Intelligent determination of proper spatial extents for input data during geographical model workflow building

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Zi-Yue Chen , Cheng-Zhi Qin , Liang-Jun Zhu , Cheng-Long Wu , Ying-Chao Ren , A-Xing Zhu
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引用次数: 0

Abstract

The spatial extent required for geographical model inputs depends on the model and input data characteristics, often differing from the user-defined area of interest (AOI). For example, a DEM input for stream network extraction should cover the upstream catchment area of the AOI. Determining proper spatial extents is crucial for both modeling accuracy and efficiency but is often complex and tedious , especially for workflows which may raise chain effect on varying spatial extents among diverse inputs. Few methods currently address this issue. This paper proposes an intelligent approach to automate spatial extent determination during geographical model workflow building, adapting to the user-defined AOI. The approach combines knowledge rules and heuristic modeling with advanced geoprocessing. Implemented in a prototype system, a case study on digital soil mapping for arbitrary-shaped AOI was conducted to validate the effectiveness of the approach, showing that it provides users with easy-to-use and accurate geographical modeling across broad applications.
在地理模型工作流构建过程中,智能确定输入数据的适当空间范围
地理模型输入所需的空间范围取决于模型和输入数据特征,通常与用户定义的兴趣区域(AOI)不同。例如,用于提取河流网络的DEM输入应覆盖AOI的上游集水区。确定适当的空间范围对于建模的准确性和效率至关重要,但往往是复杂和繁琐的,特别是对于不同输入之间可能产生不同空间范围连锁效应的工作流。目前解决这个问题的方法很少。本文提出了一种在地理模型工作流构建过程中自动确定空间范围的智能方法,以适应用户自定义的AOI。该方法将知识规则和启发式建模与先进的地理处理相结合。在一个原型系统中,对任意形状AOI的数字土壤制图进行了案例研究,以验证该方法的有效性,表明它为用户提供了易于使用和准确的地理建模,适用于广泛的应用。
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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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