Evidence map›Paper›PMID 39905008›Full record

ArticleScientific reports2025

Prediction of cold region dew volume based on an ECOA-BiTCN-BiLSTM hybrid model.

Yi Zhang, Pengtao Liu, Yingying Xu, Meng Zhang

Abstract read
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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.

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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

The trial behind it

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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

4 authors.

Yi ZhangCollege of Electrical and Computer Science, Jilin Jianzhu University, Changchun, China.
Pengtao LiuCollege of Electrical and Computer Science, Jilin Jianzhu University, Changchun, China.
Yingying XuCollege of Electrical and Computer Science, Jilin Jianzhu University, Changchun, China.
Meng ZhangKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, People's Republic of China. mengzhang313@gmail.com.

Funding

National Nature Science Foundation of China 42175140the Fundamental Research Funds for the Central Universities，JLU 2024the fund of the Science and Technology Development Project of Jilin Province No. 20220203190SF.
6 · The paper itself

Abstract

This paper presents a hybrid prediction model, ECOA-BiTCN-BiLSTM, for predicting dew in cold areas. The model integrates BiTCN and BiLSTM neural networks to enhance performance. An enhanced Crayfish optimization algorithm (ECOA) with four mixed strategies was employed to optimize the model's hyperparameters and reduce the impact of arbitrary selection. The proposed ECOA-BiTCN-BiLSTM model was validated using dew data from farmland in a northeastern Chinese city. Comparative experiments were conducted against the BiTCN model, the BiLSTM model, the original BiTCN-BiLSTM model, and other models optimized with advanced swarm intelligence algorithms. The experimental results demonstrate that the proposed model achieved a mean absolute error (MAE) of 0.002424, a root mean square error (RMSE) of 0.003984, and a mean absolute percentage error (MAPE) of 0.123050, with a coefficient of determination R

Indexed as

BiTCN-BiLSTM modelDew volume predictionEnhanced crayfish optimization algorithmHyperparameter optimizationSwarm intelligence optimization algorithm

Identifiers

PMID39905008
PMCPMC11794478

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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.