Open AccessComparison of Artificial Neural Network and Support Vector Machine for Long-Term Runoff Simulation
Author(s) -
Zulkarnain Hassan,
S Z Rosdi,
Ain Nihla Kamarudzaman,
Mustaqqim Abdul Rahim,
Zuhayr Md Ghazaly
Publication year - 2020
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 - 10.1088/1755-1315/476/1/012119
Subject(s) - surface runoff , support vector machine , artificial neural network , term (time) , computer science , process (computing) , environmental science , runoff model , runoff curve number , data mining , machine learning , hydrology (agriculture) , engineering , ecology , physics , geotechnical engineering , quantum mechanics , biology , operating system , watershed
Simulation of runoff from a river catchment is a very difficult task and it involves a lot of data which need to be considered. However, the modelling is very essential to forecast the patterns of future runoff by observing and analysing previous patterns of runoff, based on the rainfall. This study presents the evaluation of rainfall-runoff modelling for the long-term runoff series using Artificial Neural Network (ANN) and Support Vector Machine (SVM). Both models are trained and validated using the data series of current and nine (9) antecedent rainfall. During the validation, the SVM model is better in the performance as compared the ANN model, with the R and RMSE values are 0.529-0.711 and 14.27-52.55 mm, respectively. However, the SVM model is underestimated for the peak discharge. Both models have the ability to derive the relationship between the inputs and outputs of the rainfall-runoff process.
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