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Neural networks and interpolation of metal concentrations in a polluted river
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Science.
1996 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Neurala nät och interpolation av metallkoncentrationer i en förorenad flod (Swedish)
Abstract [en]

In all areas of hydrology, where one or several variables are measured as a function of time, it might be necessary to interpolate the measured variable(s). There is a form of artificial intelligence (AI) called neural networks, which seem to be appropriate for this application. In an environmental project in Bolivia, where, among others, the concentrations of arsenic (As), cadmium (Cd) and lead (Pb) in the polluted Huanuni river was measured, a data set appropriate for application of the neural network method was available. Several neural networks with different configurations were used to interpolate concentration values of the three sampled metals. The neural networks interpolated concentrations at time steps where actual, measured, values existed so that an estimate of the method’s capacity could be obtained. The neural networks’ interpolating capability were compared to that of linear interpolation and linear equations derived by multiple regression. In the case of cadmium, the linear interpolation was the best method, whereas the neural network method was best at interpolating arsenic and lead concentrations. The conclusions of this study are that the neural network method was the most succesful method; that this method was the most complicated to implement; that this method was not fully optimised; and that, to evaluate the neural network method’s general applicability as an interpolation method in hydrology, further studies have to be conducted.

Abstract [sv]

Inom alla områden av hydrologin, där man mäter en eller flera variabler som funktion av tid, kan det finnas ett behov av att interpolera glesa tidsseriedata. Det finns en form av artificiell intelligens (AI) som kallas för neurala nät, vilken verkar vara lämplig att applicera på detta problem. Genom ett miljöprojekt i Bolivia, där bl.a. arsenik-, bly-, och kadmiumkoncentrationer i den förorenade floden Huanuni mättes, blev data lämpliga för applicering av den neurala nätmetoden tillgängliga. Flera neurala nät med olika konfigurationer användes för att interpolera koncentrationsvärden av de tre metallerna. De neurala näten interpolerade värden längs tidssteg där verkliga, uppmätta, koncentrationsvärden fanns, så att en uppskattning av metodens kapacitet kunde göras. Dess kapacitet jämfördes med kapaciteten hos två andra metoder: linjär interpolation och linjära ekvationssystem härledda med hjälp av multipel regression. Den linjära interpolationsmetoden var bäst i fallet kadmium, medan den neurala nätmetoden var bäst i de två andra fallen. Slutsatserna av den här studien är att den neurala nätmetoden var den bästa; att denna metod var den mest komplicerade att använda; att denna metod inte var helt optimerad, och att det krävs ytterligare studier för att utvärdera den neurala nätmetodens allmänna lämplighet som metod att interpolera glesa hydrologiska tidsseriedata.

Place, publisher, year, edition, pages
1996. , p. 31
National Category
Oceanography, Hydrology and Water Resources
Identifiers
URN: urn:nbn:se:uu:diva-395743OAI: oai:DiVA.org:uu-395743DiVA, id: diva2:1365145
Subject / course
Hydrology
Educational program
Mathematics and Natural Sciences Programme
Available from: 2019-10-23 Created: 2019-10-23 Last updated: 2019-10-23Bibliographically approved

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