Evidence map›Paper›PMID 42091946›Full record

ArticleScientific reports2026

Identifying the key environmental drivers of chlorophyll-a in the Danjiangkou reservoir using interpretable machine learning.

Wenxiu Sun, Wenqiang Wu, Xiaobo Liu, Wenqi Peng, Huaidong Zhou, Yifan Zhang, Xiao Guo

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Wenxiu SunNational Key Laboratory of Basin Water Cycle and Water Security, Institute of Water Resources and Hydropower Research, Beijing, 100038, China.
Wenqiang WuNational Key Laboratory of Basin Water Cycle and Water Security, Institute of Water Resources and Hydropower Research, Beijing, 100038, China. wwqemail@126.com.
Xiaobo LiuNational Key Laboratory of Basin Water Cycle and Water Security, Institute of Water Resources and Hydropower Research, Beijing, 100038, China.
Wenqi PengNational Key Laboratory of Basin Water Cycle and Water Security, Institute of Water Resources and Hydropower Research, Beijing, 100038, China.
Huaidong ZhouNational Key Laboratory of Basin Water Cycle and Water Security, Institute of Water Resources and Hydropower Research, Beijing, 100038, China.
Yifan ZhangNational Key Laboratory of Basin Water Cycle and Water Security, Institute of Water Resources and Hydropower Research, Beijing, 100038, China.
Xiao GuoNational Key Laboratory of Basin Water Cycle and Water Security, Institute of Water Resources and Hydropower Research, Beijing, 100038, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Algal blooms have become a critical environmental issue worldwide, with chlorophyll-a (Chl-a) serving as a key biological indicator for assessing bloom intensity and providing essential insights into bloom dynamics. Based on four field investigations conducted in the Danjiangkou Reservoir between 2023 and 2024, this study utilized data on Chl-a, nutrients (total nitrogen, total phosphorus, ammonia nitrogen, etc.), water temperature, and light intensity to develop a Chl-a concentration prediction model using the XGBoost algorithm. The Shapley Additive Explanations method was further employed to quantitatively evaluate the relative contributions of environmental variables to Chl-a in the Danjiangkou Reservoir area and the Hanjiang Reservoir area. The results showed that the XGBoost model achieved high accuracy in modeling Chl-a concentrations (R

Indexed as

ChlorophyllChlorophyll AEnvironmental MonitoringEutrophicationMachine LearningBoosting Machine Learning AlgorithmsChinaNitrogenPhosphorusTemperatureChlorophyllChlorophyll ANitrogenPhosphorusChlorophyll-aDanjiangkou reservoirEutrophicationMachine-learningShapley additive explanations

Identifiers

PMID42091946
PMCPMC13338429

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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.