Predictive framework of vegetation resistance in channel flow.

Fengcong Jia, Weijie Wang, Yu Han, Jiayu Du, Yue Zhang, Zihan Liu, Hairong Gao

Abstract read

In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Fengcong JiaCollege of Water Resources and Civil Engineering, China Agricultural University, Beijing, 100083, China.

Weijie WangState Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, 100038, China.

Yu HanCollege of Water Resources and Civil Engineering, China Agricultural University, Beijing, 100083, China. yhan@cau.edu.cn.

Jiayu DuCollege of Water Resources and Civil Engineering, China Agricultural University, Beijing, 100083, China.

Yue ZhangCollege of Water Resources and Civil Engineering, China Agricultural University, Beijing, 100083, China.

Zihan LiuCollege of Water Resources and Civil Engineering, China Agricultural University, Beijing, 100083, China.

Hairong GaoCollege of Water Resources and Civil Engineering, China Agricultural University, Beijing, 100083, China.

Funding

Beijing Natural Science Foundation 8232052Bingtuan Science and Technology Program 2023AB060National Key Research and Development Program of China 2023YFD1900803

6 · The paper itself

Abstract

Predicting vegetation-induced flow resistance remains a significant challenge due to the diverse and dynamic nature of river vegetation. Although numerous empirical models are available, they often fail to generalize across different environmental conditions, leading to inaccurate predictions. This study introduces a machine learning-based framework for predicting vegetation flow resistance, incorporating nine ML methods, including SVM, XGBoost, and BP. To improve predictive performance, optimization algorithms such as PSO, WSO, and RIME were applied. A comprehensive dataset of 490 samples across multiple scales was used to evaluate model accuracy, indicated: (1) The submergence ratio α and Froude number F

Indexed as

PlantsRiversAlgorithmsEcosystemMachine LearningModels, TheoreticalCylinder arraysDimensional analysisDrag coefficientMachine learningVegetation flow resistance

Identifiers

PMID40038439

DOI10.1038/s41598-025-91668-8

PMCPMC11880560

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.