Hristos Tyralis

Hristos Tyralis
Hellenic Air Force · Air Force Support Command

PhD

About

139
Publications
58,115
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
2,044
Citations

Publications

Publications (139)
Article
The finding of important explanatory variables for the location and scale parameters of the generalized extreme value (GEV) distribution, when the latter is used for the modelling of annual streamflow maxima, is known to have reduced the uncertainties in inferences, as estimated through regional flood frequency analysis frameworks. However, importa...
Article
Full-text available
Random forests (RF) is a supervised machine learning algorithm, which has recently started to gain prominence in water resources applications. However, existing applications are generally restricted to the implementation of Breiman’s original algorithm for regression and classification problems, while numerous developments could be also useful in s...
Article
Post-processing of hydrological model simulations using machine learning algorithms can be applied to quantify the uncertainty of hydrological predictions. Combining multiple diverse machine learning algorithms (referred to as base-learners) using stacked generalization (stacking, i.e. a type of ensemble learning) is considered to improve predictio...
Article
Full-text available
Daily streamflow forecasting through data-driven approaches is traditionally performed using a single machine learning algorithm. Existing applications are mostly restricted to examination of few case studies, not allowing accurate assessment of the predictive performance of the algorithms involved. Here, we propose super learning (a type of ensemb...
Article
Full-text available
Machine learning algorithms have been extensively exploited in energy research, due to their flexibility, automation and ability to handle big data. Among the most prominent machine learning algorithms are the boosting ones, which are known to be “garnering wisdom from a council of fools”, thereby transforming weak learners to strong learners. Boos...
Preprint
Full-text available
Predictions and forecasts of machine learning models should take the form of probability distributions, aiming to increase the quantity of information communicated to end users. Although applications of probabilistic prediction and forecasting with machine learning models in academia and industry are becoming more frequent, related concepts and met...
Article
Full-text available
Given the increasing intensity and frequency of flood events, and the casualties and cost associated with bridge collapse events, explaining the flood behavior for the collapse sites would be of great necessity. In this study, annual peak flows of two hundred and five watersheds, associated with two hundred and ninety-seven collapse sites, are anal...
Preprint
Full-text available
Probabilistic forecasting is receiving growing attention nowadays in a variety of applied fields, including hydrology. Several machine learning concepts and methods are notably relevant to formalizing and optimizing probabilistic forecasting implementations by addressing the relevant challenges. Nonetheless, practically-oriented reviews focusing on...
Presentation
Full-text available
Both the grouping of hydroclimatic time series (often required, e.g., for technical and operational purposes) and the identification of spatial hydroclimatic patterns can be formalized and automated through algorithmic clustering methodologies. In this presentation, we focus on a new family of such methodologies that can be applied to various types...
Presentation
Full-text available
Detailed investigations across time scales and variable types can progress our understanding of hydroclimate. In this work, we analyse temperature, precipitation and streamflow time series at nine time scales (i.e., the 1-day, 2-day, 3-day, 7-day, 0.5-month, 1-month, 2-month, 3-month and 6-month ones). The analyses are performed over the continenta...
Article
Full-text available
Regression-based frameworks for streamflow regionalization are built around catchment attributes that traditionally originate from catchment hydrology, flood frequency analysis and their interplay. In this work, we deviated from this traditional path by formulating and extensively investigating the first regression-based streamflow regionalization...
Preprint
Full-text available
Detailed feature investigations and comparisons across climates, continents and time series types can progress our understanding and modelling ability of the Earth's hydroclimate and its dynamics. As a step towards these important directions, we here propose and extensively apply a multifaceted and engineering-friendly methodological framework for...
Preprint
Full-text available
Regression-based frameworks for streamflow regionalization are built around catchment attributes that traditionally originate from catchment hydrology, flood frequency analysis and their interplay. In this work, we deviated from this traditional path by formulating and extensively investigating the first regression-based streamflow regionalization...
