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Estimating groundwater recharge using chloride mass balance in the upper Berg River catchment, South AfricaMutoti, Mulalo Isaih January 2015 (has links)
>Magister Scientiae - MSc / Previous studies have shown that the use of chloride mass balance (CMB) method is a suitable and practical approach to estimate groundwater recharge. This enables the prediction of groundwater availability to inform practical strategies for managing groundwater resources. However, such studies have largely applied the chloride mass balance method on national and catchment scales with limited focus on quaternary catchment level (QCL). Neglecting the chloride mass balance method at quaternary catchment level limits practical management and utilization of water resources at quaternary catchment level. The goal of the current study was to prove that 1) the chloride mass balance method should be applied at quaternary catchment level to ensure practical assessment of groundwater availability and that 2) chloride mass balance assessment should be accompanied with supplementary methods for its application in quaternary catchments of similar physiographic and hydrogeologic conditions. To achieve these goals, the present study assessed the application of chloride mass balance method on a pilot scale used alongside rainwater infiltration breakthrough (RIB) and water table fluctuation (WTF) methods to estimate the groundwater recharge as an indicator of groundwater availability. The pilot area (PA) was in the upper Berg River catchment in Western Cape in South Africa. Chloride concentrations were determined in groundwater samples collected from boreholes and rain water in rain gauges in the pilot area. Rainfall and borehole water levels in the pilot area were used in water table fluctuation and rainwater infiltration breakthrough analyses. As quality assurance, the specific yield data obtained from the pumping test were compared to those determined with the linear regression model. This established the reliability of the analysis i.e. the relationship between groundwater level and rainfall. Mean groundwater recharge values calculated using the chloride mass balance, rainwater infiltration breakthrough and water table fluctuation methods were 27.6 %, 23.67 %, and 22.7 % of the total precipitation received in the catchment, respectively. These results indicate that the use of these three methods have potential to estimate groundwater recharge at quaternary level which is the basic unit of water management in South Africa. These findings agree with previous studies conducted in the same catchment that indicate that mean groundwater recharge ranges between 18.6 % and 28 % of the total precipitation. In the future, these methods could be tested in catchments which have physiographic and hydrogeologic conditions similar to those of the current pilot area. / African Union (AU)
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Modelling of Inflow and Infiltration into Wastewater Systems with Regression and Random Forest / Modellering av Inflöde och Infiltration i Avloppssystem med Regression och Random ForestSteen Danielsson, Viggo January 2022 (has links)
The aim of this thesis was to evaluate the validity of statistical modelling, with regards to flow in wastewater systems based on precipitation. A wastewater system, which is a system of pump-stations connected via pipes, is affected by precipitation as rainwater enters the system. The rainwater that enters the system can sometimes, especially if the precipitation is heavy, increase the flow by several hundred percent. The goal with the models created for this report was, to first predict how much rainwater there was in the wastewater system based on precipitation, and second, to examine where in a geographical grid this rainwater entered the system. For the first goal a linear regression model was applied, this model showed that it was indeed possible to predict excess water in the system, but large errors for individual time points where to be expected, especially for light precipitation. For the second goal a random forest model was applied. This model however gave no additional insight beyond what an initial correlation study between precipitation in different parts of the geographical grid and flow had already shown. The areas in the geographical grid pointed out by both the random forest model and the initial correlation study were not the actual uptake areas of the pump-station these models were applied to. This leads to the conclusion that for the data set used, and with the models applied in the manner in which they were in this thesis, it was not possible to predict where rainwater enters the system. / Målet med den här rapporten var att undersöka möjligheten att modellera regnvattens påverkan på vattenflödet i avloppsystem, med statistiska modeller. Ett avloppsystem är i dess enklaste form ett nätverk av pumpstationer sammanbunda med rör. Det här systemen påverkas av nederbörd då oönskat regnvatten kommer in via stuprör, avloppsbrunnar och sprickor i rören. Regnvattnet kan i vissa fall, särskilt under perioder med mycket nederbörd, öka flödet i avloppsystemet med flera hundra procent. Modellerna tillverkade för den här rapporten undersökte två olika aspekter av regnvattnets påverkan: Kan flöde i avloppssystemet modelleras med regnvatten? Och kan modellerna avgöra var regnvatten kommer in i avloppsystemet? Den första frågan undersöktes med linjär regressionsanalys, modellerna tillverkade med regression visade att det gick att modellera flöde i avloppsystemet med regnvatten, det bör dock nämnas att stora fel i enskilda tidssteg var att förvänta, speciellt för modellering då nederbörden är låg. Den andra frågan utvärderades med en ”random forest” modell. Den här modellen gav ingen ytterligare information angående var regnvatten kommer in i systemet, utöver vad som redan hade visats av en korrelationsstudie gjord i början av projektet. Både korrelationsstudien och ”random forest” modellen visade också på att regnvattnet kom från platser som inte var sammankopplade med den pumpstation testerna gjordes på. På grund av detta drogs slutsatsen att det inte gick att ta reda på var regnvattnet kommer in i systemet, med den data som använts, och på det sätt som modellerna har använts i den här rapporten.
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