Improved spatial outlier detection method within a river network
A spatial outlier refers to the observation whose non-spatial attribute values are significantly different from those of its neighbors. Such observations can also be found in water quality data at monitoring stations within a river network. However, existing spatial outlier detection procedures base...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Penerbit Universiti Kebangsaan Malaysia
2022
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| Online Access: | http://journalarticle.ukm.my/19175/ http://journalarticle.ukm.my/19175/1/24.pdf |
| _version_ | 1848814769188896768 |
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| author | Nur Fatihah Mohd Ali, Rossita Mohamad Yunus, Ibrahim Mohamed, Faridah Othman, |
| author_facet | Nur Fatihah Mohd Ali, Rossita Mohamad Yunus, Ibrahim Mohamed, Faridah Othman, |
| author_sort | Nur Fatihah Mohd Ali, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | A spatial outlier refers to the observation whose non-spatial attribute values are significantly different from those of its neighbors. Such observations can also be found in water quality data at monitoring stations within a river network. However, existing spatial outlier detection procedures based on distance measures such as the Euclidean distance between monitoring stations do not take into account the river network topology. In general, water quality levels in lower streams will be affected by the flow from the upper streams. Similarly, the water quality at some tributaries may have little influence on the other tributaries. Hence, a method for identifying spatial outliers in a river network, taking into account the effect of river flow connectivity on the determination of the neighbors of the monitoring stations, is proposed. While the robust Mahalalobis distance is used in both methods, the proposed method uses river distance instead of the Euclidean distance. The performance of the proposed method is shown to be superior using a synthetic river dataset through simulation. For illustration, we apply the proposed method on the water quality data from Sg. Klang Basin in 2016 provided by the Department of Environment, Malaysia. The finding provides a better identification of the water quality in some stations that significantly differ from their neighbouring stations. Such information is useful for the authorities in their planning of the environmental monitoring of water quality in the areas. |
| first_indexed | 2025-11-15T00:39:21Z |
| format | Article |
| id | oai:generic.eprints.org:19175 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:39:21Z |
| publishDate | 2022 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:191752022-08-01T04:32:06Z http://journalarticle.ukm.my/19175/ Improved spatial outlier detection method within a river network Nur Fatihah Mohd Ali, Rossita Mohamad Yunus, Ibrahim Mohamed, Faridah Othman, A spatial outlier refers to the observation whose non-spatial attribute values are significantly different from those of its neighbors. Such observations can also be found in water quality data at monitoring stations within a river network. However, existing spatial outlier detection procedures based on distance measures such as the Euclidean distance between monitoring stations do not take into account the river network topology. In general, water quality levels in lower streams will be affected by the flow from the upper streams. Similarly, the water quality at some tributaries may have little influence on the other tributaries. Hence, a method for identifying spatial outliers in a river network, taking into account the effect of river flow connectivity on the determination of the neighbors of the monitoring stations, is proposed. While the robust Mahalalobis distance is used in both methods, the proposed method uses river distance instead of the Euclidean distance. The performance of the proposed method is shown to be superior using a synthetic river dataset through simulation. For illustration, we apply the proposed method on the water quality data from Sg. Klang Basin in 2016 provided by the Department of Environment, Malaysia. The finding provides a better identification of the water quality in some stations that significantly differ from their neighbouring stations. Such information is useful for the authorities in their planning of the environmental monitoring of water quality in the areas. Penerbit Universiti Kebangsaan Malaysia 2022-03 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/19175/1/24.pdf Nur Fatihah Mohd Ali, and Rossita Mohamad Yunus, and Ibrahim Mohamed, and Faridah Othman, (2022) Improved spatial outlier detection method within a river network. Sains Malaysiana, 51 (3). pp. 911-927. ISSN 0126-6039 https://www.ukm.my/jsm/malay_journals/jilid51bil3_2022/KandunganJilid51Bil3_2022.html |
| spellingShingle | Nur Fatihah Mohd Ali, Rossita Mohamad Yunus, Ibrahim Mohamed, Faridah Othman, Improved spatial outlier detection method within a river network |
| title | Improved spatial outlier detection method within a river network |
| title_full | Improved spatial outlier detection method within a river network |
| title_fullStr | Improved spatial outlier detection method within a river network |
| title_full_unstemmed | Improved spatial outlier detection method within a river network |
| title_short | Improved spatial outlier detection method within a river network |
| title_sort | improved spatial outlier detection method within a river network |
| url | http://journalarticle.ukm.my/19175/ http://journalarticle.ukm.my/19175/ http://journalarticle.ukm.my/19175/1/24.pdf |