Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

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 col...

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Bibliographic Details
Published in:Water (Basel) Vol. 14; no. 14; p. 2211
Main Authors: Gonzales-Inca, Carlos, Calle, Mikel, Croghan, Danny, Torabi Haghighi, Ali, Marttila, Hannu, Silander, Jari, Alho, Petteri
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.07.2022
Subjects:
Artificial intelligence
Classification
Clustering
Cognition & reasoning
Data compression
Deep learning
Geomorphology
Hydraulics
Hydrology
Keywords
Machine learning
Neural networks
Optimization algorithms
prediction
Remote sensing
Spatial data
Trends
water
Water quality
Finland
ISSN:2073-4441, 2073-4441
Online Access:Get full text
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Summary: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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ISSN:2073-4441
2073-4441
DOI:10.3390/w14142211