Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting
出版年份 2019 全文链接
标题
Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting
作者
关键词
Artificial intelligence, Long short-term memory recurrent neural network, Low flow, Hydrological time series forecasting, naïve method
出版物
Acta Geophysica
Volume -, Issue -, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2019-07-20
DOI
10.1007/s11600-019-00330-1
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes
- (2019) Georgia Papacharalampous et al. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
- Predictability of monthly temperature and precipitation using automatic time series forecasting methods
- (2018) Georgia Papacharalampous et al. Acta Geophysica
- Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas
- (2018) Jianfeng Zhang et al. JOURNAL OF HYDROLOGY
- Forecasting groundwater levels using nonlinear autoregressive networks with exogenous input (NARX)
- (2018) Andreas Wunsch et al. JOURNAL OF HYDROLOGY
- Use long short-term memory to enhance Internet of Things for combined sewer overflow monitoring
- (2018) Duo Zhang et al. JOURNAL OF HYDROLOGY
- Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons
- (2018) Zaher Mundher Yaseen et al. WATER RESOURCES MANAGEMENT
- Predictability of monthly temperature and precipitation using automatic time series forecasting methods
- (2018) Georgia Papacharalampous et al. Acta Geophysica
- An evaluation of statistical, NMME and hybrid models for drought prediction in China
- (2018) Lei Xu et al. JOURNAL OF HYDROLOGY
- Assessment of Environmental Flows from Complexity to Parsimony—Lessons from Lesotho
- (2018) Aristoteles Tegos et al. Water
- Application of Support Vector Regression for Modeling Low Flow Time Series
- (2018) Bibhuti Bhusan Sahoo et al. KSCE Journal of Civil Engineering
- Univariate Time Series Forecasting of Temperature and Precipitation with a Focus on Machine Learning Algorithms: a Multiple-Case Study from Greece
- (2018) Georgia Papacharalampous et al. WATER RESOURCES MANAGEMENT
- Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network
- (2017) Kuai Fang et al. GEOPHYSICAL RESEARCH LETTERS
- Monthly Rainfall Forecasting Using Echo State Networks Coupled with Data Preprocessing Methods
- (2017) Qi Ouyang et al. WATER RESOURCES MANAGEMENT
- Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S.
- (2017) S. Sahoo et al. WATER RESOURCES RESEARCH
- Use of a nonstationary copula to predict future bivariate low flow frequency in the Connecticut river basin
- (2016) Kuk-Hyun Ahn et al. HYDROLOGICAL PROCESSES
- Predictability in dice motion: how does it differ from hydro-meteorological processes?
- (2016) Panayiotis Dimitriadis et al. HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
- Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq
- (2016) Zaher Mundher Yaseen et al. JOURNAL OF HYDROLOGY
- Artificial intelligence based models for stream-flow forecasting: 2000–2015
- (2015) Zaher Mundher Yaseen et al. JOURNAL OF HYDROLOGY
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Climacogram versus autocovariance and power spectrum in stochastic modelling for Markovian and Hurst–Kolmogorov processes
- (2015) Panayiotis Dimitriadis et al. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
- A Bayesian statistical model for deriving the predictive distribution of hydroclimatic variables
- (2013) Hristos Tyralis et al. CLIMATE DYNAMICS
- Low flows in France and their relationship to large-scale climate indices
- (2013) I. Giuntoli et al. JOURNAL OF HYDROLOGY
- A review on the applications of wavelet transform in hydrology time series analysis
- (2012) Yan-Fang Sang ATMOSPHERIC RESEARCH
- Identification of appropriate lags and temporal resolutions for low flow indicators in the River Rhine to forecast low flows with different lead times
- (2012) Mehmet C. Demirel et al. HYDROLOGICAL PROCESSES
- A wavelet-support vector machine conjunction model for monthly streamflow forecasting
- (2011) Ozgur Kisi et al. JOURNAL OF HYDROLOGY
- The relation between periods’ identification and noises in hydrologic series data
- (2009) Yan-Fang Sang et al. JOURNAL OF HYDROLOGY
- A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series
- (2009) Wen-Chuan Wang et al. JOURNAL OF HYDROLOGY
- Medium-range flow prediction for the Nile: a comparison of stochastic and deterministic methods / Prévision du débit du Nil à moyen terme: une comparaison de méthodes stochastiques et déterministes
- (2008) DEMETRIS KOUTSOYIANNIS et al. HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
- Critical appraisal of methods for the assessment of environmental flows and their application in two river systems of India
- (2008) Ramakar Jha et al. KSCE Journal of Civil Engineering
- Model complexity control for hydrologic prediction
- (2008) G. Schoups et al. WATER RESOURCES RESEARCH
- Using adaptive neuro-fuzzy inference system for hydrological time series prediction
- (2007) Mohammad Zounemat-Kermani et al. APPLIED SOFT COMPUTING
- Hydrological time-series modelling using an adaptive neuro-fuzzy inference system
- (2007) Mahmut Firat et al. HYDROLOGICAL PROCESSES
- A new boosting algorithm for improved time-series forecasting with recurrent neural networks
- (2006) Mohammad Assaad et al. Information Fusion
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