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Speleology in Kazakhstan

Shakalov on 04 Jul, 2018
Hello everyone!   I pleased to invite you to the official site of Central Asian Karstic-Speleological commission ("Kaspeko")   There, we regularly publish reports about our expeditions, articles and reports on speleotopics, lecture course for instructors, photos etc. ...

New publications on hypogene speleogenesis

Klimchouk on 26 Mar, 2012
Dear Colleagues, This is to draw your attention to several recent publications added to KarstBase, relevant to hypogenic karst/speleogenesis: Corrosion of limestone tablets in sulfidic ground-water: measurements and speleogenetic implications Galdenzi,

The deepest terrestrial animal

Klimchouk on 23 Feb, 2012
A recent publication of Spanish researchers describes the biology of Krubera Cave, including the deepest terrestrial animal ever found: Jordana, Rafael; Baquero, Enrique; Reboleira, Sofía and Sendra, Alberto. ...

Caves - landscapes without light

akop on 05 Feb, 2012
Exhibition dedicated to caves is taking place in the Vienna Natural History Museum   The exhibition at the Natural History Museum presents the surprising variety of caves and cave formations such as stalactites and various crystals. ...

Did you know?

That lost circulation is the result of drilling fluid escaping from a borehole into the formation by way of crevices within the formation [6]. it is a common occurrence in most karst aquifers due to the existence of large subsurface voids that are sometimes intersected during a drilling program.?

Checkout all 2699 terms in the KarstBase Glossary of Karst and Cave Terms

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KarstBase a bibliography database in karst and cave science.

Featured articles from Cave & Karst Science Journals
Chemistry and Karst, White, William B.
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Calculating flux to predict future cave radon concentrations, Rowberry, Matt; Marti, Xavi; Frontera, Carlos; Van De Wiel, Marco; Briestensky, Milos
Microbial mediation of complex subterranean mineral structures, Tirato, Nicola; Torriano, Stefano F.F;, Monteux, Sylvain; Sauro, Francesco; De Waele, Jo; Lavagna, Maria Luisa; D’Angeli, Ilenia Maria; Chailloux, Daniel; Renda, Michel; Eglinton, Timothy I.; Bontognali, Tomaso Renzo Rezio
Evidence of a plate-wide tectonic pressure pulse provided by extensometric monitoring in the Balkan Mountains (Bulgaria), Briestensky, Milos; Rowberry, Matt; Stemberk, Josef; Stefanov, Petar; Vozar, Jozef; Sebela, Stanka; Petro, Lubomir; Bella, Pavel; Gaal, Ludovit; Ormukov, Cholponbek;
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601 DEMPSEY RD, WESTERVILLE, OH 43081 USA
Ground Water, 2001, Vol 39, Issue 1, p. 109-118
Forecasting of turbid floods in a coastal, chalk karstic drain using an artificial neural network
Abstract:
Water collected at the Yport (eastern Normandy, France) Drinking Water Supply well, situated on a karst cavity, is affected by surface runoff-related turbidity spikes that occur mainly in winter, In order to forecast turbidity, precipitation was measured at the center of the catchment basin over two years, while water level and turbidity were monitored at the web site. Application of the approach of Box and Jenkins (1976) leads to a linear model that can accurately predict major floods about eight hours in advance, providing an estimate of turbidity variation on the basis of precipitation and mater level variation over the previous 24 hours. However, this model is intrinsically unable to deal with (1) nonstationary changes in the time process caused by seasonal variations of in ground surface characteristics or tidal influence within the downstream past of the aquifer, and (2) nonlinear phenomena such as the threshold for the onset of runoff. This results in many false-positive signals of turbidity in summer. Here we present an alternative composite model combining a conceptual runoff submodel with a feedforward artificial neural network (ANN), This composite model allows us to deal with meaningful variables, the actioneffect of which on turbidity is complex, nonlinear, temporally variable and often poorly described. Predictions are markedly improved, i.e,, the variance of the target variable explained by 12-hour forward predictions increases from 28% to 74% and summer inaccuracies are considerably lowered. The ANN can adjust itself to new hydrological conditions, provided that on-line learning is maintained