P and support vector regression, and a novel hybrid model based on the two algorithms was developed, which led to the best predictions with R2 values up to 0.87 and 0.92 for the stations of Rangpur and Sylhet, respectively.
References
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Proceedings ArticleDOI
Particle swarm optimization
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Journal ArticleDOI
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TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Proceedings ArticleDOI
Long-term recurrent convolutional networks for visual recognition and description
Jeff Donahue,Lisa Anne Hendricks,Sergio Guadarrama,Marcus Rohrbach,Subhashini Venugopalan,Trevor Darrell,Kate Saenko +6 more
TL;DR: A novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and shows such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined and/or optimized.