Prediction Model of Water Demand for Scouring Siltation in Coastal River Networks Based on APSO and SVM: A Case Study of Doulong Port
Siltation in coastal river networks is a serious water security issue, and balancing siltation through water diversion is an important measure in ecological water resource dispatching. Taking the Doulong Port in the Lixiahe region of Jiangsu Province as the research subject, this paper redefined the...
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Editorial Office of Pearl River
2024-12-01
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| Series: | Renmin Zhujiang |
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| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.12.012 |
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| author | MA Zhutong XIANG Long YAN Ke |
| author_facet | MA Zhutong XIANG Long YAN Ke |
| author_sort | MA Zhutong |
| collection | DOAJ |
| description | Siltation in coastal river networks is a serious water security issue, and balancing siltation through water diversion is an important measure in ecological water resource dispatching. Taking the Doulong Port in the Lixiahe region of Jiangsu Province as the research subject, this paper redefined the water demand for scouring siltation based on the characteristic of scouring siltation in the area, so as to reflect the water demand for the net change in sediment monitored. By analyzing the in-situ monitoring data for the downstream channel of Doulong Port from 2001 to 2018, the paper extracted key variables such as initial riverbed volume, siltation amount, time duration, rainfall volume, and the frequency of sluice openings. A predictive model of water demand for scouring siltation was constructed, which combined adaptive particle swarm optimization (APSO) algorithm with support vector machine (SVM) and optimized the model parameters of the SVM through the APSO algorithm, enhancing the prediction accuracy of the APSO-SVM model. Furthermore, Sobol sensitivity analysis indicates that siltation amount, initial riverbed volume, and the frequency of sluice openings are the main variables affecting the water demand for scouring siltation, while rainfall volume and time duration show significant interactive effects. This study provides a theoretical basis and practical guidance for water resource management and channel maintenance, contributing to the improvement of water diversion efficiency in sea-entry channels and channel safety. |
| format | Article |
| id | doaj-art-ee7412ee94044202ad2083ea2cfb13aa |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-ee7412ee94044202ad2083ea2cfb13aa2025-01-15T03:08:36ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352024-12-014511412162664898Prediction Model of Water Demand for Scouring Siltation in Coastal River Networks Based on APSO and SVM: A Case Study of Doulong PortMA ZhutongXIANG LongYAN KeSiltation in coastal river networks is a serious water security issue, and balancing siltation through water diversion is an important measure in ecological water resource dispatching. Taking the Doulong Port in the Lixiahe region of Jiangsu Province as the research subject, this paper redefined the water demand for scouring siltation based on the characteristic of scouring siltation in the area, so as to reflect the water demand for the net change in sediment monitored. By analyzing the in-situ monitoring data for the downstream channel of Doulong Port from 2001 to 2018, the paper extracted key variables such as initial riverbed volume, siltation amount, time duration, rainfall volume, and the frequency of sluice openings. A predictive model of water demand for scouring siltation was constructed, which combined adaptive particle swarm optimization (APSO) algorithm with support vector machine (SVM) and optimized the model parameters of the SVM through the APSO algorithm, enhancing the prediction accuracy of the APSO-SVM model. Furthermore, Sobol sensitivity analysis indicates that siltation amount, initial riverbed volume, and the frequency of sluice openings are the main variables affecting the water demand for scouring siltation, while rainfall volume and time duration show significant interactive effects. This study provides a theoretical basis and practical guidance for water resource management and channel maintenance, contributing to the improvement of water diversion efficiency in sea-entry channels and channel safety.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.12.012water diversion for scouring siltationwater demand for scouring siltationsupport vector machineadaptive particle swarm optimizationsensitivity analysis |
| spellingShingle | MA Zhutong XIANG Long YAN Ke Prediction Model of Water Demand for Scouring Siltation in Coastal River Networks Based on APSO and SVM: A Case Study of Doulong Port Renmin Zhujiang water diversion for scouring siltation water demand for scouring siltation support vector machine adaptive particle swarm optimization sensitivity analysis |
| title | Prediction Model of Water Demand for Scouring Siltation in Coastal River Networks Based on APSO and SVM: A Case Study of Doulong Port |
| title_full | Prediction Model of Water Demand for Scouring Siltation in Coastal River Networks Based on APSO and SVM: A Case Study of Doulong Port |
| title_fullStr | Prediction Model of Water Demand for Scouring Siltation in Coastal River Networks Based on APSO and SVM: A Case Study of Doulong Port |
| title_full_unstemmed | Prediction Model of Water Demand for Scouring Siltation in Coastal River Networks Based on APSO and SVM: A Case Study of Doulong Port |
| title_short | Prediction Model of Water Demand for Scouring Siltation in Coastal River Networks Based on APSO and SVM: A Case Study of Doulong Port |
| title_sort | prediction model of water demand for scouring siltation in coastal river networks based on apso and svm a case study of doulong port |
| topic | water diversion for scouring siltation water demand for scouring siltation support vector machine adaptive particle swarm optimization sensitivity analysis |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.12.012 |
| work_keys_str_mv | AT mazhutong predictionmodelofwaterdemandforscouringsiltationincoastalrivernetworksbasedonapsoandsvmacasestudyofdoulongport AT xianglong predictionmodelofwaterdemandforscouringsiltationincoastalrivernetworksbasedonapsoandsvmacasestudyofdoulongport AT yanke predictionmodelofwaterdemandforscouringsiltationincoastalrivernetworksbasedonapsoandsvmacasestudyofdoulongport |