Improving Accuracy of River Flow Forecasting Using LSSVR with Gravitational Search Algorithm
River flow prediction is essential in many applications of water resources planning and management. In this paper, the accuracy of multivariate adaptive regression splines (MARS), model 5 regression tree (M5RT), and conventional multiple linear regression (CMLR) is compared with a hybrid least squar...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2017/2391621 |
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| _version_ | 1841524870843727872 |
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| author | Rana Muhammad Adnan Xiaohui Yuan Ozgur Kisi Rabia Anam |
| author_facet | Rana Muhammad Adnan Xiaohui Yuan Ozgur Kisi Rabia Anam |
| author_sort | Rana Muhammad Adnan |
| collection | DOAJ |
| description | River flow prediction is essential in many applications of water resources planning and management. In this paper, the accuracy of multivariate adaptive regression splines (MARS), model 5 regression tree (M5RT), and conventional multiple linear regression (CMLR) is compared with a hybrid least square support vector regression-gravitational search algorithm (HLGSA) in predicting monthly river flows. In the first part of the study, all three regression methods were compared with each other in predicting river flows of each basin. It was found that the HLGSA method performed better than the MARS, M5RT, and CMLR in river flow prediction. The effect of log transformation on prediction accuracy of the regression methods was also examined in the second part of the study. Log transformation of the river flow data significantly increased the prediction accuracy of all regression methods. It was also found that log HLGSA (LHLSGA) performed better than the other regression methods. In the third part of the study, the accuracy of the LHLGSA and HLGSA methods was examined in river flow estimation using nearby river flow data. On the basis of results of all applications, it was found that LHLGSA and HLGSA could be successfully used in prediction and estimation of river flow. |
| format | Article |
| id | doaj-art-78862258073a4921a3f9497baf9e8d58 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-78862258073a4921a3f9497baf9e8d582025-02-03T05:47:11ZengWileyAdvances in Meteorology1687-93091687-93172017-01-01201710.1155/2017/23916212391621Improving Accuracy of River Flow Forecasting Using LSSVR with Gravitational Search AlgorithmRana Muhammad Adnan0Xiaohui Yuan1Ozgur Kisi2Rabia Anam3School of Hydropower and Information Engineering, Huazhong University of Science & Technology, Wuhan 430074, ChinaSchool of Hydropower and Information Engineering, Huazhong University of Science & Technology, Wuhan 430074, ChinaCenter for Interdisciplinary Research, International Black Sea University, Tbilisi, GeorgiaFaculty of Agricultural Engineering & Technology, Department of Farm Machinery & Power, University of Agriculture, Faisalabad, PakistanRiver flow prediction is essential in many applications of water resources planning and management. In this paper, the accuracy of multivariate adaptive regression splines (MARS), model 5 regression tree (M5RT), and conventional multiple linear regression (CMLR) is compared with a hybrid least square support vector regression-gravitational search algorithm (HLGSA) in predicting monthly river flows. In the first part of the study, all three regression methods were compared with each other in predicting river flows of each basin. It was found that the HLGSA method performed better than the MARS, M5RT, and CMLR in river flow prediction. The effect of log transformation on prediction accuracy of the regression methods was also examined in the second part of the study. Log transformation of the river flow data significantly increased the prediction accuracy of all regression methods. It was also found that log HLGSA (LHLSGA) performed better than the other regression methods. In the third part of the study, the accuracy of the LHLGSA and HLGSA methods was examined in river flow estimation using nearby river flow data. On the basis of results of all applications, it was found that LHLGSA and HLGSA could be successfully used in prediction and estimation of river flow.http://dx.doi.org/10.1155/2017/2391621 |
| spellingShingle | Rana Muhammad Adnan Xiaohui Yuan Ozgur Kisi Rabia Anam Improving Accuracy of River Flow Forecasting Using LSSVR with Gravitational Search Algorithm Advances in Meteorology |
| title | Improving Accuracy of River Flow Forecasting Using LSSVR with Gravitational Search Algorithm |
| title_full | Improving Accuracy of River Flow Forecasting Using LSSVR with Gravitational Search Algorithm |
| title_fullStr | Improving Accuracy of River Flow Forecasting Using LSSVR with Gravitational Search Algorithm |
| title_full_unstemmed | Improving Accuracy of River Flow Forecasting Using LSSVR with Gravitational Search Algorithm |
| title_short | Improving Accuracy of River Flow Forecasting Using LSSVR with Gravitational Search Algorithm |
| title_sort | improving accuracy of river flow forecasting using lssvr with gravitational search algorithm |
| url | http://dx.doi.org/10.1155/2017/2391621 |
| work_keys_str_mv | AT ranamuhammadadnan improvingaccuracyofriverflowforecastingusinglssvrwithgravitationalsearchalgorithm AT xiaohuiyuan improvingaccuracyofriverflowforecastingusinglssvrwithgravitationalsearchalgorithm AT ozgurkisi improvingaccuracyofriverflowforecastingusinglssvrwithgravitationalsearchalgorithm AT rabiaanam improvingaccuracyofriverflowforecastingusinglssvrwithgravitationalsearchalgorithm |