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
收藏
页数:24
相关论文
共 50 条
  • [31] Decomposing streamflow for improved river flow prediction accuracy of machine learning models
    Elkurdy, Mostafa
    Binns, Andrew
    Gharabaghi, Bahram
    INTERNATIONAL JOURNAL OF RIVER BASIN MANAGEMENT, 2025,
  • [32] Application of optimized machine learning techniques for prediction of occupational accidents
    Sarkar, Sobhan
    Vinay, Sammangi
    Raj, Rahul
    Maiti, J.
    Mitra, Pabitra
    COMPUTERS & OPERATIONS RESEARCH, 2019, 106 : 210 - 224
  • [33] Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models
    Kumar, Vijendra
    Kedam, Naresh
    Sharma, Kul Vaibhav
    Mehta, Darshan J.
    Caloiero, Tommaso
    WATER, 2023, 15 (14)
  • [34] Inconsistent Monthly Runoff Prediction Models Using Mutation Tests and Machine Learning
    Ren, Miaomiao
    Sun, Wei
    Chen, Shu
    Zeng, Decheng
    Xie, Yutong
    WATER RESOURCES MANAGEMENT, 2024, 38 (13) : 5235 - 5254
  • [35] Application of Machine Learning Models to Predict Maximum Event Water Fractions in Streamflow
    Sahraei, Amir
    Chamorro, Alejandro
    Kraft, Philipp
    Breuer, Lutz
    FRONTIERS IN WATER, 2021, 3
  • [36] Data-driven models for monthly streamflow time series prediction
    Wu, C. L.
    Chau, K. W.
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2010, 23 (08) : 1350 - 1367
  • [37] Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran
    Akbarian, Mohammad
    Saghafian, Bahram
    Golian, Saeed
    JOURNAL OF HYDROLOGY, 2023, 620
  • [38] Evaluation of optimized machine learning models for nuclear reactor accident prediction
    Racheal, Suubi
    Liu, Yongkuo
    Ayodeji, Abiodun
    PROGRESS IN NUCLEAR ENERGY, 2022, 149
  • [39] Application of machine learning ensemble models for rainfall prediction
    Ahmadi, Hasan
    Aminnejad, Babak
    Sabatsany, Hojat
    ACTA GEOPHYSICA, 2023, 71 (04) : 1775 - 1786
  • [40] Application of machine learning ensemble models for rainfall prediction
    Hasan Ahmadi
    Babak Aminnejad
    Hojat Sabatsany
    Acta Geophysica, 2023, 71 : 1775 - 1786