Comparative Analysis of the Predictability of Linear \& Non-linear Methods for Seasonal Streamflow Forecasting: A Case Study of New South Wales (NSW)

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@PhdThesis{Rijwana_Esha_Thesis,

• author = "Rijwana Ishat Esha",
• title = "Comparative Analysis of the Predictability of Linear \& Non-linear Methods for Seasonal Streamflow Forecasting: A Case Study of New South Wales (NSW)",
• school = "Department of Civil and Construction Engineering Faculty of Science, Engineering and Technology Swinburne University of Technology",
• year = "2020",
• address = "Hawthorn, VIC 3122 Australia",
• month = dec,
• keywords = "genetic algorithms, genetic programming, GEP",
• URL = "http://hdl.handle.net/1959.3/459586",
• URL = "https://researchbank.swinburne.edu.au/file/4c9cf5d5-4cd9-4479-8036-a33044d80c64/1/Rijwana_Esha_Thesis.pdf",
• size = "361 pages",
• abstract = "High inter-annual variability of stream-flow resulting from the extensive topographic variation and climatic inconsistency cause immense difficulties to the water users and planners of Australia. New South Wales, which is situated in the south-eastern part of Australia, is the most populous state and is one of the major contributors of Australia agricultural income. The inter-annual variation of streamflow hampers the agricultural production and proper allocation of water of the state largely. Therefore, prediction of streamflow over a large time period will enable the water allocators and agricultural producers to take the low-risk decision at an earlier stage of the crop year which will ultimately enhance the economic growth of the country. Since streamflow is largely dependent on rainfall, it appears to be a more complex phenomenon compared to rainfall. Thereby, long-lead forecasting of streamflow rather than rainfall will be more beneficial to the irrigators. To date, many researchers have attempted to predict future streamflow and rainfall using oceanic and atmospheric indices with the help of both statistical and dynamic approaches. While most of the past studies were concentrated on revealing the relationship between streamflow of single concurrent or lagged climate indices, this study makes an effort to explore the combined impact of large-scale climate drivers to forecast seasonal streamflow of New South Wales (NSW) region. To accomplish the aim of this study, several oceanic and atmospheric climate indices are selected considering their influence on the streamflow of NSW which includes but not limited to four major climate drivers of this region PDO (Pacific Decadal Oscillation), IPO (Inter Decadal Pacific Oscillation), IOD (Indian Ocean Dipole) and the ENSO (El Nino Southern Oscillation) indices. Many past research works demonstrated that different regions of NSW are influenced by different climate modes which lead the present study to divide NSW into four regions with a view to identifying the regional variation of the impacts of various climate drivers. At first single lagged co-rrelation analysis is performed to identify the individual interactions of indices with spring streamflow till nine lagged months which is, later on, exploited as the basis for selecting input variables for developing Multiple Linear Regression (MLR) models to examine the extent of the combined impact of the selected climate drivers on forecasting spring streamflow several months ahead. As many researchers have claimed that a non-linear approach may better capture the relationship between climate variables and seasonal streamflow, Multiple Non-Linear Regression (MNLR) Analysis is conducted to explore the underlying non-linear relationship between seasonal streamflow and climate indices. Finally, for further improvement, an Artificial Intelligence (AI) based method, Gene Expression Programming (GEP) is introduced to evaluate the potential of this method for forecasting seasonal streamflow of NSW. Performances of the developed models are assessed using standard statistical measures such as RRSE (Root Relative Squared Error), RAE ( Relative Absolute Error), RMSE ( Root Mean Square Error), MAE (Mean Absolute Error) and Pearson correlation (r) values. A comparative analysis is performed among the applied methods where GEP method has outperformed the other two methods. The highest predictabilities of the GEP based models are evident from the Pearson correlation (r) values ranging between 0.57 and 0.97, which are mostly about twice the values achieved by MLR and MNLR models. The developed GEP models are able to predict spring streamflow up to 5 months in advance with significantly high correlation values. The current study showed better performances while compared to the previous research studies in this field. This research concludes that GEP models can be used to predict seasonal streamflow of NSW incorporating large-scale multiple climate indices as predictors. In future, a similar concept will be applied to other regions for other seasons to explore the spatial and seasonal variation of influences different climate indices on seasonal streamflow",
• notes = "Supervisor: Monzur Alam Imteaz",

}

Genetic Programming entries for Rijwana Esha

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