Transfer learning with convolutional neural networks for hydrological streamline delineation

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Nattapon Jaroenchai , Shaowen Wang , Lawrence V. Stanislawski , Ethan Shavers , Zhe Jiang , Vasit Sagan , E. Lynn Usery
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引用次数: 0

Abstract

Hydrological streamline delineation is critical for effective environmental management, influencing agriculture sustainability, river dynamics, watershed planning, and more. This study develops a novel approach to combining transfer learning with convolutional neural networks that capitalize on ImageNet pre-trained models to improve the accuracy and transferability of streamline delineation. We evaluate the performance of eleven ImageNet pre-trained models and a baseline model using datasets from Rowan County, NC, and Covington River, VA in the USA. Our results demonstrate that when models are adapted to a new area, the fine-tuned ImageNet pre-trained model exhibits superior predictive accuracy, markedly higher than the models trained from scratch or those only fine-tuned on the same area. Moreover, the ImageNet model achieves better smoothness and connectivity between classified streamline channels. These findings underline the effectiveness of transfer learning in enhancing the delineation of hydrological streamlines across varied geographies, offering a scalable solution for accurate and efficient environmental modelling.

利用卷积神经网络进行水文流线划定的迁移学习
水文流线划定对于有效的环境管理至关重要,它影响着农业的可持续性、河流动态和流域规划。本研究开发了一种新方法,将迁移学习与卷积神经网络相结合,利用 ImageNet 预训练模型提高流线划定的准确性和可迁移性。我们使用来自美国北卡罗来纳州罗文县和弗吉尼亚州科文顿河的数据集,评估了 11 个 ImageNet 预训练模型和一个基线模型的性能。结果表明,当模型适应一个新区域时,经过微调的 ImageNet 预训练模型表现出卓越的预测准确性,明显高于从零开始训练的模型或仅在同一区域经过微调的模型。此外,ImageNet 模型在分类流线通道之间实现了更好的平滑性和连接性。这些发现强调了迁移学习在增强不同地域水文流线划分方面的有效性,为准确高效的环境建模提供了可扩展的解决方案。
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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