Toward the reliable prediction of reservoir landslide displacement using earthworm optimization algorithm-optimized support vector regression (EOA-SVR)
Reliable prediction of reservoir displacement is essential for practical applications. Machine learning offers an attractive and accessible set of tools for the displacement prediction of reservoir landslides. In the present study, earthworm optimization algorithm-optimized support vector regression...
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| Veröffentlicht in: | Natural hazards (Dordrecht) 2024-03, Vol.120 (4), p.3165-3188 |
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| creator | Liu, Zhiyang Ma, Junwei Xia, Ding Jiang, Sheng Ren, Zhiyuan Tan, Chunhai Lei, Dongze Guo, Haixiang |
| description | Reliable prediction of reservoir displacement is essential for practical applications. Machine learning offers an attractive and accessible set of tools for the displacement prediction of reservoir landslides. In the present study, earthworm optimization algorithm-optimized support vector regression (EOA-SVR) was proposed for the reliable prediction of reservoir landslide displacement. The proposed approach was evaluated and compared with metaheuristics, including artificial bee colony (ABC), biogeography-based optimization (BBO), genetic algorithm (GA), gray wolf optimization (GWO), particle swarm optimization (PSO), and water cycle algorithm (WCA), by the Friedman and post hoc Nemenyi tests. The results from the Baishuihe landslide showed that the EOA-optimized SVR provided satisfactory performance with a Kling–Gupta efficiency (KGE) greater than 0.98 and nearly optimal values of the coefficient of determination. Significant performance differences were revealed between the compared metaheuristics. The EOA is superior with respect to both performance and stability. The hyperparameter sensitivity analysis demonstrated that the EOA can stably provide reliable predictions by maintaining the optimal solution. The experimental results from the Baishuihe landslide indicate that the EOA-optimized SVR is promising for accurate and reliable prediction of reservoir landslide displacements, thus aiding in medium- and long-term landslide early warning. |
| doi_str_mv | 10.1007/s11069-023-06322-1 |
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Machine learning offers an attractive and accessible set of tools for the displacement prediction of reservoir landslides. In the present study, earthworm optimization algorithm-optimized support vector regression (EOA-SVR) was proposed for the reliable prediction of reservoir landslide displacement. The proposed approach was evaluated and compared with metaheuristics, including artificial bee colony (ABC), biogeography-based optimization (BBO), genetic algorithm (GA), gray wolf optimization (GWO), particle swarm optimization (PSO), and water cycle algorithm (WCA), by the Friedman and post hoc Nemenyi tests. The results from the Baishuihe landslide showed that the EOA-optimized SVR provided satisfactory performance with a Kling–Gupta efficiency (KGE) greater than 0.98 and nearly optimal values of the coefficient of determination. Significant performance differences were revealed between the compared metaheuristics. The EOA is superior with respect to both performance and stability. The hyperparameter sensitivity analysis demonstrated that the EOA can stably provide reliable predictions by maintaining the optimal solution. The experimental results from the Baishuihe landslide indicate that the EOA-optimized SVR is promising for accurate and reliable prediction of reservoir landslide displacements, thus aiding in medium- and long-term landslide early warning.</description><identifier>ISSN: 0921-030X</identifier><identifier>EISSN: 1573-0840</identifier><identifier>DOI: 10.1007/s11069-023-06322-1</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Algorithms ; Biogeography ; Civil Engineering ; Earth and Environmental Science ; Earth Sciences ; Environmental Management ; Genetic algorithms ; Geophysics/Geodesy ; Geotechnical Engineering & Applied Earth Sciences ; Heuristic methods ; Hydrogeology ; Hydrologic cycle ; Hydrological cycle ; Landslide warnings ; Landslides ; Landslides & mudslides ; Machine learning ; Natural Hazards ; Optimization algorithms ; Original Paper ; Particle swarm optimization ; Predictions ; Reservoirs ; Sensitivity analysis ; Stability analysis ; Support vector machines ; Swarm intelligence ; Worms</subject><ispartof>Natural hazards (Dordrecht), 2024-03, Vol.120 (4), p.3165-3188</ispartof><rights>The Author(s), under exclusive licence to Springer Nature B.V. 2023. 