dc.contributor.advisor | Smit, G. J. F. | en_ZA |
dc.contributor.advisor | Brink, Willie | en_ZA |
dc.contributor.advisor | Wilms, Josefine M. | en_ZA |
dc.contributor.author | Steyn, Melise | en_ZA |
dc.contributor.other | Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences (Applied Mathematics) | en_ZA |
dc.date.accessioned | 2018-02-15T14:36:17Z | |
dc.date.accessioned | 2018-04-09T06:54:52Z | |
dc.date.available | 2018-02-15T14:36:17Z | |
dc.date.available | 2018-04-09T06:54:52Z | |
dc.date.issued | 2018-03 | |
dc.identifier.uri | http://hdl.handle.net/10019.1/103394 | |
dc.description | Thesis (MSc)--Stellenbosch University, 2018. | en_ZA |
dc.description.abstract | ENGLISH ABSTRACT : Stream flow is an important component in the hydrological cycle and plays a
vital role in many hydrological applications. Accurate stream flow forecasts
may be used for the study of various hydro-environmental aspects and may
assist in reducing the consequences of floods. The utility of time series records
for stream flow analyses is often dependent on continuous, uninterrupted observations.
However, interruptions are often unavoidable and may negatively
impact the sustainable management of water resources. This study proposes
the application of machine learning techniques to address these hydrological
challenges.
The first part of this study focuses on single station short-term stream flow
forecasting for river basins where historical time series data are available. Two
machine learning techniques were investigated, namely support vector regression
and multilayer perceptrons. Each model was trained on historical stream
flow and precipitation data to forecast stream flow with a lead time of up to
seven days. The Shoalhaven, Herbert and Adelaide rivers in Australia were
considered for experimentation. The predictive performance of each model
was determined by the Pearson correlation coefficient, the root mean squared
error and the Nash-Sutcliffe efficiency, and the predictive capabilities of the
models were compared to that of a physically based stream flow forecasting
model currently supplied by the Australian Bureau of Meteorology. Based on
the results, it was concluded that the machine learning models have the ability
to overcome certain challenges faced by physically based models and the
potential to be useful stream flow forecasting tools in river basin modelling.
The second part of this study investigates the ability of support vector regression
and multilayer perceptron models to infill incomplete stream flow records.
The infilling techniques relied upon data from donor stations and rain gauges
within close proximity to the station considered for infilling. A case study was
conducted on a channel in the Goulburn basin in Australia. The results showed
the promising role of machine learning applications for the infilling of gaps in
stream flow records and indicated that data from donor stations contribute
more to the success of these models compared to precipitation data. | en_ZA |
dc.description.abstract | AFRIKAANSE OPSOMMING : Stroomvloei is ’n belangrike komponent in die hidrologiese siklus en speel ’n
prominente rol in verskeie hidrologiese toepassings. Akkurate stroomvloeivoorspellings
kan vir die bestudering van verskeie hidrologiese omgewingsaspekte
gebruik word en kan help om die nagevolge van vloede te verminder.
Die gebruik van tydreeksdata vir stroomvloei-analise is dikwels afhanklik van
ononderbroke waarnemings. Onderbrekings is egter dikwels onvermydelik en
kan ’n negatiewe impak op die volhoubare bestuur van waterhulpbronne hê.
In hierdie studie is die toepassing van masjienleertegnieke met die doel om
hierdie hidrologiese uitdagings aan te spreek, bestudeer.
In die eerste gedeelte van hierdie studie is daar op korttermyn stroomvloeivoorspellings
by meetstasies wat oor beskikbare historiese tydreeksdata beskik,
gefokus. Twee masjienleertegnieke is ondersoek, naamlik steunvektor-regressie
en multi-laag perseptron modelle. Elke model is op historiese stroomvloei- en
reënvaldata afgerig om stroomvloei tot en met sewe dae vooruit te voorspel.
Eksperimente is op die Shoalhaven, Herbert en Adelaide riviere in Australië
uitgevoer. Die voorspellingsvermoëns van elke model is deur die Pearsonkorrelasiekoëffisiënt,
die wortel-gemiddelde-kwadraat fout en die Nash-Sutcliffedoeltreffendheid
bepaal, en is met dié van ’n fisiese stroomvloeivoorspellingsmodel
wat tans deur die Australiese Buro vir Meteorologie verskaf word, vergelyk.
Op grond van die resultate is daar tot die gevolgtrekking gekom dat
die masjienleermodelle oor die vermoëns beskik om sekere uitdagings waarmee
fisiese modelle gekonfronteer word, te oorkom, en dat hulle ’n waardevolle
bydrae tot die modellering van riverkomme kan lewer.
In die tweede gedeelte van hierdie studie is steunvektor-regressie en multilaag
perseptron modelle se vermoëns om onvolledige stroomvloeistate te vul,
ondersoek. Die invultegnieke was afhanklik van data vanaf ander nabygeleë
meetstasies en reënmeters. ’n Gevallestudie is op ’n kanaal in die Goulburn
opvangsgebied in Australië uitgevoer. Die resultate het die belowende rol van
masjienleertoepassings op die invul van gapings in stroomvloeistate getoon en
aangedui dat data van meetstasies ’n groter bydrae tot die sukses van hierdie
modelle lewer in vergelyking met reënvaldata. | af_ZA |
dc.language.iso | en_ZA | en_ZA |
dc.publisher | Stellenbosch : Stellenbosch University | en_ZA |
dc.subject | Hydrology -- Data processing | en_ZA |
dc.subject | Streamflow | en_ZA |
dc.subject | Machine learning -- Models | en_ZA |
dc.subject | Rivers -- Australia | en_ZA |
dc.subject | Forecasting -- Mathematical models | en_ZA |
dc.title | Short-term stream flow forecasting and downstream gap infilling using machine learning techniques | en_ZA |
dc.type | Thesis | en_ZA |
dc.rights.holder | Stellenbosch University | en_ZA |