Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends
2. Review Methodology and Outline
3. Brief Introduction to Geospatial Artificial Intelligence
4. Current GeoAI Applications in Integrated Hydrological and Fluvial Systems Modeling
4.1. Hydrological and Hydraulic Modeling
4.1.1. Hydrological System Classification
4.1.2. Hydrological Data Fusion and Geospatial Downscaling
4.1.3. Spatial Prediction of Hydrological Variables
4.1.4. Hydrological Process Modeling
4.1.5. Hydraulic Modeling
4.1.6. Hydrological Data Assimilation
4.2. Modeling Optimization Problems for Hydrological Model Calibration and Decision Support System
4.2.1. Hydrological Model Calibration
4.2.2. Decision Support System for Integrated Water Resources Management
4.3. Automatic Water Quality Monitoring and Spatio-Temporal Prediction
4.3.1. Automatic Water Quality Monitoring
4.3.2. Spatio-Temporal Water Quality Prediction
4.4. Machine Learning in Fluvial Geomorphic and Morphodynamic Mapping
5. GeoAI Causal and Predictive Inference Capability
5.1. Renewed Data-Driven Research
5.2. Generalization of GeoAI Prediction
5.3. GeoAI Data Requirement for Reliably Prediction
5.4. GeoAI Capacity to Provide Novel Physical Insights
6. GeoAI Research Trends in Integrated Hydrological and Fluvial Systems Modeling
6.1. Toward Transdisciplinary GeoAI Research in Hydrological Modeling
6.2. Augmenting GeoAI Prediction Capability with Open Data and Crowdsourced Data
6.3. From Physical-Based and GeoAI Hybrid Models to Fully Integrated GeoAI–Physical-Based Models
6.4. From Small-Scale to Global-Scale Hydrological Modeling
6.5. Automation of Hydrological and Fluvial System Modeling
6.6. GeoAI-Based Multi-Dimensional Geo-Visualization and Digital Twin
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
|ANN||Artificial Neural Network|
|CNN||Convolution Neural Network|
|DEM||Digital Elevation Model|
|GAN||Generative Adversarial Networks|
|GeoAI||Geospatial Artificial Intelligence|
|IoT||Internet of Things|
|IWRM||Integrated Water Resources Management|
|LiDAR||Light Detection and Ranging|
|LSTM||Long Short-term Memory Networks|
|RF||Random Forest (RF)|
|RNN||Recurrent Neural Network (RNN)|
|SVM||Support Vector Machine (SVM)|
|UAV||Unmanned Aerial Vehicles|
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|Unsupervised Learning||Supervised Learning||Modeling Optimization Problems|
|Clustering:||Regression and Classification:||Evolutionary Computing:|
|Genetic algorithm (GA)|
|Dimension reduction unsupervised/semi-supervised depth learning:||Metaheuristic methods:|
|Particle swan optimization (PSO) algorithm|
Artificial bee colony (ABC)
Ant colony optimization (ACO)
Gray wolf optimization algorithm
Whale optimization algorithm (WOA)
|Self-supervised learning/Reinforced learning (RL)|
|Single agent/Multi-agent RL|
|Method and Software||Objectives, Advantages, and Limitations||Reference|
|ANN and RF and permuted feature importance|
Software: Coded in r, available online: https://gitlab.com/lennartschmidt/floodmagnitude (accessed on 21 June 2022).
|Objective: To compare ANN and RF flood prediction at the national level in Germany|
Advantages: ANN and RF achieved higher prediction accuracy for a large area with a large dataset than linear models. They reflect basic hydrological principles.
Limitations: Heterogeneity of results across algorithms. The non-uniqueness/equifinality problem was identified due to the ML model setting.
|ANN, ANFIS, wavelet neural networks, and hybrid ANFIS with wavelets transformation|
|Objective: Hourly rainfall-runoff forecasting in Richmond River, Australia|
Advantages: Several ML methods compared, showing that hybrid ANFIS wavelet-based models significantly outperform ANFIS and ANN.
Limitations: Only rainfall and runoff data were used in the modeling. Catchment physical features were not included.
|RF and hybrid RF and|
the hydromad hydrological model.