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Machine learning offers an attractive and accessible set of tools for the displacement prediction of reservoir landslides. In the present study, earthworm optimization algorithm-optimized support vector regression (EOA-SVR) was proposed for the reliable prediction of reservoir landslide displacement. The proposed approach was evaluated and compared with metaheuristics, including artificial bee colony (ABC), biogeography-based optimization (BBO), genetic algorithm (GA), gray wolf optimization (GWO), particle swarm optimization (PSO), and water cycle algorithm (WCA), by the Friedman and post hoc Nemenyi tests. The results from the Baishuihe landslide showed that the EOA-optimized SVR provided satisfactory performance with a Kling–Gupta efficiency (KGE) greater than 0.98 and nearly optimal values of the coefficient of determination. Significant performance differences were revealed between the compared metaheuristics. The EOA is superior with respect to both performance and stability. The hyperparameter sensitivity analysis demonstrated that the EOA can stably provide reliable predictions by maintaining the optimal solution. The experimental results from the Baishuihe landslide indicate that the EOA-optimized SVR is promising for accurate and reliable prediction of reservoir landslide displacements, thus aiding in medium- and long-term landslide early warning.</description><subject>Algorithms</subject><subject>Biogeography</subject><subject>Civil Engineering</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Environmental Management</subject><subject>Genetic algorithms</subject><subject>Geophysics/Geodesy</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>Heuristic methods</subject><subject>Hydrogeology</subject><subject>Hydrologic cycle</subject><subject>Hydrological cycle</subject><subject>Landslide warnings</subject><subject>Landslides</subject><subject>Landslides & mudslides</subject><subject>Machine learning</subject><subject>Natural Hazards</subject><subject>Optimization algorithms</subject><subject>Original Paper</subject><subject>Particle swarm optimization</subject><subject>Predictions</subject><subject>Reservoirs</subject><subject>Sensitivity analysis</subject><subject>Stability analysis</subject><subject>Support vector machines</subject><subject>Swarm intelligence</subject><subject>Worms</subject><issn>0921-030X</issn><issn>1573-0840</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kc1q3DAQx0VJoNtNX6AnQS_tQc3Isq3VMYSkKQQW0iTkJrT2eFfBttyRdkP7InndaLOB3nIaZvh_wPwY-yLhhwTQp1FKqI2AQgmoVVEI-YHNZKXzuijhiM3AFFKAgoeP7FOMjwBS1oWZsefb8OSo5WmDnLD3btUjnwhb3yQfRh66fI5Iu-CJ925sY-9b5K2PU-8aHHBMfBv9uOboKG2eAg08TMkP_p97DXD9OpBPm0G8nbHlcTtNgRLfYZMC5YJ17oh79beL5Zn4fX_z_YQdd66P-Pltztnd5cXt-ZW4Xv78dX52LRolTRLSlC3kB2jZmaJyqkbVaa07gNI1WprFQledKXVV4ap2utEL3agWcVVqYzSAmrOvh9yJwp8txmQfw5bGXGkLo5Uq9wlZVRxUDYUYCTs7kR8c_bUS7B6APQCwGYB9BWBlNqmDKWbxuEb6H_2O6wWcxYvT</recordid><startdate>20240301</startdate><enddate>20240301</enddate><creator>Liu, Zhiyang</creator><creator>Ma, Junwei</creator><creator>Xia, Ding</creator><creator>Jiang, Sheng</creator><creator>Ren, Zhiyuan</creator><creator>Tan, Chunhai</creator><creator>Lei, Dongze</creator><creator>Guo, Haixiang</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7TG</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0001-8408-2821</orcidid></search><sort><creationdate>20240301</creationdate><title>Toward the reliable prediction of reservoir landslide displacement using earthworm optimization algorithm-optimized support vector regression (EOA-SVR)</title><author>Liu, Zhiyang ; 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Machine learning offers an attractive and accessible set of tools for the displacement prediction of reservoir landslides. In the present study, earthworm optimization algorithm-optimized support vector regression (EOA-SVR) was proposed for the reliable prediction of reservoir landslide displacement. The proposed approach was evaluated and compared with metaheuristics, including artificial bee colony (ABC), biogeography-based optimization (BBO), genetic algorithm (GA), gray wolf optimization (GWO), particle swarm optimization (PSO), and water cycle algorithm (WCA), by the Friedman and post hoc Nemenyi tests. The results from the Baishuihe landslide showed that the EOA-optimized SVR provided satisfactory performance with a Kling–Gupta efficiency (KGE) greater than 0.98 and nearly optimal values of the coefficient of determination. Significant performance differences were revealed between the compared metaheuristics. The EOA is superior with respect to both performance and stability. The hyperparameter sensitivity analysis demonstrated that the EOA can stably provide reliable predictions by maintaining the optimal solution. The experimental results from the Baishuihe landslide indicate that the EOA-optimized SVR is promising for accurate and reliable prediction of reservoir landslide displacements, thus aiding in medium- and long-term landslide early warning.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s11069-023-06322-1</doi><tpages>24</tpages><orcidid>https://orcid.org/0000-0001-8408-2821</orcidid></addata></record> |
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| subjects | Algorithms Biogeography Civil Engineering Earth and Environmental Science Earth Sciences Environmental Management Genetic algorithms Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Heuristic methods Hydrogeology Hydrologic cycle Hydrological cycle Landslide warnings Landslides Landslides & mudslides Machine learning Natural Hazards Optimization algorithms Original Paper Particle swarm optimization Predictions Reservoirs Sensitivity analysis Stability analysis Support vector machines Swarm intelligence Worms |
| title | Toward the reliable prediction of reservoir landslide displacement using earthworm optimization algorithm-optimized support vector regression (EOA-SVR) |
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