Application of novel binary optimized machine learning models for monthly streamflow prediction

被引:31
|
作者
Adnan, Rana Muhammad [1 ]
Dai, Hong-Liang [1 ]
Mostafa, Reham R. [2 ]
Islam, Abu Reza Md. Towfiqul [3 ,8 ]
Kisi, Ozgur [4 ,5 ]
Elbeltagi, Ahmed [6 ]
Zounemat-Kermani, Mohammad [7 ]
机构
[1] Guangzhou Univ, Sch Econ & Stat, Guangzhou 510006, Peoples R China
[2] Mansoura Univ, Fac Comp & Informat Sci, Informat Syst Dept, Mansoura 35516, Egypt
[3] Begum Rokeya Univ, Dept Disaster Management, Rangpur 5400, Bangladesh
[4] Lubeck Univ Appl Sci, Dept Civil Engn, D-23562 Lubeck, Germany
[5] Ilia State Univ, Civil Engn Dept, Tbilisi 0162, Georgia
[6] Mansoura Univ, Fac Agr, Agr Engn Dept, Mansoura 35516, Egypt
[7] Shahid Bahonar Univ Kerman, Dept Water Engn, Kerman, Iran
[8] Daffodil Int Univ, Dept Dev Studies, Dhaka 1216, Bangladesh
关键词
Streamflow prediction; Extreme learning machine; Particle swarm optimization; Grey wolf optimization; Simulated annealing; PARTICLE SWARM OPTIMIZATION; WATER-RESOURCES; RIVER; PARAMETERS; ALGORITHM; SVM; ELM;
D O I
10.1007/s13201-023-01913-6
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
Accurate measurements of available water resources play a key role in achieving a sustainable environment of a society. Precise river flow estimation is an essential task for optimal use of hydropower generation, flood forecasting, and best utilization of water resources in river engineering. The current paper presents the development and verification of the prediction abilities of new hybrid extreme learning machine (ELM)-based models coupling with metaheuristic methods, e.g., Particle swarm optimization (PSO), Mayfly optimization algorithm (MOA), Grey wolf optimization (GWO), and simulated annealing (SA) for monthly streamflow prediction. Prediction precision of standalone ELM model was compared with two-phase optimized state-of-the-arts models, e.g., ELM-PSO, ELM-MOA, ELM-PSOGWO, and ELM-SAMOA, respectively. Hydro-meteorological data acquired from Gorai and Padma Hardinge Bridge stations at Padma River Basin, northwestern Bangladesh, were utilized as inputs in this study to employ models in the form of seven different input combinations. The model's performances are appraised using Nash-Sutcliffe efficiency, root-mean-square-error (RMSE), mean absolute error, mean absolute percentage error and determination coefficient. The tested results of both stations reported that the ELM-SAMOA and ELM-PSOGWO models offered the best accuracy in the prediction of monthly streamflows compared to ELM-PSO, ELM-MOA, and ELM models. Based on the local data, the ELM-SAMOA reduced the RMSE of ELM, ELM-PSO, ELM-MOA, and ELM-PSOGWO by 31%, 27%, 19%, and 14% for the Gorai station and by 29%, 27%, 19%, and 14% for Padma Hardinge bridge station, in the testing stage, respectively. In contrast, based on external data, ELM-PSOGWO improves in RMSE of ELM, ELM-PSO, ELM-MOA, and ELM-SAMOA by 20%, 5.1%, 6.2%, and 4.6% in the testing stage, respectively. The results confirmed the superiority of two-phase optimized ELM-SAMOA and ELM-PSOGWO models over a single ELM model. The overall results suggest that ELM-SAMOA and ELM-PSOGWO models can be successfully applied in modeling monthly streamflow prediction with either local or external hydro-meteorological datasets.
引用
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页数:24
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