RF package in R and hydromad package (available online: https://hydromad.catchment.org) (accessed on 21 June 2022).
|Objective: To study the RF model performance vs. hydromad conceptual models in USA and Canada.|
Advantages: The RF model is simple and outperformed existing conceptual flood-forecasting models, predicting low and medium flood magnitudes.
Limitations: The RF models exhibit inaccuracies for higher flood events, and their performance depends on the catchment characteristics.
Matlab toolboxes (e.g., wavelet)
|Objective: Comparison of ANN and ARMA, combining them with wavelet analysis, empirical model decomposition, and singular spectrum analysis, for hindcasting and forecasting of monthly streamflow in two Chinese basins.|
Advantages: In hindcasting, the hybrid ANN and ARAM models performed better than the non-hybrid ones.
Limitations: Hybrid models were not suitable for monthly streamflow forecasting, needing further refinement.
|SOM and modified SOM (MSOM)|
Software: Not stated
|Objective: To compare SOM and the MSOM clustering approaches, to deal with missing values, identifying groundwater exchange areas between the Serra Geral and Guarani aquifers (Brazil).|
Advantages: The MSOM showed higher accuracy in mapping hydrochemistry and groundwater physical properties, quantifying relationships in a large set of variables, which would not be revealed with conventional multivariate statistics.
Limitations: SOM requires extensive data to obtain accurate and informative clusters.
|CNN with transfer learning in time-series flood prediction|
Software: Not stated
|Objective: To introduce a new CNN transfer-learning model as a conversion tool between time-series and image data to predict water levels in flood events.|
Advantages: CNN showed acceptable agreement with the observed water level data. Quantitative improvement in the CNN transfer learning appeared in the reduction in computational costs.
Limitations: CNN captured higher peaks poorly. CNN was not as good as a fully connected deep neural network or RNN, especially when predicting the highest peaks.
|Deep neural network (DNN) and data augmentation.|
Software: Not stated
|Objective: To assess the feasibility of DNN in urban flood mapping, integrated with the stormwater management model (SWMM). Study area was two small urban catchments in Seoul, Korea.|
Advantages: The DNN was about 300 times faster than SWMM. Data augmentation could improve the poor predictive power of DNN.
Limitations: Limited amount of input data needed to be improved by applying data augmentation.
|CNN-based segmentation, VGG16, U-net, and Segnet|
Software: Not stated
|Objective: To learn the spatiotemporal patterns of the mismatch between total water storage anomalies derived from the GRACE satellite mission and those simulated by the NOAH land surface model. Study area was India.|
Advantages: CNN models significantly improve the match with modeled and satellite-based observations of terrestrial total water storage.
Limitations: Current grid resolution is relatively coarse.
|LSTM and sequence-to-sequence (seq2seq) model|
Software: uses the Keras and TensorFlow packages in Python 3
|Objective: To present a prediction model based on LSTM and the seq2seq structure to estimate hourly rainfall-runoff. Study area was two small watersheds in Iowa, US.|
Advantages: The LSTM-based seq2seq model was demonstrated to be an effective method for rainfall-runoff predictions and applicable to different watersheds.
Limitations: Needs sufficient predictive power.
|Transfer entropy (TE), ANN, LSTM, random forest regression (RFR), and support vector regression (SVR)|
Software: Not stated
|Objective: To predict discharge with ML models and identify dominant drivers of discharge and their timescales using sensor data and TE. Study area was the Dry Creek Experimental Watershed, ID, USA.|
Advantages: The LSTM model is effective in identifying the key lag and aggregation scales for predicting discharge. TE was able to identify dominant streamflow controls and the relative importance of different mechanisms of streamflow generation.
Limitations: Restricting ML models based on dominant timescales undercuts their skill at learning these timescales internally.
|ANN and K-nearest neighbor|
Software: Not stated
|Objective: To introduce the coupled ANN with the K-nearest neighbor hybrid machine learning (HML) for flood forecast. The study area was the Tunxi watershed, Anhui, China.|
Advantages: HML model showed satisfactory performance and reliable stability and predicted discharge continuously without accuracy loss.
Limitations: Peak flow was not well captured.
|Extreme learning machine (ELM) and the multilayer perceptron (MLP) for data assimilation with GR4J lumped hydrological model.|
Software: Not stated
|Objective: To test two new ELM and MLP data assimilation methods in the rainfall-runoff models. The study catchments were Mistassibi (Canada), Schwuerbitz (Germany), Ourthe (Belgium), and Los Idolos (Mexico).|
Advantages: It shows that ELM and MLP can be successfully used for data assimilation, with a noticeable improvement over the GR4J and OpenLoop hydrological models for all studied catchments.