Preprint
Full-text available
Hydrological post-processing using quantile regression algorithms constitutes a prime means of estimating the uncertainty of hydrological predictions. Nonetheless, conventional large-sample theory for quantile regression does not apply sufficiently far in the tails of the probability distribution of the dependent variable. To overcome this limitati...
Article
Full-text available
Statistical analyses and descriptive characterizations are sometimes assumed to be offering information on time series forecastability. Despite the scientific interest suggested by such assumptions, the relationships between descriptive time series features (e.g., temporal dependence, entropy, seasonality, trend and linearity features) and actual t...
Preprint
Full-text available
Predictions of hydrological models should be probabilistic in nature. Our aim is to introduce a method that estimates directly the uncertainty of hydrological simulations using expectiles, thus complementing previous quantile-based direct approaches. Expectiles are new risk measures in hydrology. They are least square analogues of quantiles and can...
Article
Full-text available
Predictive uncertainty in hydrological modelling is quantified by using post-processing or Bayesian-based methods. The former methods are not straightforward and the latter ones are not distribution-free (i.e., assumptions on the probability distribution of the hydrological model’s output are necessary). To alleviate possible limitations related to...
Preprint
Full-text available
A comprehensive understanding of the behaviours of the various geophysical processes requires, among others, detailed investigations across temporal scales. In this work, we propose a new time series feature compilation for advancing and enriching such investigations in a hydroclimatic context. This specific compilation can facilitate largely inter...
Preprint
Full-text available
Predictive uncertainty in hydrological modelling is quantified by using post-processing or Bayesian-based methods. The former methods are not straightforward and the latter ones are not distribution-free. To alleviate possible limitations related to these specific attributes, in this work we propose the calibration of the hydrological model by usin...
Presentation
Full-text available
Hydroclimatic time series analysis focuses on a few feature types (e.g., autocorrelations, trends, extremes), which describe a small portion of the entire information content of the observations. Aiming to exploit a larger part of the available information and, thus, to deliver more reliable results (e.g., in hydroclimatic time series clustering co...
Poster
Full-text available
We use 40-year-long time series originating from over 13 000 stations around the globe for the identification of patterns related to the spatial variability of mean monthly temperature, total monthly precipitation and mean monthly river flow features. To formalize this identification, we also develop and apply a new hydroclimatic time series cluste...
Preprint
Full-text available
Statistical analyses and descriptive characterizations are sometimes assumed to be offering information on time series forecastability. Despite the scientific interest suggested by such assumptions, the relationships between descriptive time series features (e.g., temporal dependence, entropy, seasonality, trend and nonlinearity features) and actua...
Article
Hydroclimatic time series analysis focuses on a few feature types (e.g., autocorrelations, trends, extremes), which describe a small portion of the entire information content of the observations. Aiming to exploit a larger part of the available information and, thus, to deliver more reliable results (e.g., in hydroclimatic time series clustering co...
Presentation
Full-text available
We discuss possible pathways towards reducing uncertainty in predictive modelling contexts in hydrology. Such pathways may require big datasets and multiple models, and may include (but are not limited to) large-scale benchmark experiments, forecast combinations, and predictive modelling frameworks with hydroclimatic time series analysis and cluste...
Presentation
Full-text available
Probabilistic hydrological modelling methodologies often comprise two-stage post-processing schemes, thereby allowing the exploitation of the information provided by conceptual or physically-based rainfall-runoff models. They might also require issuing an ensemble of rainfall-runoff model simulations by using the rainfall-runoff model with differen...
Article
Full-text available
Hydrological signatures, i.e., statistical features of streamflow time series, are used to characterize the hydrology of a region. A relevant problem is the prediction of hydrological signatures in ungauged regions using the attributes obtained from remote sensing measurements at ungauged and gauged regions together with estimated hydrological sign...
