Comparative analysis of river flow modelling by using supervised learning technique

• Main Authors: Ismail, Shuhaida, Pandiahi, Siraj Mohamad, Shabri, Ani, Mustapha, Aida
• Format: Conference or Workshop Item
• Language: English
• Published: 2018
• Subjects: TD194-195 Environmental effects of industries and plants
• 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
• Institution: Universiti Tun Hussein Onn Malaysia

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.