Adaptive cluster sampling for a temporal-scale population
Adaptive cluster sampling (ACS) is appropriate for rare clustered populations with localization tendencies. Up to now, it has been used exclusively for investigating spatial-scale problems rather than temporal-scale such as t his study is dealing with, i.e.sediment transport in rivers. Suspended se...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Institute for Mathematical Research, Universiti Putra Malaysia
2010
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/12931/1/Adaptive%20Cluster%20Sampling%20for%20a%20Temporal-Scale%20Population.pdf |
| _version_ | 1825945138821595136 |
|---|---|
| author | Arabkhedri, Mahmood Lai, Food See Ibrahim, Noor Akma Mohamad Kasim, Mohamad Roslan |
| author_facet | Arabkhedri, Mahmood Lai, Food See Ibrahim, Noor Akma Mohamad Kasim, Mohamad Roslan |
| author_sort | Arabkhedri, Mahmood |
| collection | UPM |
| description | Adaptive cluster sampling (ACS) is appropriate for rare clustered populations with localization tendencies. Up to now, it has been used exclusively for investigating spatial-scale problems rather than temporal-scale such as t
his study is dealing with, i.e.sediment transport in rivers. Suspended sediment load is carried mostly during
relatively short periods coincide with high flows otherwise
negligible. In ACS, more samples from critical river stages can be taken with respect to the aggregation tendencies of sediment loads during transport; thus increasing the level of representativeness of samples. Adoption of ACS to this new area needs further verification and adaptation such as definition of the sampling unit, population frame, neighborhood relation, and threshold. In this study, several scenarios were defined for the purpose of evaluating the ACS in sediment estimation. Numerous sample sets were taken from intensive discharge-load records of Sg. Pangsun River, Malaysia. These sample sets are different with respect to initial sample size, neighborhood relation, and discharge threshold. Total suspended sediment loads were then estimated using modified Horvitz-Thompson method. The comparison made between the symmetric
neighborhood relation and the forward method suggested in this study showed that the latter could be used instead of the former in sediment studies without losing the accuracy. The findings also suggested the flow duration curve is a useful tool for ranking initial samples in order to determine an optimum discharge threshold. |
| first_indexed | 2024-03-06T07:26:51Z |
| format | Article |
| id | upm.eprints-12931 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T07:26:51Z |
| publishDate | 2010 |
| publisher | Institute for Mathematical Research, Universiti Putra Malaysia |
| record_format | dspace |
| spelling | upm.eprints-129312015-08-30T00:45:19Z http://psasir.upm.edu.my/id/eprint/12931/ Adaptive cluster sampling for a temporal-scale population Arabkhedri, Mahmood Lai, Food See Ibrahim, Noor Akma Mohamad Kasim, Mohamad Roslan Adaptive cluster sampling (ACS) is appropriate for rare clustered populations with localization tendencies. Up to now, it has been used exclusively for investigating spatial-scale problems rather than temporal-scale such as t his study is dealing with, i.e.sediment transport in rivers. Suspended sediment load is carried mostly during relatively short periods coincide with high flows otherwise negligible. In ACS, more samples from critical river stages can be taken with respect to the aggregation tendencies of sediment loads during transport; thus increasing the level of representativeness of samples. Adoption of ACS to this new area needs further verification and adaptation such as definition of the sampling unit, population frame, neighborhood relation, and threshold. In this study, several scenarios were defined for the purpose of evaluating the ACS in sediment estimation. Numerous sample sets were taken from intensive discharge-load records of Sg. Pangsun River, Malaysia. These sample sets are different with respect to initial sample size, neighborhood relation, and discharge threshold. Total suspended sediment loads were then estimated using modified Horvitz-Thompson method. The comparison made between the symmetric neighborhood relation and the forward method suggested in this study showed that the latter could be used instead of the former in sediment studies without losing the accuracy. The findings also suggested the flow duration curve is a useful tool for ranking initial samples in order to determine an optimum discharge threshold. Institute for Mathematical Research, Universiti Putra Malaysia 2010 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/12931/1/Adaptive%20Cluster%20Sampling%20for%20a%20Temporal-Scale%20Population.pdf Arabkhedri, Mahmood and Lai, Food See and Ibrahim, Noor Akma and Mohamad Kasim, Mohamad Roslan (2010) Adaptive cluster sampling for a temporal-scale population. Malaysian Journal of Mathematical Sciences, 4 (1). pp. 53-75. ISSN 1823-8343; ESSN: 2289-750X http://einspem.upm.edu.my/journal/fullpaper/vol4no1/4.%20Arabkhedri.pdf |
| spellingShingle | Arabkhedri, Mahmood Lai, Food See Ibrahim, Noor Akma Mohamad Kasim, Mohamad Roslan Adaptive cluster sampling for a temporal-scale population |
| title | Adaptive cluster sampling for a temporal-scale population |
| title_full | Adaptive cluster sampling for a temporal-scale population |
| title_fullStr | Adaptive cluster sampling for a temporal-scale population |
| title_full_unstemmed | Adaptive cluster sampling for a temporal-scale population |
| title_short | Adaptive cluster sampling for a temporal-scale population |
| title_sort | adaptive cluster sampling for a temporal scale population |
| url | http://psasir.upm.edu.my/id/eprint/12931/1/Adaptive%20Cluster%20Sampling%20for%20a%20Temporal-Scale%20Population.pdf |
| work_keys_str_mv | AT arabkhedrimahmood adaptiveclustersamplingforatemporalscalepopulation AT laifoodsee adaptiveclustersamplingforatemporalscalepopulation AT ibrahimnoorakma adaptiveclustersamplingforatemporalscalepopulation AT mohamadkasimmohamadroslan adaptiveclustersamplingforatemporalscalepopulation |