RT Journal Article
T1 Geospatial artificial intelligence (GeoAI) in the integrated hydrological and fluvial systems modeling: review of current applications and trends
A1 Gonzales Inca, Carlos
A1 Calle Navarro, Mikel
A1 Croghan, Danny
A1 Torabi Haghighi, Ali
A1 Marttila, Hannu
A1 Silander, Jari
A1 Alho, Petteri
AB 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.
PB MDPI
YR 2022
FD 2022-07-13
LK https://hdl.handle.net/20.500.14352/133743
UL https://hdl.handle.net/20.500.14352/133743
LA eng
NO 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. https://doi.org/10.3390/w14142211
NO Academy of Finland
NO University of Turku
DS Docta Complutense
RD 21 mar 2026