Effect of Gradient Descent Optimizers and Dropout Technique on Deep Learning LSTM Performance in Rainfall-runoff Modeling

[1]  Guoqing Wang,et al.  Enhanced LSTM model for daily runoff prediction in the upper huai river basin, china , 2022, Engineering.

[2]  Dongguo Shao,et al.  Application of Long Short-Term Memory (LSTM) on the Prediction of Rainfall-Runoff in Karst Area , 2022, Frontiers in Physics.

[3]  Dagang Wang,et al.  A hybrid deep learning algorithm and its application to streamflow prediction , 2021 .

[4]  Xue Li,et al.  Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks , 2021, Water.

[5]  Y. Ouma,et al.  Rainfall and runoff time-series trend analysis using LSTM recurrent neural network and wavelet neural network with satellite-based meteorological data: case study of Nzoia hydrologic basin , 2021, Complex & Intelligent Systems.

[6]  Nguyen Mai Dang,et al.  ANN optimized by PSO and Firefly algorithms for predicting scour depths around bridge piers , 2021, Engineering with Computers.

[7]  Jimmy Lin,et al.  Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network , 2020, Hydrology and Earth System Sciences.

[8]  Akhtar Jamil,et al.  A deep learning approach for hydrological time-series prediction: A case study of Gilgit river basin , 2020, Earth Science Informatics.

[9]  Wenchao Jiang,et al.  Streamflow Prediction Using Deep Learning Neural Network: Case Study of Yangtze River , 2020, IEEE Access.

[10]  Duong Tran Anh,et al.  Deep learning convolutional neural network in rainfall–runoff modelling , 2020, Journal of Hydroinformatics.

[11]  Nadhir Al-Ansari,et al.  Deep Learning Data-Intelligence Model Based on Adjusted Forecasting Window Scale: Application in Daily Streamflow Simulation , 2020, IEEE Access.

[12]  Jun Yan,et al.  A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning , 2020, Water Resources Research.

[13]  Natalia Gimelshein,et al.  PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.

[14]  Thanh Duc Dang,et al.  Downscaling rainfall using deep learning long short‐term memory and feedforward neural network , 2019, International Journal of Climatology.

[15]  Wei Gao,et al.  A predictive model based on an optimized ANN combined with ICA for predicting the stability of slopes , 2019, Engineering with Computers.

[16]  Hui Li,et al.  Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation , 2018, Water.

[17]  Sashank J. Reddi,et al.  On the Convergence of Adam and Beyond , 2018, ICLR.

[18]  Nathan Srebro,et al.  The Marginal Value of Adaptive Gradient Methods in Machine Learning , 2017, NIPS.

[19]  Kwok-wing Chau,et al.  Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling , 2017 .

[20]  Sebastian Ruder,et al.  An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.

[21]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[22]  Timothy Dozat,et al.  Incorporating Nesterov Momentum into Adam , 2016 .

[23]  Zoubin Ghahramani,et al.  A Theoretically Grounded Application of Dropout in Recurrent Neural Networks , 2015, NIPS.

[24]  Zaher Mundher Yaseen,et al.  Artificial intelligence based models for stream-flow forecasting: 2000-2015 , 2015 .

[25]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[26]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[27]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[28]  Christopher Kermorvant,et al.  Dropout Improves Recurrent Neural Networks for Handwriting Recognition , 2013, 2014 14th International Conference on Frontiers in Handwriting Recognition.

[29]  Geoffrey Zweig,et al.  Recent advances in deep learning for speech research at Microsoft , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[30]  Matthew D. Zeiler.  ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[31]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[32]  Marc'Aurelio Ranzato,et al.  Large Scale Distributed Deep Networks , 2012, NIPS.

[33]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.

[34]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

[35]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[36]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[37]  Ah Chung Tsoi,et al.  Lessons in Neural Network Training: Overfitting May be Harder than Expected , 1997, AAAI/IAAI.

[38]  H. Robbins.  A Stochastic Approximation Method , 1951 .

[39]  Karsten Schulz,et al.  Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks , 2018, Hydrology and Earth System Sciences.

[40]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..