A novel approach for estimating the capacity of ungauged small reservoirs using remote sensing and DEM
The estimation of small reservoir capacity is of great significance for water resources management. However, many widely distributed small reservoirs lack the capacity information because of the high costs of field measurements. This study proposed a novel approach to estimate the small reservoir ca...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IWA Publishing
2022-07-01
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| Series: | Hydrology Research |
| Subjects: | |
| Online Access: | http://hr.iwaponline.com/content/53/7/1001 |
| Summary: | The estimation of small reservoir capacity is of great significance for water resources management. However, many widely distributed small reservoirs lack the capacity information because of the high costs of field measurements. This study proposed a novel approach to estimate the small reservoir capacity in the hilly area by using remote sensing and Digital Elevation Model (DEM). The basic idea of this approach is to explore the relationship between influential factors (i.e., topographic and geomorphic parameters) and measured reservoirs’ capacity to establish a machine learning model based on particle swarm optimization–extreme learning machine (PSO–ELM) to estimate the capacity. The Mihe River basin in northern China is selected as a case study, 111 measured reservoirs, and six optional influential factors are selected to develop and test this model. The results show that the five influential factors (i.e., the area of sub-catchment, the water surface area, the longest flow path of sub-catchment, the average slope of sub-catchment, and the average slope of buffer area) are the optimal combination with the lowest difference between the measured and the estimated reservoir capacities. The results demonstrate that the proposed approach is a robust tool for estimating the capacity of small reservoirs in the hilly area.
HIGHLIGHTS
A total of 123 small reservoirs are identified in the Mihe River basin above the Tanjiafang station by remote sensing and DEM.;
Five influential factors are selected from the spheres of topography and geomorphology to estimate the reservoir capacity.;
The estimation model based on particle swarm optimization–extreme learning machine (PSO–ELM) is a robust tool for estimating the small reservoir capacity in the hilly area.; |
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| ISSN: | 1998-9563 2224-7955 |