利用 VAE-WGAN-GP 模型缓解水质评估中的样本不平衡问题。

IF 2.5 4区 环境科学与生态学 Q3 ENGINEERING, ENVIRONMENTAL Water Science and Technology Pub Date : 2023-12-01 DOI:10.2166/wst.2023.373
Jingbin Xu, Degang Xu, Kun Wan, Ying Zhang
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引用次数: 0

摘要

水资源对于维持人类生活和促进可持续发展至关重要。然而,快速的城市化和工业化导致淡水供应量下降。有效预防和控制水污染对生态平衡和人类福祉至关重要。水质评估对于监测和管理水资源至关重要。现有的基于机器学习的评估方法倾向于将结果划分为多数类,由于实际场景中普遍存在类样本分布不平衡的问题,导致结果不准确。为解决这一问题,我们提出了一种利用 VAE-WGAN-GP 模型的新方法。VAE-WGAN-GP 模型将 VAE 的编码和解码机制与 GAN 的对抗学习相结合。它生成的合成样本与真实样本非常相似,能有效补偿水质评价中的稀缺类数据。我们的贡献包括:(1)引入了一种深度生成模型来缓解水质评估中类别样本不平衡的问题;(2)证明了所提出的 VAE-WGAN-GP 模型具有更快的收敛速度和更强的势分布学习能力;(3)引入了补偿度概念并进行了全面的补偿实验,使得多分类不平衡样本的水质评估准确率提高了 9.7%。
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Alleviating sample imbalance in water quality assessment using the VAE-WGAN-GP model.

Water resources are essential for sustaining human life and promoting sustainable development. However, rapid urbanization and industrialization have resulted in a decline in freshwater availability. Effective prevention and control of water pollution are essential for ecological balance and human well-being. Water quality assessment is crucial for monitoring and managing water resources. Existing machine learning-based assessment methods tend to classify the results into the majority class, leading to inaccuracies in the outcomes due to the prevalent issue of imbalanced class sample distribution in practical scenarios. To tackle the issue, we propose a novel approach that utilizes the VAE-WGAN-GP model. The VAE-WGAN-GP model combines the encoding and decoding mechanisms of VAE with the adversarial learning of GAN. It generates synthetic samples that closely resemble real samples, effectively compensating data of the scarcity category in water quality evaluation. Our contributions include (1) introducing a deep generative model to alleviate the issue of imbalanced category samples in water quality assessment, (2) demonstrating the faster convergence speed and improved potential distribution learning ability of the proposed VAE-WGAN-GP model, (3) introducing the compensation degree concept and conducting comprehensive compensation experiments, resulting in a 9.7% increase in the accuracy of water quality assessment for multi-classification imbalance samples.

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来源期刊
Water Science and Technology
Water Science and Technology 环境科学-工程:环境
CiteScore
4.90
自引率
3.70%
发文量
366
审稿时长
4.4 months
期刊介绍: Water Science and Technology publishes peer-reviewed papers on all aspects of the science and technology of water and wastewater. Papers are selected by a rigorous peer review procedure with the aim of rapid and wide dissemination of research results, development and application of new techniques, and related managerial and policy issues. Scientists, engineers, consultants, managers and policy-makers will find this journal essential as a permanent record of progress of research activities and their practical applications.
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