Nattapon Jaroenchai , Shaowen Wang , Lawrence V. Stanislawski , Ethan Shavers , Zhe Jiang , Vasit Sagan , E. Lynn Usery
{"title":"Transfer learning with convolutional neural networks for hydrological streamline delineation","authors":"Nattapon Jaroenchai , Shaowen Wang , Lawrence V. Stanislawski , Ethan Shavers , Zhe Jiang , Vasit Sagan , E. Lynn Usery","doi":"10.1016/j.envsoft.2024.106165","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"181 ","pages":"Article 106165"},"PeriodicalIF":4.8000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Modelling & Software","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1364815224002263","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
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.
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.