Abstract
Geographical modeling has been recognized as a powerful way to solve complex geographic problems. However, its wide applicability is increasingly hindered by its complexity in domain knowledge required and the procedures involved. In this chapter, we argue that domain knowledge plays a key role in making geographical modeling intelligent. Domain-knowledge-based intelligent geographical modeling would not only solve wide geographical problems in an easy-to-use manner on the premise of the effectiveness of the built model specific to the application context, but also contribute to research in artificial intelligence.
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References
Bergen, K. J., Johnson, P. A., de Hoop, M. V., & Beroza, G. C. (2019). Machine learning for data-driven discovery in solid Earth geoscience. Science, 363, eaau0323.
Chen, M., Voinov, A., Ames, D. P., Kettner, A. J., Goodall, J. L., Jakeman, A. J., Barton, M. C., Harpham, Q., Cuddy, S. M., DeLuca, C., Yue, S., Wang, J., Zhang, F., Wen, Y., & Lu, G. (2020). Position paper: Open web-distributed integrated geographic modelling and simulation to enable broader participation and applications. Earth-Science Reviews, 207, 103223.
Goodchild, M. F. (2004). The validity and usefulness of laws in geographic information science and geography. Annals of the Association of American Geographers, 94, 300–303.
Goodchild, M. F., & Li, W. (2021). Replication across space and time must be weak in the social and environmental sciences. Proceedings of the National Academy of Sciences, 118(35), e2015759118.
Hedelin, B., Gray, S., Woehlke, S., BenDor, T. K., Singer, A., Jordan, R., Zellner, M., Giabbanelli, P., Glynn, P., Jenni, K., Jetter, A., Kolagani, N., Laursen, B., Leong, K. M., Olabisi, L. C., & Sterling, E. (2021). What’s left before participatory modeling can fully support real-world environmental planning processes: A case study review. Environmental Modelling & Software, 143, 105073.
Hou, Z.-W., Qin, C.-Z., Zhu, A.-X., Liang, P., Wang, Y.-J., & Zhu, Y.-Q. (2019). From manual to intelligent: A review of input data preparation methods for geographic modeling. ISPRS International Journal of Geo-Information, 8(9), 376.
Janowicz, K., Gao, S., McKenzie, G., Hu, Y., & Bhaduri, B. (2020). GeoAI: Spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. International Journal of Geographical Information Science, 34(4), 625–636.
Jiang, J., Zhu, A.-X., Qin, C.-Z., & Liu, J. (2019). A knowledge-based method for the automatic determination of hydrological model structures. Journal of Hydroinformatics, 21(6), 1163–1178.
Li, W. (2020). GeoAI: Where machine learning and big data converge in GIScience. Journal of Spatial Information Science, 20, 71–77.
Liang, P., Qin, C.-Z., Zhu, A.-X., Hou, Z.-W., Fan, N.-Q., & Wang, Y.-J. (2020). A case-based method of selecting covariates for digital soil mapping. Journal of Integrative Agriculture, 19(8), 2127–2136.
Qin, C.-Z., Wu, X.-W., Jiang, J.-C., & Zhu, A.-X. (2016). Case-based knowledge formalization and reasoning method for digital terrain analysis—Application to extracting drainage networks. Hydrology and Earth System Sciences, 20, 3379–3392.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566, 195–204.
Wang, S. W. (2010). A CyberGIS framework for the synthesis of cyberinfrastructure, GIS, and spatial analysis. Annals of the Association of American Geographers, 100(3), 535–557.
Zhu, A.-X., Zhao, F.-H., Liang, P., & Qin, C.-Z. (2021). Next generation of GIS: Must be easy. Annals of GIS, 27(1), 71–86.
Acknowledgements
This work was supported by the National Key R&D Project of China (Grant No. 2021YFB3900904), the National Natural Science Foundation of China (No. 41871362), and the 111 Program of China (No. D19002). Supports to A-Xing Zhu through the Vilas Associate Award, the Hammel Faculty Fellow Award, and the Manasse Chair Professorship from the University of Wisconsin-Madison are greatly appreciated.
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Qin, CZ., Zhu, AX. (2022). Towards Domain-Knowledge-Based Intelligent Geographical Modeling. In: Li, B., Shi, X., Zhu, AX., Wang, C., Lin, H. (eds) New Thinking in GIScience. Springer, Singapore. https://doi.org/10.1007/978-981-19-3816-0_19
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DOI: https://doi.org/10.1007/978-981-19-3816-0_19
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