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Sampling Designs and Estimation Methods for Sediment Load Prediction in Two Rivers in Iran and Malaysia


Arabkhedri, Mahmood (2009) Sampling Designs and Estimation Methods for Sediment Load Prediction in Two Rivers in Iran and Malaysia. PhD thesis, Universiti Putra Malaysia.


The effect of sampling designs and estimation methods on the accuracy of predicted total suspended sediment load (SSL) were evaluated in Gorgan- Rood and Sg. Pangsun, two rivers in Iran and Malaysia respectively. Concerning the rare clustered nature of high load periods during transport, adaptive cluster sampling (ACS) was conducted. Nevertheless, some adaptations were made because continuous sediment records of study rivers are time-scale populations rather than ordinary spatial-scale in previous studies. This study suggests the use of forward neighborhood relation instead of symmetric and the use of flow duration curve instead of ranking of initial samples. In order to evaluate the ACS in river sediment estimation, 1 2000 sample sets were generated in each river which categorized in scenarios differed in terms of neighborhood relation, flow discharge threshold, cluster size and initial sample size. For all sample sets, total SSLs were predicted using the modified Hansen-Hurwitz (HH) and Horvitz-Thompson (HT) estimators which then were statistically evaluated. The results suggests that ACS can not estimate sediment load properly when sample size is smaller than 15 % of the size of respective population. HT estimator showed a better performance than the HH. Moreover, employing forward neighborhood relation instead of symmetric showed underestimations less than 5%.

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Additional Metadata

Item Type: Thesis (PhD)
Subject: Rivers - Iran - Sedimentation analysis - Case studies
Call Number: FH 2009 10
Chairman Supervisor: Associate Professor Dr. Lai Food See, PhD
Divisions: Faculty of Forestry
Depositing User: Mohd Nezeri Mohamad
Date Deposited: 10 Mar 2011 05:17
Last Modified: 10 Mar 2011 05:18
URI: http://psasir.upm.edu.my/id/eprint/10166
Statistic Details: View Download Statistic

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