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Open AccessArticle

Simulation of Non-Gaussian Correlated Random Variables, Stochastic Processes and Random Fields: Introducing the anySim R-Package for Environmental Applications and Beyond

Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Heroon Polytechneiou 5, 15780 Zographou, Greece
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Water 2020, 12(6), 1645; https://doi.org/10.3390/w12061645
Received: 2 May 2020 / Revised: 28 May 2020 / Accepted: 1 June 2020 / Published: 8 June 2020
Stochastic simulation has a prominent position in a variety of scientific domains including those of environmental and water resources sciences. This is due to the numerous applications that can benefit from it, such as risk-related studies. In such domains, stochastic models are typically used to generate synthetic weather data with the desired properties, often resembling those of hydrometeorological observations, which are then used to drive deterministic models of the understudy system. However, generating synthetic weather data with the desired properties is not an easy task. This is due to the peculiarities of such processes, i.e., non-Gaussianity, intermittency, dependence, and periodicity, and the limited availability of open-source software for such purposes. This work aims to simplify the synthetic data generation procedure by providing an R-package called anySim, specifically designed for the simulation of non-Gaussian correlated random variables, stochastic processes at single and multiple temporal scales, and random fields. The functionality of the package is demonstrated through seven simulation studies, accompanied by code snippets, which resemble real-world cases of stochastic simulation (i.e., generation of synthetic weather data) of hydrometeorological processes and fields (e.g., rainfall, streamflow, temperature, etc.), across several spatial and temporal scales (ranging from annual down to 10-min simulations). View Full-Text
Keywords: R-package; stochastic simulation; non-gaussian; random variables; stochastic processes; random fields; disaggregation models; weather generation; synthetic time series R-package; stochastic simulation; non-gaussian; random variables; stochastic processes; random fields; disaggregation models; weather generation; synthetic time series
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MDPI and ACS Style

Tsoukalas, I.; Kossieris, P.; Makropoulos, C. Simulation of Non-Gaussian Correlated Random Variables, Stochastic Processes and Random Fields: Introducing the anySim R-Package for Environmental Applications and Beyond. Water 2020, 12, 1645.

AMA Style

Tsoukalas I, Kossieris P, Makropoulos C. Simulation of Non-Gaussian Correlated Random Variables, Stochastic Processes and Random Fields: Introducing the anySim R-Package for Environmental Applications and Beyond. Water. 2020; 12(6):1645.

Chicago/Turabian Style

Tsoukalas, Ioannis; Kossieris, Panagiotis; Makropoulos, Christos. 2020. "Simulation of Non-Gaussian Correlated Random Variables, Stochastic Processes and Random Fields: Introducing the anySim R-Package for Environmental Applications and Beyond" Water 12, no. 6: 1645.

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