A global-scale investigation of stochastic similarities in marginal distribution and dependence structure of key hydrological-cycle processes
To seek stochastic analogies in key processes related to the hydrological cycle, an extended
collection of several billions of data values from hundred thousands of worldwide stations is …
collection of several billions of data values from hundred thousands of worldwide stations is …
[HTML][HTML] Untenable nonstationarity: An assessment of the fitness for purpose of trend tests in hydrology
F Serinaldi, CG Kilsby, F Lombardo - Advances in Water Resources, 2018 - Elsevier
The detection and attribution of long-term patterns in hydrological time series have been
important research topics for decades. A significant portion of the literature regards such …
important research topics for decades. A significant portion of the literature regards such …
Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes
G Papacharalampous, H Tyralis… - … research and risk …, 2019 - Springer
Research within the field of hydrology often focuses on the statistical problem of comparing
stochastic to machine learning (ML) forecasting methods. The performed comparisons are …
stochastic to machine learning (ML) forecasting methods. The performed comparisons are …
Predictability of monthly temperature and precipitation using automatic time series forecasting methods
We investigate the predictability of monthly temperature and precipitation by applying
automatic univariate time series forecasting methods to a sample of 985 40-year-long …
automatic univariate time series forecasting methods to a sample of 985 40-year-long …
An improved long short-term memory network for streamflow forecasting in the upper Yangtze River
S Zhu, X Luo, X Yuan, Z Xu - Stochastic Environmental Research and Risk …, 2020 - Springer
Characterized by essential complexity, dynamism, and dynamics, streamflow forecasting
presents a great challenge to hydrologists. Long short-term memory (LSTM) streamflow …
presents a great challenge to hydrologists. Long short-term memory (LSTM) streamflow …
Hurst‐Kolmogorov Dynamics and Uncertainty1
D Koutsoyiannis - JAWRA Journal of the American Water …, 2011 - Wiley Online Library
Koutsoyiannis, Demetris, 2011. Hurst‐Kolmogorov Dynamics and Uncertainty. Journal of the
American Water Resources Association (JAWRA) 47 (3): 481‐495. DOI: 10.1111/j. 1752 …
American Water Resources Association (JAWRA) 47 (3): 481‐495. DOI: 10.1111/j. 1752 …
The importance of prewhitening in change point analysis under persistence
F Serinaldi, CG Kilsby - Stochastic Environmental Research and Risk …, 2016 - Springer
The presence of serial correlation in hydro-meteorological time series often makes the
detection of deterministic gradual or abrupt changes with tests such as Mann–Kendall (MK) …
detection of deterministic gradual or abrupt changes with tests such as Mann–Kendall (MK) …
Climacogram versus autocovariance and power spectrum in stochastic modelling for Markovian and Hurst–Kolmogorov processes
P Dimitriadis, D Koutsoyiannis - Stochastic environmental research and …, 2015 - Springer
Three common stochastic tools, the climacogram ie variance of the time averaged process
over averaging time scale, the autocovariance function and the power spectrum are …
over averaging time scale, the autocovariance function and the power spectrum are …
Stochastic synthesis approximating any process dependence and distribution
P Dimitriadis, D Koutsoyiannis - Stochastic environmental research and …, 2018 - Springer
An extension of the symmetric-moving-average (SMA) scheme is presented for stochastic
synthesis of a stationary process for approximating any dependence structure and marginal …
synthesis of a stationary process for approximating any dependence structure and marginal …
Univariate time series forecasting of temperature and precipitation with a focus on machine learning algorithms: A multiple-case study from Greece
G Papacharalampous, H Tyralis… - Water resources …, 2018 - Springer
We provide contingent empirical evidence on the solutions to three problems associated
with univariate time series forecasting using machine learning (ML) algorithms by …
with univariate time series forecasting using machine learning (ML) algorithms by …