A runoff forecast model with selective ensemble method

Yi Tang; Hui Qin; Shuai Liu; Yuhua Qu; Zhiqiang Jiang; Zhengyang Tang; Keyan Shen

doi:10.1117/12.3004654

9 October 2023 A runoff forecast model with selective ensemble method

Yi Tang, Hui Qin, Shuai Liu, Yuhua Qu, Zhiqiang Jiang, Zhengyang Tang, Keyan Shen

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127912N (2023) https://doi.org/10.1117/12.3004654
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

Highly accurate runoff forecasting is essential for the efficient use of water resources. Considering the non-linearity and randomness of runoff sequences, a selective ensemble forecasting method combined with the PSO algorithm is proposed. Firstly, sub-learners are selected for homogeneous ensemble and the parameters of each sub-learner are rate-determined on the training set. Then, the PSO algorithm is used to assign weights based on the performance of the sub-learners on the validation set to obtain the selective ensemble model. Finally, the selective ensemble method was validated on the test set. Experiments were performed using runoff data from the WuLong station in the Yangtze River basin, and the results show that the selective ensemble method can provide more accurate forecast results than homogeneous ensemble with the same average weights of the learners.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yi Tang, Hui Qin, Shuai Liu, Yuhua Qu, Zhiqiang Jiang, Zhengyang Tang, and Keyan Shen "A runoff forecast model with selective ensemble method", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127912N (9 October 2023); https://doi.org/10.1117/12.3004654

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available