Open AccessAssessing Gene Expression Programming as a technique for seasonal streamflow prediction: A case study of NSW
Author(s) -
Rijwana Esha,
Monzur Alam Imteaz,
Abolfazl Nazari
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/351/1/012004
Subject(s) - predictability , streamflow , mean squared error , indian ocean dipole , gene expression programming , climatology , pacific decadal oscillation , regression , correlation , environmental science , statistics , mathematics , el niño southern oscillation , computer science , drainage basin , geography , geology , machine learning , cartography , geometry
This research aims to provide long term streamflow forecast models using multiple climate indices as the predictors with the help of an advanced evolutionary method, Gene Expression Programming (GEP) to solve the developed symbolic regression problems as it is found to be superior than other traditional methods. Being a transparent model, GEP is able to provide the relationship between input (climate indices) and output (streamflow) variables with mathematical expressions which help the users to understand the underlying hydrological process between the climate mode and streamflow without having much knowledge about the used software. Two stations of New South Wales (NSW) are chosen based on their longer data record and fewer missing values. Several preliminary researches including single and multiple correlation analyses reveal PDO (Pacific Decadal Oscillation), IPO (Inter Decadal Pacific Oscillation), IOD (Indian Ocean Dipole) and ENSO (El Nino Southern Oscillation) are few among the influential indices on the study region. The resultant models appear to be more efficient with up to 50% higher Pearson correlation (r) values than that of the simple MLR technique adapted in one of our previous studies. Furthermore, the statistical performance analyses including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Willmott index of agreement (d) and Nash-Sutcliffe efficiency (NSE) ensure high predictability of the developed models. The similar correlation values (r) generated from calibration and validation periods which ranges between 0.74 and 0.91 increase the reliability of the resultant models for predicting seasonal streamflow up to three months in advance.