Evidence map›Paper›PMID 39533063›Full record

ArticleScientific reports2024

Medium and long-term regional water demand prediction using Harris hawks optimisation-backpropagation neural network model.

Mengzhuo Yang, Erkun Gao, Gaoxu Wang, Daiyuan Li, Wenqi Zhou, Xingchi Zhou

Abstract read
In one paragraph

Article in Scientific reports, 2024. 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

6 authors.

Mengzhuo YangHydrology and Water Resources Department, Nanjing Hydraulic Research Institute, Nanjing, 210029, China. yangmengzhuo0927@hhu.edu.cn.
Erkun GaoHydrology and Water Resources Department, Nanjing Hydraulic Research Institute, Nanjing, 210029, China.
Gaoxu WangHydrology and Water Resources Department, Nanjing Hydraulic Research Institute, Nanjing, 210029, China. gxwang@nhri.cn.
Daiyuan LiHydrology and Water Resources Department, Nanjing Hydraulic Research Institute, Nanjing, 210029, China.
Wenqi ZhouHydrology and Water Resources Department, Nanjing Hydraulic Research Institute, Nanjing, 210029, China.
Xingchi ZhouHydrology and Water Resources Department, Nanjing Hydraulic Research Institute, Nanjing, 210029, China.

Funding

National Key Research and Development Program of China 2022YFC3204601National Natural Sciences Foundation of China 42075191Provincial and Municipal Integration Project of Yunnan Province 202202AH210007Water Technology Innovation Project of Guangdong Province 2021-07
6 · The paper itself

Abstract

Precise water demand prediction is essential for the efficient allocation and rational utilisation of regional water resources. This study addressed the challenge associated with medium and long-term water demand prediction by introducing a novel coupled model, HHO-BPNN (Harris Hawks Optimisation-Backpropagation Neural Network). Principal component analysis was employed to reduce the dimensionality of potential water demand factors. The performance of the forecasting models was compared through mean square error (MSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R

Indexed as

Backpropagation neural networkHarris hawks optimisation algorithmRegional water demand prediction

Identifiers

PMID39533063
PMCPMC11557846

What OpenQuestion holds

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Registered trials

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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.