Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes
Authors
Keywords
No free lunch theorem, Random forests, River discharge, Stochastic hydrology, Support vector machines, Time series
Journal
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
Volume -, Issue -, Pages -
Publisher
Springer Nature
Online
2019-01-01
DOI
10.1007/s00477-018-1638-6
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Predictability of monthly temperature and precipitation using automatic time series forecasting methods
- (2018) Georgia Papacharalampous et al. Acta Geophysica
- Statistical and Machine Learning forecasting methods: Concerns and ways forward
- (2018) Spyros Makridakis et al. PLoS One
- Predictability of monthly temperature and precipitation using automatic time series forecasting methods
- (2018) Georgia Papacharalampous et al. Acta Geophysica
- Improved validation framework and R-package for artificial neural network models
- (2017) Greer B. Humphrey et al. ENVIRONMENTAL MODELLING & SOFTWARE
- On the prediction of persistent processes using the output of deterministic models
- (2017) Hristos Tyralis et al. HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
- Mastering the game of Go with deep neural networks and tree search
- (2016) David Silver et al. NATURE
- Non-tuned machine learning approach for hydrological time series forecasting
- (2016) Zaher Mundher Yaseen et al. NEURAL COMPUTING & APPLICATIONS
- On the criteria of model performance evaluation for real-time flood forecasting
- (2016) Ke-Sheng Cheng et al. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
- A random forest guided tour
- (2016) Gérard Biau et al. TEST
- A random forest guided tour
- (2016) Gérard Biau et al. TEST
- Consistency of random forests
- (2015) Erwan Scornet et al. ANNALS OF STATISTICS
- Negligent killing of scientific concepts: the stationarity case
- (2015) Demetris Koutsoyiannis et al. HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
- How do I know if my forecasts are better? Using benchmarks in hydrological ensemble prediction
- (2015) F. Pappenberger et al. JOURNAL OF HYDROLOGY
- Automatic Time Series Forecasting: TheforecastPackage forR
- (2015) Rob J. Hyndman et al. Journal of Statistical Software
- The Split-Apply-Combine Strategy for Data Analysis
- (2015) Hadley Wickham Journal of Statistical Software
- Support vector machine applications in the field of hydrology: A review
- (2014) Sujay Raghavendra. N et al. APPLIED SOFT COMPUTING
- A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin
- (2014) Shivshanker Singh Patel et al. WATER RESOURCES MANAGEMENT
- A Bayesian statistical model for deriving the predictive distribution of hydroclimatic variables
- (2013) Hristos Tyralis et al. CLIMATE DYNAMICS
- Parameter tuning or default values? An empirical investigation in search-based software engineering
- (2013) Andrea Arcuri et al. EMPIRICAL SOFTWARE ENGINEERING
- A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region
- (2013) Zhibin He et al. JOURNAL OF HYDROLOGY
- Streamflow forecasting using least-squares support vector machines
- (2012) Ani Shabri et al. HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
- Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir
- (2012) Mohammad Valipour et al. JOURNAL OF HYDROLOGY
- Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing
- (2012) Alysha M. De Livera et al. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
- Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting
- (2012) Robert J. Abrahart et al. PROGRESS IN PHYSICAL GEOGRAPHY
- Forecasting daily lake levels using artificial intelligence approaches
- (2011) Ozgur Kisi et al. COMPUTERS & GEOSCIENCES
- Precipitation forecasting by using wavelet-support vector machine conjunction model
- (2011) Ozgur Kisi et al. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
- Monthly streamflow forecasting based on improved support vector machine model
- (2011) Jun Guo et al. EXPERT SYSTEMS WITH APPLICATIONS
- EMD-KNN Model for Annual Average Rainfall Forecasting
- (2011) Jian Hu et al. JOURNAL OF HYDROLOGIC ENGINEERING
- A wavelet-support vector machine conjunction model for monthly streamflow forecasting
- (2011) Ozgur Kisi et al. JOURNAL OF HYDROLOGY
- Hurst-Kolmogorov Dynamics and Uncertainty1
- (2011) Demetris Koutsoyiannis JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION
- To Explain or to Predict?
- (2011) Galit Shmueli STATISTICAL SCIENCE
- An Empirical Comparison of Machine Learning Models for Time Series Forecasting
- (2010) Nesreen K. Ahmed et al. Econometric Reviews
- Communicating uncertainty in hydro-meteorological forecasts: mission impossible?
- (2010) Maria-Helena Ramos et al. METEOROLOGICAL APPLICATIONS
- Simultaneous estimation of the parameters of the Hurst–Kolmogorov stochastic process
- (2010) Hristos Tyralis et al. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
- A neural network experiment on the simulation of daily nitrate-nitrogen and suspended sediment fluxes from a small agricultural catchment
- (2009) François Anctil et al. ECOLOGICAL MODELLING
- Time Series Prediction Using Support Vector Machines: A Survey
- (2009) Nicholas Sapankevych et al. IEEE Computational Intelligence Magazine
- A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series
- (2009) Wen-Chuan Wang et al. JOURNAL OF HYDROLOGY
- Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling
- (2009) Hoshin V. Gupta et al. JOURNAL OF HYDROLOGY
- Rainfall forecasting by technological machine learning models
- (2008) Wei-Chiang Hong APPLIED MATHEMATICS AND COMPUTATION
- Do Nash values have value? Discussion and alternate proposals
- (2008) Robert E. Criss et al. HYDROLOGICAL PROCESSES
- 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
- A new indirect multi-step-ahead prediction model for a long-term hydrologic prediction
- (2008) Chun-Tian Cheng et al. JOURNAL OF HYDROLOGY
- On model selection criteria in multimodel analysis
- (2008) Ming Ye et al. WATER RESOURCES RESEARCH
- Data-driven modelling: some past experiences and new approaches
- (2007) Dimitri P. Solomatine et al. JOURNAL OF HYDROINFORMATICS
Become a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get StartedAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started