A knowledge-based method for the automatic determination of hydrological model structures

To determine a suitable hydrological model structure for a specific application context using integrated modelling frameworks, modellers usually need to manually select the required hydrological processes, identify the appropriate algorithm for each process, and couple the algorithms’ software components. However, these modelling steps are difficult and require corresponding knowledge. It is not easy for modellers to master all of the required knowledge. To alleviate this problem, a knowledge-based method is proposed to automatically determine hydrological model structures. First, modelling knowledge for process selection, algorithm identification, and component coupling is formalized in the formats of the Rule Markup Language (RuleML) and Resource Description Framework (RDF). Second, the formalized knowledge is applied to an inference engine to determine model structures. The method is applied to three hypothetical experiments and a real experiment. These experiments show how the knowledge-based method could support modellers in determining suitable model structures. The proposed method has the potential to reduce the knowledge burden on modellers and would be conducive to the promotion of integrated modelling frameworks. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY-NC-ND 4.0), which permits copying and redistribution for non-commercial purposes with no derivatives, provided the original work is properly cited (http://creativecommons.org/ licenses/by-nc-nd/4.0/). doi: 10.2166/hydro.2019.029 s://iwaponline.com/jh/article-pdf/21/6/1163/623411/jh0211163.pdf Jingchao Jiang Smart City Research Center, School of Automation, Hangzhou Dianzi University, Hangzhou 310012, China A-Xing Zhu Department of Geography, University of Wisconsin–Madison, Madison, WI 53706, USA Cheng-Zhi Qin (corresponding author) State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China E-mail: qincz@lreis.ac.cn Junzhi Liu Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China

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