Algae content prediction based on transfer learning and mean impact value

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-10-11 DOI:10.1016/j.chemolab.2024.105244

Haonan Zhang, Xiaojing Ping, Haiying Wan, Xiaoli Luan, Fei Liu

引用次数: 0

Abstract

To improve the prediction accuracies of algae contents in different water bodies, this paper proposes a chlorophyll-A prediction model method based on transfer learning(TL) and mean impact value(MIV) algorithm. Firstly, we preprocess the data collected from the Huai River, including removing the missing data and standardizing the preserved data. Then, the MIV algorithm is used to reduce the dimensionality of the data and determine the input variables of the model. Based on the selected input variables, the TL algorithm is introduced to establish the chlorophyll-A prediction model. The developed method can effectively enhance the prediction accuracy, especially when the number of samples is small. The simulation results verify the effectiveness and feasibility of the proposed prediction model.

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基于迁移学习和平均影响值的藻类含量预测

为了提高不同水体藻类含量的预测精度，本文提出了一种基于迁移学习（TL）和平均影响值（MIV）算法的叶绿素-A预测模型方法。首先，我们对从淮河采集的数据进行预处理，包括去除缺失数据和标准化保留数据。然后，使用 MIV 算法降低数据维度，确定模型的输入变量。在选定输入变量的基础上，引入 TL 算法建立叶绿素-A 预测模型。所开发的方法能有效提高预测精度，尤其是在样本数量较少时。仿真结果验证了所建预测模型的有效性和可行性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Chemometrics and Intelligent Laboratory Systems 工程技术-分析化学

CiteScore

7.50

自引率

7.70%

发文量

169

审稿时长

3.4 months

期刊介绍： Chemometrics and Intelligent Laboratory Systems publishes original research papers, short communications, reviews, tutorials and Original Software Publications reporting on development of novel statistical, mathematical, or computer techniques in Chemistry and related disciplines. Chemometrics is the chemical discipline that uses mathematical and statistical methods to design or select optimal procedures and experiments, and to provide maximum chemical information by analysing chemical data. The journal deals with the following topics: 1) Development of new statistical, mathematical and chemometrical methods for Chemistry and related fields (Environmental Chemistry, Biochemistry, Toxicology, System Biology, -Omics, etc.) 2) Novel applications of chemometrics to all branches of Chemistry and related fields (typical domains of interest are: process data analysis, experimental design, data mining, signal processing, supervised modelling, decision making, robust statistics, mixture analysis, multivariate calibration etc.) Routine applications of established chemometrical techniques will not be considered. 3) Development of new software that provides novel tools or truly advances the use of chemometrical methods. 4) Well characterized data sets to test performance for the new methods and software. The journal complies with International Committee of Medical Journal Editors'' Uniform requirements for manuscripts.