Application of genetic algorithm to land use optimization for non-point source pollution control based on CLUE-S and SWAT
•CLUE-S and SWAT were used to simulate land use change and its corresponding NPS pollution.•Genetic algorithm was better than Markov chain method to predict the land use quantity change.•The optimal methods should be used to predict the quantity and pattern of landuse in the future. The genetic algo...
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| Published in | Journal of hydrology (Amsterdam) Vol. 560; pp. 86 - 96 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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Elsevier B.V
01.05.2018
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| Abstract | •CLUE-S and SWAT were used to simulate land use change and its corresponding NPS pollution.•Genetic algorithm was better than Markov chain method to predict the land use quantity change.•The optimal methods should be used to predict the quantity and pattern of landuse in the future.
The genetic algorithm (GA) was combined with the Conversion of Land Use and its Effect at Small regional extent (CLUE-S) model to obtain an optimized land use pattern for controlling non-point source (NPS) pollution. The performance of the combination was evaluated. The effect of the optimized land use pattern on the NPS pollution control was estimated by the Soil and Water Assessment Tool (SWAT) model and an assistant map was drawn to support the land use plan for the future. The Xiangxi River watershed was selected as the study area. Two scenarios were used to simulate the land use change. Under the historical trend scenario (Markov chain prediction), the forest area decreased by 2035.06 ha, and was mainly converted into paddy and dryland area. In contrast, under the optimized scenario (genetic algorithm (GA) prediction), up to 3370 ha of dryland area was converted into forest area. Spatially, the conversion of paddy and dryland into forest occurred mainly in the northwest and southeast of the watershed, where the slope land occupied a large proportion. The organic and inorganic phosphorus loads decreased by 3.6% and 3.7%, respectively, in the optimized scenario compared to those in the historical trend scenario. GA showed a better performance in optimized land use prediction. A comparison of the land use patterns in 2010 under the real situation and in 2020 under the optimized situation showed that Shennongjia and Shuiyuesi should convert 1201.76 ha and 1115.33 ha of dryland into forest areas, respectively, which represented the greatest changes in all regions in the watershed. The results of this study indicated that GA and the CLUE-S model can be used to optimize the land use patterns in the future and that SWAT can be used to evaluate the effect of land use optimization on non-point source pollution control. These methods may provide support for land use plan of an area. |
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| AbstractList | •CLUE-S and SWAT were used to simulate land use change and its corresponding NPS pollution.•Genetic algorithm was better than Markov chain method to predict the land use quantity change.•The optimal methods should be used to predict the quantity and pattern of landuse in the future.
The genetic algorithm (GA) was combined with the Conversion of Land Use and its Effect at Small regional extent (CLUE-S) model to obtain an optimized land use pattern for controlling non-point source (NPS) pollution. The performance of the combination was evaluated. The effect of the optimized land use pattern on the NPS pollution control was estimated by the Soil and Water Assessment Tool (SWAT) model and an assistant map was drawn to support the land use plan for the future. The Xiangxi River watershed was selected as the study area. Two scenarios were used to simulate the land use change. Under the historical trend scenario (Markov chain prediction), the forest area decreased by 2035.06 ha, and was mainly converted into paddy and dryland area. In contrast, under the optimized scenario (genetic algorithm (GA) prediction), up to 3370 ha of dryland area was converted into forest area. Spatially, the conversion of paddy and dryland into forest occurred mainly in the northwest and southeast of the watershed, where the slope land occupied a large proportion. The organic and inorganic phosphorus loads decreased by 3.6% and 3.7%, respectively, in the optimized scenario compared to those in the historical trend scenario. GA showed a better performance in optimized land use prediction. A comparison of the land use patterns in 2010 under the real situation and in 2020 under the optimized situation showed that Shennongjia and Shuiyuesi should convert 1201.76 ha and 1115.33 ha of dryland into forest areas, respectively, which represented the greatest changes in all regions in the watershed. The results of this study indicated that GA and the CLUE-S model can be used to optimize the land use patterns in the future and that SWAT can be used to evaluate the effect of land use optimization on non-point source pollution control. These methods may provide support for land use plan of an area. |
| Author | Wang, Qingrui Guo, Lijia Liu, Ruimin Men, Cong |
| Author_xml | – sequence: 1 givenname: Qingrui surname: Wang fullname: Wang, Qingrui – sequence: 2 givenname: Ruimin surname: Liu fullname: Liu, Ruimin email: liurm@bnu.edu.cn – sequence: 3 givenname: Cong surname: Men fullname: Men, Cong – sequence: 4 givenname: Lijia surname: Guo fullname: Guo, Lijia |
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| Keywords | Markov chain Genetic algorithm Land use change CLUE-S model SWAT model Non-point source pollution Xiangxi watershed |
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| Snippet | •CLUE-S and SWAT were used to simulate land use change and its corresponding NPS pollution.•Genetic algorithm was better than Markov chain method to predict... |
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| SubjectTerms | CLUE-S model Genetic algorithm Land use change Markov chain Non-point source pollution SWAT model Xiangxi watershed |
| Title | Application of genetic algorithm to land use optimization for non-point source pollution control based on CLUE-S and SWAT |
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