University of Oulu

Gonzales-Inca, C., Calle, M., Croghan, D., Torabi Haghighi, A., Marttila, H., Silander, J., & Alho, P. (2022). Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends. Water, 14(14), 2211.

Geospatial artificial intelligence (GeoAI) in the integrated hydrological and fluvial systems modeling : review of current applications and trends

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Author: Gonzales-Inca, Carlos1; Calle, Mikel1,2; Croghan, Danny3;
Organizations: 1Department of Geography and Geology, University of Turku, FI-20014 Turun Yliopisto, Finland
2Turku Collegium of Sciences, Medicine and Technology, University of Turku, FI-20014 Turun Yliopisto, Finland
3Water, Energy and Environmental Engineering Research Unit, University of Oulu, FI-90014 Oulu, Finland
4Finnish Environmental Institute (SYKE), FI-00790 Helsinki, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1 MB)
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Language: English
Published: Multidisciplinary Digital Publishing Institute, 2022
Publish Date: 2022-12-29


This paper reviews the current GeoAI and machine learning applications in hydrological and hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial geomorphic and morphodynamic mapping. GeoAI effectively harnesses the vast amount of spatial and non-spatial data collected with the new automatic technologies. The fast development of GeoAI provides multiple methods and techniques, although it also makes comparisons between different methods challenging. Overall, selecting a particular GeoAI method depends on the application’s objective, data availability, and user expertise. GeoAI has shown advantages in non-linear modeling, computational efficiency, integration of multiple data sources, high accurate prediction capability, and the unraveling of new hydrological patterns and processes. A major drawback in most GeoAI models is the adequate model setting and low physical interpretability, explainability, and model generalization. The most recent research on hydrological GeoAI has focused on integrating the physical-based models’ principles with the GeoAI methods and on the progress towards autonomous prediction and forecasting systems.

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Series: Water
ISSN: 2073-4441
ISSN-E: 2073-4441
ISSN-L: 2073-4441
Volume: 14
Issue: 14
Article number: 2211
DOI: 10.3390/w14142211
Type of Publication: A2 Review article in a scientific journal
Field of Science: 218 Environmental engineering
Funding: This work was funded by Academy of Finland, grant number 337279, 346161, 347701 and 346165 (NextGenerationEU). MC was funded by the Turku Collegium of Science, Medicine and Technology (TCSMT), University of Turku, Finland.
Copyright information: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (