On the long-range dependence properties of annual precipitation using a global network of instrumental measurements

Tyralis, Hristos; Dimitriadis, Panayiotis; Koutsoyiannis, Demetris; O’Connell, P. E.; Tzouka, Katerina; Iliopoulou, Theano

doi:10.1016/j.advwatres.2017.11.010

Cited by 34 publications

(18 citation statements)

References 69 publications

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“…Yet the degree of uncertainty significantly varies across the examined processes. Specifically, we find solar radiation and wave height to exhibit large Hurst parameters, while others such as precipitation exhibit relatively lower ones, the latter being consistent with findings in Tyralis et al, 2017a). The degree of variability of the different processes at the time-scales of interest are crucial for the design and operation of a renewable energy system, where the operation rules of each energy source should be specified in a way that ensures reliability and optimal performance of the whole system.…”

Section: Discussionsupporting

confidence: 70%

A preliminary stochastic analysis of the uncertainty of natural processes related to renewable energy resources

et al. 2018

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Abstract. The ever-increasing energy demand has led to overexploitation of fossil fuels deposits, while renewables offer a viable alternative. Since renewable energy resources derive from phenomena related to either atmospheric or geophysical processes, unpredictability is inherent to renewable energy systems. An innovative and simple stochastic tool, the climacogram, was chosen to explore the degree of unpredictability. By applying the climacogram across the related timeseries and spatial-series it was feasible to identify the degree of unpredictability in each process through the Hurst parameter, an index that quantifies the level of uncertainty. All examined processes display a Hurst parameter larger than 0.5, indicating increased uncertainty on the long term. This implies that only through stochastic analysis may renewable energy resources be reliably manageable and cost efficient. In this context, a pilot application of a hybrid renewable energy system in the Greek island of Astypalaia is discussed, for which we show how the uncertainty (in terms of variability) of the input hydrometeorological processes alters the uncertainty of the output energy values.

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Section: Discussionsupporting

confidence: 70%

A preliminary stochastic analysis of the uncertainty of natural processes related to renewable energy resources

et al. 2018

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“…Classification and regression trees (CARTs, [56]) are methods to partition the variable space based on a set of rules embedded in a decision tree (see Figure 1 below), where each node splits according to a decision rule; see e.g., Hastie et al [58] (pp. [305][306][307][308][309][310][311][312][313][314][315][316][317], and the review in Loh [19]. In this way, the variable space is partitioned into a set of rectangles, and a model is fitted to each set, which in the simplest case can be a constant.…”

Section: Classification and Regression Treesmentioning

confidence: 99%

A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources

Tyralis

Papacharalampous

Langousis

2019

Water

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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 solving diverse practical problems in the water sector. Here we popularize RF and their variants for the practicing water scientist, and discuss related concepts and techniques, which have received less attention from the water science and hydrologic communities. In doing so, we review RF applications in water resources, highlight the potential of the original algorithm and its variants, and assess the degree of RF exploitation in a diverse range of applications. Relevant implementations of random forests, as well as related concepts and techniques in the R programming language, are also covered.

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“…The R code is available upon request to the corresponding author. The analyses were performed in R Programming Language (R Core Team, 2018) using the R packages devtools , forecast (Hyndman and Khandakar, 2008;, fracdiff (Fraley et al, 2012), gdata (Warnes et al, 2017), ggplot2 (Wickham, 2016;Wickham and Chang, 2016), HKprocess (Tyralis, 2016), knitr (Xie, 2014(Xie, , 2015(Xie, , 2018, lubridate (Grolemund and Wickham, 2011;Spinu et al, 2018), maps (Brownrigg et al, 2018), prophet (Taylor and Letham, 2017), readr (Wickham et al, 2017), rmarkdown (Allaire et al, 2018), stringi (Gagolewski, 2018), zoo (Zeileis and Grothendieck, 2005;Zeileis et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow

Tyralis

Papacharalampous

2018

Adv. Geosci.

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Abstract. 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 approach uses exogenous predictor variables alongside with the use of the endogenous ones. The additional information used in the fitting and forecasting processes includes monthly precipitation and/or temperature time series, and their forecasts respectively. Specifically, the exploited exogenous (observed or forecasted) information considered at each time step exclusively concerns the time of interest. The algorithms based on the Prophet model are in total four. Their forecasts are also compared with those obtained using two classical algorithms and two benchmarks. The comparison is performed in terms of four metrics. The findings suggest that the compared approaches are equally useful.

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On the long-range dependence properties of annual precipitation using a global network of instrumental measurements

Cited by 34 publications

References 69 publications

A preliminary stochastic analysis of the uncertainty of natural processes related to renewable energy resources

A preliminary stochastic analysis of the uncertainty of natural processes related to renewable energy resources

A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources

Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow

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