Efficient Priority-Flood depression filling in raster digital elevation models
Depressions in raster digital elevation models (DEM) present a challenge for extracting hydrological networks. They are commonly filled before subsequent algorithms are further applied. Among existing algorithms for filling depressions, the Priority-Flood algorithm runs the fastest. In this study, w...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2019-04-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17538947.2018.1429503 |
| _version_ | 1827811180861718528 |
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| author | Hongqiang Wei Guiyun Zhou Suhua Fu |
| author_facet | Hongqiang Wei Guiyun Zhou Suhua Fu |
| author_sort | Hongqiang Wei |
| collection | DOAJ |
| description | Depressions in raster digital elevation models (DEM) present a challenge for extracting hydrological networks. They are commonly filled before subsequent algorithms are further applied. Among existing algorithms for filling depressions, the Priority-Flood algorithm runs the fastest. In this study, we propose an improved variant over the fastest existing sequential variant of the Priority-Flood algorithm for filling depressions in floating-point DEMs. The proposed variant introduces a series of improvements and greatly reduces the number of cells that need to be processed by the priority queue (PQ), the key data structure used in the algorithm. The proposed variant is evaluated based on statistics from 30 experiments. On average, our proposed variant reduces the number of cells processed by the PQ by around 70%. The speed-up ratios of our proposed variant over the existing fastest variant of the Priority-Flood algorithm range from 31% to 52%, with an average of 45%. The proposed variant can be used to fill depressions in large DEMs in much less time and in the parallel implementation of the Priority-Flood algorithm to further reduce the running time for processing huge DEMs that cannot be dealt with easily on single computers. |
| first_indexed | 2024-03-11T23:01:33Z |
| format | Article |
| id | doaj.art-601576e27de44e9c9ecb7907b0628292 |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T23:01:33Z |
| publishDate | 2019-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-601576e27de44e9c9ecb7907b06282922023-09-21T14:38:06ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552019-04-0112441542710.1080/17538947.2018.14295031429503Efficient Priority-Flood depression filling in raster digital elevation modelsHongqiang Wei0Guiyun Zhou1Suhua Fu2University of Electronic Science and Technology of ChinaUniversity of Electronic Science and Technology of ChinaInstitute of Soil and Water Conservation, Chinese Academy of SciencesDepressions in raster digital elevation models (DEM) present a challenge for extracting hydrological networks. They are commonly filled before subsequent algorithms are further applied. Among existing algorithms for filling depressions, the Priority-Flood algorithm runs the fastest. In this study, we propose an improved variant over the fastest existing sequential variant of the Priority-Flood algorithm for filling depressions in floating-point DEMs. The proposed variant introduces a series of improvements and greatly reduces the number of cells that need to be processed by the priority queue (PQ), the key data structure used in the algorithm. The proposed variant is evaluated based on statistics from 30 experiments. On average, our proposed variant reduces the number of cells processed by the PQ by around 70%. The speed-up ratios of our proposed variant over the existing fastest variant of the Priority-Flood algorithm range from 31% to 52%, with an average of 45%. The proposed variant can be used to fill depressions in large DEMs in much less time and in the parallel implementation of the Priority-Flood algorithm to further reduce the running time for processing huge DEMs that cannot be dealt with easily on single computers.http://dx.doi.org/10.1080/17538947.2018.1429503depression fillingdemdrainage network |
| spellingShingle | Hongqiang Wei Guiyun Zhou Suhua Fu Efficient Priority-Flood depression filling in raster digital elevation models International Journal of Digital Earth depression filling dem drainage network |
| title | Efficient Priority-Flood depression filling in raster digital elevation models |
| title_full | Efficient Priority-Flood depression filling in raster digital elevation models |
| title_fullStr | Efficient Priority-Flood depression filling in raster digital elevation models |
| title_full_unstemmed | Efficient Priority-Flood depression filling in raster digital elevation models |
| title_short | Efficient Priority-Flood depression filling in raster digital elevation models |
| title_sort | efficient priority flood depression filling in raster digital elevation models |
| topic | depression filling dem drainage network |
| url | http://dx.doi.org/10.1080/17538947.2018.1429503 |
| work_keys_str_mv | AT hongqiangwei efficientpriorityflooddepressionfillinginrasterdigitalelevationmodels AT guiyunzhou efficientpriorityflooddepressionfillinginrasterdigitalelevationmodels AT suhuafu efficientpriorityflooddepressionfillinginrasterdigitalelevationmodels |