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From Manual to Intelligent: A Review of Input Data Preparation Methods for Geographic Modeling
Hou, Zhi-Wei1,2; Qin, Cheng-Zhi1,2,3; Zhu, A-Xing1,2,3,4,5; Liang, Peng1,2; Wang, Yi-Jie1,2; Zhu, Yun-Qiang1,2,3
2019-09-01
Source PublicationISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
Volume8Issue:9Pages:16
Corresponding AuthorQin, Cheng-Zhi(qincz@lreis.ac.cn)
AbstractOne of the key concerns in geographic modeling is the preparation of input data that are sufficient and appropriate for models. This requires considerable time, effort, and expertise since geographic models and their application contexts are complex and diverse. Moreover, both data and data pre-processing tools are multi-source, heterogeneous, and sometimes unavailable for a specific application context. The traditional method of manually preparing input data cannot effectively support geographic modeling, especially for complex integrated models and non-expert users. Therefore, effective methods are urgently needed that are not only able to prepare appropriate input data for models but are also easy to use. In this review paper, we first analyze the factors that influence data preparation and discuss the three corresponding key tasks that should be accomplished when developing input data preparation methods for geographic models. Then, existing input data preparation methods for geographic models are discussed through classifying into three categories: manual, (semi-)automatic, and intelligent (i.e., not only (semi-)automatic but also adaptive to application context) methods. Supported by the adoption of knowledge representation and reasoning techniques, the state-of-the-art methods in this field point to intelligent input data preparation for geographic models, which includes knowledge-supported discovery and chaining of data pre-processing functionalities, knowledge-driven (semi-)automatic workflow building (or service composition in the context of geographic web services) of data preprocessing, and artificial intelligent planning-based service composition as well as their parameter-settings. Lastly, we discuss the challenges and future research directions from the following aspects: Sharing and reusing of model data and workflows, integration of data discovery and processing functionalities, task-oriented input data preparation methods, and construction of knowledge bases for geographic modeling, all assisting with the development of an easy-to-use geographic modeling environment with intelligent input data preparation.
Keywordgeographic modeling input data preparation intelligent geoprocessing service composition
DOI10.3390/ijgi8090376
WOS KeywordWEB SERVICES ; SEMANTIC WEB ; AUTOMATIC COMPOSITION ; ENVIRONMENTAL-MODELS ; DATA VISUALIZATION ; SOFTWARE PACKAGE ; SWAT MODEL ; GIS ; WATER ; FRAMEWORK
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41431177] ; National Natural Science Foundation of China[41422109] ; Innovation Project of LREIS[O88RA20CYA] ; Vilas Associate Award ; Hammel Faculty Fellow Award ; Manasse Chair Professorship from the University of Wisconsin-Madison
Funding OrganizationNational Natural Science Foundation of China ; Innovation Project of LREIS ; Vilas Associate Award ; Hammel Faculty Fellow Award ; Manasse Chair Professorship from the University of Wisconsin-Madison
WOS Research AreaPhysical Geography ; Remote Sensing
WOS SubjectGeography, Physical ; Remote Sensing
WOS IDWOS:000488826400005
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/129929
Collection中国科学院地理科学与资源研究所
Corresponding AuthorQin, Cheng-Zhi
Affiliation1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
4.Nanjing Normal Univ, Sch Geog, Nanjing 210023, Jiangsu, Peoples R China
5.Univ Wisconsin, Dept Geog, Madison, WI 53706 USA
Recommended Citation
GB/T 7714
Hou, Zhi-Wei,Qin, Cheng-Zhi,Zhu, A-Xing,et al. From Manual to Intelligent: A Review of Input Data Preparation Methods for Geographic Modeling[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2019,8(9):16.
APA Hou, Zhi-Wei,Qin, Cheng-Zhi,Zhu, A-Xing,Liang, Peng,Wang, Yi-Jie,&Zhu, Yun-Qiang.(2019).From Manual to Intelligent: A Review of Input Data Preparation Methods for Geographic Modeling.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,8(9),16.
MLA Hou, Zhi-Wei,et al."From Manual to Intelligent: A Review of Input Data Preparation Methods for Geographic Modeling".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 8.9(2019):16.
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