4.2 Article

Improved Spatial Outlier Detection Method Within a River Network

期刊

SAINS MALAYSIANA
卷 51, 期 3, 页码 911-927

出版社

UNIV KEBANGSAAN MALAYSIA, FAC SCIENCE & TECHNOLOGY
DOI: 10.17576/jsm-2022-5103-24

关键词

Euclidean distance; river distance; robust multivariate; spatial outlier; water quality

资金

  1. Universiti Malaya [RF015B-2018]
  2. Ministry of Higher Education Malaysia [FRGS/1/2018/STG06/UM/02/12]
  3. Department of Environment, Malaysia

向作者/读者索取更多资源

This study proposes a method for identifying spatial outliers in a river network by taking into account the effect of river flow connectivity. The proposed method, which uses river distance instead of Euclidean distance, is shown to have superior performance using a simulated river dataset. Its application on water quality data from the Sg. Klang Basin in Malaysia provides better identification of stations with significantly different water quality from their neighboring stations.
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

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