Chapter
Traditional methods for streamflow forecasting include statistical models, which have outperformed machine learning algorithms at large timescales (i.e., monthly and annual) in the absence of informative exogenous variables. This chapter presents an overview as well as the theory of statistical models and methods for forecasting of streamflow time...
Article
Delivering useful hydrological forecasts is critical for urban and agricultural water management, hydropower generation, flood protection and management, drought mitigation and alleviation, and river basin planning and management, among others. In this work, we present and appraise a new simple and flexible methodology for hydrological time series...
Preprint
Hydroclimatic time series analysis focuses on a few feature types (e.g., autocorrelations, trends, extremes), which describe a small portion of the entire information content of the observations. Aiming to exploit a larger part of the available information and, thus, to deliver more reliable results (e.g., in hydroclimatic time series clustering co...
Method
A blog post discussing how to use machine learning algorithms for probabilistic hydrological post-processing and forecasting.
Preprint
Full-text available
Machine learning algorithms have been extensively exploited in (renewable) energy research, due to their flexibility, automation and ability to handle big data. Among the most prominent machine learning algorithms are the boosting ones, which are known to be "garnering wisdom from a council of fools", thereby transforming weak learners to strong le...
Article
Predictive hydrological uncertainty can be quantified by using ensemble methods. If properly formulated, these methods can offer improved predictive performance by combining multiple predictions. In this work, we use 50-year-long monthly time series observed in 270 catchments in the United States to explore the performances provided by an ensemble...
Preprint
Full-text available
Delivering useful hydrological forecasts is critical for urban and agricultural water management, hydropower generation, flood protection and management, drought mitigation and alleviation, and river basin planning and management, among others. In this work, we present and appraise a new methodology for hydrological time series forecasting. This me...
Article
Full-text available
We conduct a large-scale benchmark experiment aiming to advance the use of machine-learning quantile regression algorithms for probabilistic hydrological post-processing “at scale” within operational contexts. The experiment is set up using 34-year-long daily time series of precipitation, temperature, evapotranspiration and streamflow for 511 catch...
Preprint
Full-text available
Daily streamflow forecasting through data-driven approaches is traditionally performed using a single machine learning algorithm. Existing applications are mostly restricted to examination of few case studies, not allowing accurate assessment of the predictive performance of the algorithms involved. Here we propose super learning (a type of ensembl...
Preprint
Full-text available
We conduct a large-scale benchmark experiment aiming to advance the use of machine-learning quantile regression algorithms for probabilistic hydrological post-processing "at scale" within operational contexts. The experiment is set up using 34-year-long daily time series of precipitation, temperature, evapotranspiration and streamflow for 501 catch...
Article
Full-text available
This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through on-line media, followed by two workshops through which a large number of potential science questions were collated, p...
Presentation
Full-text available
Quantification of predictive uncertainty in hydrological modelling is often made by post-processing point hydrological predictions using regression models. We perform an extensive comparison of machine learning algorithms in obtaining quantile predictions of daily streamflow under this specific approach. The comparison is performed using a large am...
Data
Quantification of predictive uncertainty in hydrological modelling is often made by post-processing point hydrological predictions using regression models. We perform an extensive comparison of machine learning algorithms in obtaining quantile predictions of daily streamflow under this specific approach. The comparison is performed using a large am...
Presentation
Full-text available
Probabilistic streamflow forecasting by postprocessing the outputs of hydrological models is commonly performed using regression models. The relevant applications are mostly based on quantile regression. Ensemble learning of regression (statistical learning) algorithms can improve their generalization ability when applied properly. Here we propose...
Preprint
Full-text available
Predictive hydrological uncertainty can be quantified by using ensemble methods. If properly formulated, these methods can offer improved predictive performance by combining multiple predictions. In this work, we use 50-year-long monthly time series observed in 270 catchments in the United States to explore the performances provided by an ensemble...
