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Forecasting time series water levels on Mekong river using machine learning models
Forecasting water levels on Mekong river is an important problem needed to be studied for flood warning. In this paper, we investigate the application to forecasting of daily water levels at Thakhek station on Mekong river using machine learning models such as LASSO

Forecasting time series water levels

on Mekong river

using machine learning models

Thanh-Tung Nguyen

Faculty of Computer Scienceand Engineering,

Thuyloi University

Email: tungnt@tlu.edu.vn

Quynh Nguyen Huu

Information Technology Faculty,

Electric Power University

Email: quynhnh@epu.edu.vn

Hanoi, Vietnam

 

Abstract—Forecasting    water   levels on Mekong    river  is an important  problem  needed  to  be  studied  for  flood  warning.  In this  paper,  we  investigate  the  application  to  forecasting  of  daily water levels at Thakhek station on Mekong river using machine learning  models  such  as  LASSO,  Random  Forests  and  Support Vector Regression (SVR). Experimental results showed that SVR was  able  to  achieve  feasible  results,  the  mean  absolute  error  of SVR  is    while  the  acceptable  error  of  a  flood  forecast model  required   by the  Mekong   River  Commission  is between 0.5m  and 0.75m.

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