Noise causes to decrease the performance of simulation models. Data disturbance and inefficient simulation models are two important roots of noise. Data disturbance is resulted by errors which occur in observations and measurements. Kalman's approach in filtering the data disturbance is well-known method to reduce the noise of modeling procedure. Nowadays, the extended Kalman filters are applied to both linear or nonlinear state space and arbitrary data disturbance. In this paper, the authors applied the Kalman filter to improve two classical classes of run-off routing methods in a reach with 63.2 Km length, between Bamdej and Harmaleh stations in Khouzestan province. The results of comparing two approaches, fully adaptive and Kalman filter, showed noticeable improvement in application of filtering regarding to run-off simulation and reducing the error of computation versus observation.
Moeini, A., Nasseri, M., & Abdollahi Memarzadeh, S. (2011). More Investigations on Kalman Filter Correction of Muskingum and ARMA Methods in Flood Routing on Karoon River Data. Journal of Civil and Surveying Engineering, 45(3), 371-378.
MLA
A. Moeini; M. Nasseri; S.H.R. Abdollahi Memarzadeh. "More Investigations on Kalman Filter Correction of Muskingum and ARMA Methods in Flood Routing on Karoon River Data", Journal of Civil and Surveying Engineering, 45, 3, 2011, 371-378.
HARVARD
Moeini, A., Nasseri, M., Abdollahi Memarzadeh, S. (2011). 'More Investigations on Kalman Filter Correction of Muskingum and ARMA Methods in Flood Routing on Karoon River Data', Journal of Civil and Surveying Engineering, 45(3), pp. 371-378.
VANCOUVER
Moeini, A., Nasseri, M., Abdollahi Memarzadeh, S. More Investigations on Kalman Filter Correction of Muskingum and ARMA Methods in Flood Routing on Karoon River Data. Journal of Civil and Surveying Engineering, 2011; 45(3): 371-378.
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