TY - GEN
T1 - Application of LSSVM in estimating the metabolism rate for a river restoration study
AU - Huang, Wei
AU - Yano, Shinichiro
AU - Zhang, Jianmin
N1 - Funding Information:
This work was supported financially by the open fund of state key laboratory of hydraulics and mountain river engineering, Sichuan University, China. We also give our thanks to Doctor M.Saito and other students in Kyushu University, Japan, for their help in the field measurement.
Publisher Copyright:
© 34th IAHR Congress 2011. All rights reserved.
PY - 2011
Y1 - 2011
N2 - In this paper, we employ the least square support vector machine (LSSVM) to study the river metabolism, which is one of the most integrative ecosystem functions, highly sensitive to many anthropogenic and natural stressors and thereby often used to assess the impairment or health state of river ecosystem. With the data from the continual field measurement, the discharge, travel time of water mass, dissolved oxygen concentration and water temperature are selected as the main variables for LSSVM. From the results, it can be found that the LSSVM can be used successfully in predicting the metabolism rate. Moreover, compared to other artificial intelligence tools like back propagation artificial neural networks (BP_ANN), it seems that LSSVM can perform better with higher accuracy and shorter time computation. Thus, it may be considered as an alternative method to estimate the metabolism rate and to assess the river ecosystem health in river study.
AB - In this paper, we employ the least square support vector machine (LSSVM) to study the river metabolism, which is one of the most integrative ecosystem functions, highly sensitive to many anthropogenic and natural stressors and thereby often used to assess the impairment or health state of river ecosystem. With the data from the continual field measurement, the discharge, travel time of water mass, dissolved oxygen concentration and water temperature are selected as the main variables for LSSVM. From the results, it can be found that the LSSVM can be used successfully in predicting the metabolism rate. Moreover, compared to other artificial intelligence tools like back propagation artificial neural networks (BP_ANN), it seems that LSSVM can perform better with higher accuracy and shorter time computation. Thus, it may be considered as an alternative method to estimate the metabolism rate and to assess the river ecosystem health in river study.
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M3 - Conference contribution
AN - SCOPUS:85066125830
T3 - 34th IAHR Congress 2011 - Balance and Uncertainty: Water in a Changing World, Incorporating the 33rd Hydrology and Water Resources Symposium and the 10th Conference on Hydraulics in Water Engineering
SP - 4376
EP - 4383
BT - 34th IAHR Congress 2011 - Balance and Uncertainty
PB - International Association for Hydro-Environment Engineering and Research (IAHR)
T2 - 34th IAHR Congress 2011 - Balance and Uncertainty: Water in a Changing World, Incorporating the 33rd Hydrology and Water Resources Symposium and the 10th Conference on Hydraulics in Water Engineering
Y2 - 26 June 2011 through 1 July 2011
ER -