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1.
Time-series analysis of oxygen as an important environmental parameter for monitoring diversity hotspot ecosystems : an example of a river sinking into the karst underground
Saptashwa Bhattacharyya, Janez Mulec, Andreea Oarga-Mulec, 2023, original scientific article

Abstract: Predicting variations in dissolved oxygen concentration (DO) is important for management and environmental monitoring of aquatic ecosystems. Regression analyses and univariate and multivariate time-series analyses based on autoregressive methods were performed to investigate oxygen conditions in the Pivka River, Slovenia. The monitoring site was established upstream where the river sinks into the karst cave Postojnska jama, which hosts one of the richest subterranean faunas yet studied worldwide. It was found that abnormal variations of DO started to be noticeable at values of DO < 3 mg/L and became more pronounced until the ecosystem reached fully anoxic conditions. The abnormal fluctuations during the critical summer period were due to environmental conditions, organic load and resident biota. Predictions for future detection of anomalies in DO values were made from stable residuals of the measured data, and it was demonstrated that the model could be used to obtain a reliable estimate for a short period, such as one day. The example presented an analysis pipeline based on specific and established threshold DO values, and it is particularly important for ecosystems with diversity hotspots where prolonged low DO values can pose a threat to their biota.
Keywords: karst (geology), aquatic ecosystems, dissolved oxygen, modelling, prediction
Published in RUNG: 05.09.2023; Views: 1127; Downloads: 5
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2.
A solution technique to cascading link failure prediction
Niknaz Nakhaei, Morteza Ebrahimi, Ahmad Hosseini, 2022, original scientific article

Keywords: Failure prediction, Complex networks, Cascading failure, Bayesian Belief Networks, Operations Research
Published in RUNG: 14.02.2023; Views: 1235; Downloads: 0
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3.
Prediction problem for target events based on the inter-event waiting time
Alexander Shapoval, 2010, original scientific article

Keywords: loss function, coefficient of variation, earthquake prediction
Published in RUNG: 19.04.2021; Views: 2026; Downloads: 0
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4.
A dissipative deterministic BTW model with an activation scenario of strong events
Alexander Shapoval, M. Shnirman, 2009, original scientific article

Keywords: sandpile, earthquake prediction, extremes
Published in RUNG: 19.04.2021; Views: 2152; Downloads: 0
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5.
Prediction of the largest events in the sandpile model based on the earthquake precursors
Alexander Shapoval, M. Shnirman, 2009, original scientific article

Keywords: sadnpile, earthquake prediction, extremes
Published in RUNG: 19.04.2021; Views: 2001; Downloads: 0
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6.
Variable predictability in deterministic dissipative sandpile
M. Shnirman, Alexander Shapoval, 2010, original scientific article

Keywords: sadnpile, prediction, rare events
Published in RUNG: 19.04.2021; Views: 2103; Downloads: 0
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7.
How size of target avalanches influences prediction efficiency
Alexander Shapoval, M. Shnirman, 2004, original scientific article

Abstract: Bak, Tang, and Wiesenfeld [Phys. Rev. Lett.59, 381 (1987)] introduced their sand-pile (BTW sand-pile) as the cellular automata coming to their critical state without tuning any inner model parameters. The main model features deal with grains falling slowly onto the two-dimensional lattice and a quick deterministic transport of the superfluous grains to the boundary. The simplest modifications of the BTW sand-pile develop a random transport mechanism instead of a deterministic one. The model transportation of the grains generates avalanches. We find that before the big avalanches the height of the pile increases and the singular grains organize themselves in special clusters. These observations lead to the formal algorithm that predicts the big avalanches in advance with a certain efficiency. However the efficiency for the BTW sand-pile is worse than that for its stochastic modifications.
Keywords: sandpile, prediction, avalanches
Published in RUNG: 19.04.2021; Views: 1915; Downloads: 0
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8.
Strong events in the sand-pile model
Alexander Shapoval, M. Shnirman, 2004, original scientific article

Abstract: Here is a sand-pile introduced by Bak et al. The system accumulates particles one by one. From time to time it topples. Every toppling initiates an event. The distribution of the events' size follows a power law for all the events except the strongest ones. The fraction of the strongest events does not depend on the system length. The number of particles and their clustering increase before the strongest events.
Keywords: sandpile, prediction, power-law
Published in RUNG: 19.04.2021; Views: 2004; Downloads: 0
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