The Influence of Different Canopies on Groundwater Table Level Changes at Kláštorské Lúky Natural Reserve.

Skalová, Jana; Jaroš, Branislav; Novák, Viliam

doi:10.2478/v10098-009-0024-y

Cited by 2 publications

(3 citation statements)

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“…The escalation of these processes in recent decades threatens and visibly affects the individual components of agricultural land (Bárek et al 2008;Skalová, 2009;Szilagyi, 2011). The question remains to what extent is it possible to put a strain on the landscape system without resulting in irreversible negative changes.…”

Section: Introductionmentioning

confidence: 99%

“…Anthropogenic activity in a combination with land use changes, particularly its vegetation cover but also other possible factors, such as flow changes of ocean currents, solar activity, etc., may affect the overall water balance of regions and the large territorial units. The escalation of these processes in recent decades threatens and visibly affects the individual components of agricultural land (Bárek et al 2008;Skalová, 2009;Szilagyi, 2011). The question remains to what extent is it possible to put a strain on the landscape system without resulting in irreversible negative changes.…”

Section: Introductionmentioning

confidence: 99%

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Soil hydrophysical characteristics in the Nitra river basin (Slovakia): Their monitoring, analysis, online publishing

Igaz¹,

Hořák²,

Šinka³

et al. 2014

EJSS

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Article Info Received : 20.06.2014 Accepted : 14.10.2014 The paper is focused on the purpose made, or local monitoring of areal unit of the Nitra river basin (Slovakia, Central EU with total area 4501 km 2 ) in order to obtain the inputs on soil, moisture and hydrophysical characteristics of the given area. In this study, there was evaluated the share of individual soil texture classes in the Nitra river basin on the basis of map records and its comparison with the soil samples taken from the 111 selected sites. Soil samples were taken from two depths of soil profile (15-20 cm, 40-45 cm). The sites were chosen according to the percentage representation of individual soil texture classes. Based on the identification of sampling points localization and following analysis of granularity ratio, it can be concluded that the grain composition from the soil samplings does not correspond fully with the map records. Subsequently, drainage branches of moisture retention curves were measured for all sites Obtained hydrophysical data were enabled on the web-portal. With the use of OpenGeo Suite software, version 4.0.2 and its components Geoserver and Geoexplorer, the data on soil characteristics were published online at: http://fzki.uniag.sk/02FacultyStructure/02Departments/KBH/02Research/Hydrophysics.Thus, information about the soil characteristics in the basin is available to specialists.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Soil hydrophysical characteristics in the Nitra river basin (Slovakia): Their monitoring, analysis, online publishing

Igaz¹,

Hořák²,

Šinka³

et al. 2014

EJSS

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show abstract

“…For instance, optimum irrigation management requires a systematic estimation of the soil-water status to determine both the appropriate amounts and timing of irrigation. A possible ecological application of soil-water modeling is described, e.g., in Skalová et al, 2009. In general, two categories of methods for evaluating a soil-water regime can be distinguished: (1) the measurement techniques and (2) predictive methods (mathematical modeling).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Three Regression Models for Determining Water Retention Curves

Skalová¹,

Čistý²,

Bezak³

2011

Journal of Hydrology and Hydromechanics

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A key physical property used in the description of a soil-water regime is a soil water retention curve, which shows the relationship between the water content and the water potential of the soil. Pedotransfer functions are based on the supposed dependence of the soil water content on the available soil characteristics, e.g., on the relative content of the particle size in the soil and the dry bulk density of the soil. This dependence could be extracted from the available data by various regression methods. In this paper, artificial neural networks (ANNs) and support vector machines (SVMs) were used to estimate a drying branch of a water retention curve. The paper compares the mentioned methods by estimating the water retention curves on regional scale for the Záhorská lowland in the Slovak Republic, where relatively small data set was available. The performance of the models was evaluated and compared. These computations did not fully confirm the superiority of SVMs over ANNs as is often proclaimed in the literature, because the results obtained show that in this study the ANN model performs somewhat better and is easier to handle in determining pedotransfer functions than the SVM models. Nevertheless, the results from both data-driven models are quite close, and the results show that they provide a significantly more precise outcome than a traditional multi-linear regression does. Autori sa v príspevku venujú určovaniu pedotransferových funkcií (PTF), ktoré umožňujú stanoviť body vlhkostných retenčných kriviek pôdy z ľahšie merateľných pôdnych vlastností a sú dôležitým prvkom modelovania vodného režimu pôdy. Ešte v minulej dekáde sa objavili snahy využívať na ich určenie umelé neurónové siete (UNS). Multi-layer perceptron (MLP) čiže viacvrstvový perceptrón je najčastejšie použí-vaný model doprednej umelej neurónovej siete s kontrolovaným typom učenia. Vstupné signály prechádza-jú sieťou typu MLP iba dopredným smerom, teda postupne od vrstvy k vrstve. MLP používa tri a viac vrstiev neurónov rozdelených na vstupnú, skrytú a výstupnú vrstvu s nelineárnou aktivačnou funkciou a vie rozpoznať alebo modelovať informácie, ktoré nie sú lineárne oddeliteľné alebo závislé. Novší vývoj v oblasti učiacich algoritmov poskytuje ďalšie možnosti, z ktorých sa v tomto príspevku venujeme tzv. mechanizmom podporných vektorov (Support Vector Machines -SVM). SVM využíva pri svojom kalibrovaní na riešený problém princíp tzv. štrukturálnej minimalizácie namiesto iba minimalizácie chyby - (Vapnik, 1995). Pri trénovaní siete MLP je jediným cieľom minimalizovať celkovú chybu. Pri SVM sa simultánne minimalizuje chyba aj zložitosť modelu. Použitie tohto princípu vedie zvyčajne k vyššej schopnosti generalizácie, t.j. umožneniu presnejších predpovedí pre dáta, ktoré neboli použité pri trénovaní SVM. Vhodnosť štandardnej umelej neurónovej siete, SVM a viacnásobnej lineárnej regresie sa v článku vyhodnocuje na základe údajov získaných z pôdnych vzoriek odobratých v lokalite Záhorskej nížiny. Pôvodné údaje a ich aplikáciu pri vyhodnocovaní vodn...

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The Influence of Different Canopies on Groundwater Table Level Changes at Kláštorské Lúky Natural Reserve.

Cited by 2 publications

References 1 publication

Soil hydrophysical characteristics in the Nitra river basin (Slovakia): Their monitoring, analysis, online publishing

Soil hydrophysical characteristics in the Nitra river basin (Slovakia): Their monitoring, analysis, online publishing

Comparison of Three Regression Models for Determining Water Retention Curves

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