The runoff errors of RF-SVR and RF-MLPR at different prediction period.

The runoff errors of RF-SVR and RF-MLPR at different prediction period.

<p>The runoff errors of RF-SVR and RF-MLPR at different prediction period.</p>

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Bibliographic Details
Main Author:	Jintao Li (448681) (author)
Other Authors:	Ping Ai (9912567) (author), Chuansheng Xiong (20422027) (author), Yanhong Song (4516555) (author)
Published:	2024
Subjects:	Ecology Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified yalong river basin water resource scheduling similar hydrological characteristics providing crucial insights nonlinear relationships inherent narrow approach overlooks multilayer perceptron regression less reliable predictions integrated random forest deep learning structure atmospheric circulation indices individual runoff sequences term predictions due traditional statistical models single baseline models mlpr — due complicate forecasting efforts term runoff forecasts term runoff based water resource management mlpr model outperformed mlpr model achieved ylrb runoff exhibits teleconnection factors selection complex process influenced term runoff forecasting prediction accuracy decreased longer forecast periods globally relevant contribution div >< p mlpr model ’ flood control planning complex latent information mlpr handle nonlinearity long prediction periods 7 %– 6 2 </ sup term forecasting forecast periods runoff formation flood control proposed models climate prediction ylrb ), redundant information predictive accuracy framework ’ ecological management svr model month forecast influencing factors flood prevention anthropogenic factors various natural sutcliffe efficiency results demonstrate research lies redundant variables primarily focus particularly suitable month lag lianghekou station integrate multi increasing challenge greater uncertainty ecological restoration drought mitigation complementary strengths broader applicability
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