Comparative analysis of river flow modelling by using supervised learning technique
The goal of this research is to investigate the efficiency of three supervised learning algorithms for forecasting monthly river flow of the Indus River in Pakistan, spread over 550 square miles or 1800 square kilometres. The algorithms include the Least Square Support Vector Machine (LSSVM),...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2018
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/6974/1/P9860_86c30fe4c3819f7c20bebe82732ee268.pdf http://eprints.uthm.edu.my/6974/ https://doi.org/10.1088/1742-6596/995/1/012045 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Tun Hussein Onn Malaysia |
| Language: | English |
| Summary: | The goal of this research is to investigate the efficiency of three supervised learning
algorithms for forecasting monthly river flow of the Indus River in Pakistan, spread over 550
square miles or 1800 square kilometres. The algorithms include the Least Square Support Vector
Machine (LSSVM), Artificial Neural Network (ANN) and Wavelet Regression (WR). The
forecasting models predict the monthly river flow obtained from the three models individually
for river flow data and the accuracy of the all models were then compared against each other.
The monthly river flow of the said river has been forecasted using these three models. The
obtained results were compared and statistically analysed. Then, the results of this analytical
comparison showed that LSSVM model is more precise in the monthly river flow forecasting. It
was found that LSSVM has he higher r with the value of 0.934 compared to other models. This
indicate that LSSVM is more accurate and efficient as compared to the ANN and WR model. |
|---|