Article
Full-text available
Determining the geophysical properties of rocks and geological formations is of high importance in many fields such as geotechnical engineering. In this study, we investigate the second-order dependence structure of spatial (two-dimensional) processes through the statistical perspective of variance vs. scale (else known as the climacogram) instead...
Article
Full-text available
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 based on case studies, while a study providing large-scale results on the subject is missing. Herein, we compare 11 stochastic and 9 ML methods regarding their multi-step...
Article
Full-text available
We present an approach to estimate intensity–duration–frequency (IDF) curves based on max-stable processes. The proposed method has been inspired by the seminal study of Nadarajah et al. (J R Stat Soc B 60(2):473–496, 1998), who used a multivariate extreme value distribution (MEVD) to estimate (IDF) curves from rainfall records. Max-stable processe...
Preprint
Full-text available
Research within the field of hydrology often focuses on comparing stochastic to machine learning (ML) forecasting methods. The comparisons performed are all based on case studies, while an extensive study aiming to provide generalized results on the subject is missing. Herein, we compare 11 stochastic and 9 ML methods regarding their multi-step ahe...
Article
Full-text available
We provide contingent empirical evidence on the solutions to three problems associated with univariate time series forecasting using machine learning (ML) algorithms by conducting an extensive multiple-case study. These problems are: (a) lagged variable selection, (b) hyperparameter handling, and (c) comparison between ML and classical algorithms....
Article
Full-text available
The purpose of this article is to examine the prediction accuracy of the Random Forests, a machine learning method, when it is applied for residential mass appraisals in the city of Nicosia, Cyprus. The analysis is performed using transaction sales data from the Cyprus Department of Lands and Surveys, the Consumer Price Index of Cyprus from the Cyp...
Preprint
Full-text available
The finding of important explanatory variables for the location parameter and the scale parameter of the generalized extreme value (GEV) distribution, when the latter is used for the modelling of annual streamflow maxima, is known to have reduced the uncertainties in inferences, as estimated through regional flood frequency analysis frameworks. How...
Article
Full-text available
We assess the performance of random forests and Prophet in forecasting daily streamflow up to seven days ahead in a river in the US. Both the assessed forecasting methods use past streamflow observations, while random forests additionally use past precipitation information. For benchmarking purposes we also implement a naïve method based on the pre...
Article
Full-text available
We assess the performance of the recently introduced Prophet model in multi-step ahead forecasting of monthly streamflow by using a large dataset. Our aim is to compare the results derived through two different approaches. The first approach uses past information about the time series to be forecasted only (standard approach), while the second appr...
Article
Full-text available
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 monthly temperature and 1552 40-year-long monthly precipitation time series. The methods include a naïve one based on the monthly values of the last year, as well as the random w...
Conference Paper
Full-text available
Multi-step ahead streamflow forecasting is of practical interest. We examine the error evolution in multi-step ahead forecasting by conducting six simulation experiments. Within each of these experiments we compare the error evolution patterns created by 16 forecasting methods, when the latter are applied to 2 000 time series. Our findings suggest...
Presentation
Full-text available
“With four parameters I can fit an elephant, and with five I can make him wiggle his trunk”, ∼ John von Neumann. This famous quote, literally possible as proved by Mayer et al. (2010), has been widely used to question the parsimony of a model providing a good description of the available data. Still, a significant part of the hydrological literatur...
Data
We investigate the one-step ahead predictability of annual geophysical processes using 16 univariate time series forecasting methods. We examine two real-world datasets, a precipitation dataset and a temperature dataset, together containing 297 annual time series of 91 values. We use the first 50, 60, 70, 80 and 90 data points for model-fitting and...
Presentation
Full-text available
We investigate the one-step ahead predictability of annual geophysical processes using 16 univariate time series forecasting methods. We examine two real-world datasets, a precipitation dataset and a temperature dataset, together containing 297 annual time series of 91 values. We use the first 50, 60, 70, 80 and 90 data points for model-fitting and...