Limitations: The ELM, MLP, and hydrological models are loosely coupled and simulated on an open-loop approach, and no feedback from the model output is considered. Furthermore, few observed variables in the ANN training are used (discharge and temperature).
|Bayesian and variational data assimilation hybrid algorithm called OPTIMISTS (Optimized PareTo Inverse Modeling through Integrated STochastic Search)|
Software: code available online:
https://github.com/felherc/OPTIMISTS (accessed on 21 June 2022).
|Objective: To introduce the new data assimilation algorithm OPTIMISTS and to test it using the DHSVM hydrological models in the Blue River and Indiantown watershed, USA.|
Advantages: OPTIMISTS combines the features from Bayesian and variational approaches. OPTIMISTS produced probabilistic forecasts efficiently, with the combined advantages of allowing for fast, non-Gaussian, non-linear, and high-resolution prediction and for the balancing of the imperfect observation.
Limitations: The model seems to be under development.
|ML Method||Objectives, Advantages, and Limitations||Reference|
|Non-dominated sorting genetic algorithm II (NSGA-II), particle swarm optimization (MPSO), the Pareto envelope-based selection algorithm II (PESA-II), the strength Pareto evolutionary algorithm II (SPEA-II) with the combined objective function and genetic algorithm.|
Software: Not stated
|Objective: To compare optimization techniques to calibrate conceptual hydrological models.|
Advantages: All techniques perform well, better results gained than when using a single-objective algorithm. The NSGA-II with two indicators performed better than the MPSO with one indicator.
Limitations: Depending on the selected performance indicators, the best model varied.
|The multi-objective evolution algorithm MODE-ACM and the enhanced Pareto multi-objective differential evolution algorithm (EPMODE).|
Software: Not stated
|Objective: To introduce the MODE-ACM and the enhanced EPMODE model. To test model efficacy by comparing the NSGA-II and SPEA2 model.|
Advantages: The EPMODE and the MODE-ACM were both reliable and showed better performance than the NSGA-II and SPEA2 models.
Limitation: Very complex model.
|The multi-objective particle swarm optimization (MOPSO), NSGA-II and the multi-objective shuffled complex evolution metropolis (MOSCEM-UA).|
Software: Not stated
|Objective: Comparison of three multi-objective algorithms for hydrological model calibration.|
Advantages: All three algorithms are able to find Pareto sets of solutions. The most uniform distribution of the solutions was derived with MOSCEM-U as the NSGA-II has the shortest Pareto optimal front and the MOPSO has the maximal extent of the obtained non-dominated front.
Limitations: The rate of convergence with the optimal solutions varies across the three algorithms.
|MOSCEM-UA and shuffled complex|
evolution metropolis (SCEM-UA).
Software: Not stated
|Objective: To calibrate hydrological models using more effective and efficient multi-objective algorithms called MOSCEM-UA.|
Advantages: MOSCEM-UA allows multi-objective calibration, preventing the collapse of the algorithm to a single region of highest attraction. It combines the complex shuffling and the probabilistic covariance-annealing search procedure of the SCEM-UA algorithm.
Limitations: Challenging to make sure that a diverse and large initial population size is provided, which supports multiple objective approaches.
|ANN coupled with SWAT model.|
Software: SWAT-ANN, available online: https://zenodo.org/record/3699658#.YewXid_RZaQ (accessed on 21 June 2022).
|Objective: Evaluation of SWAT-ANN rainfall-runoff simulation in a catchment, Italy.|
Advantages: The SWAT-ANN prediction accuracy was acceptable and useful in the absence of observational data.
Limitations: The overall model calibration is only evaluated by model residual error.
Software: Multi-objective evolutionary sensitivity handling algorithm (MOESHA), programed in Python
|Objective: River discharge modeling with a hydrological model called EXP-HYDRO, applied in a small catchment in Wales, UK.|
Advantages: The MOESHA algorithm determines well the optimal distribution of the model parameters that maximize model robustness and minimize error; it also estimates model parameter uncertainty.
Disadvantages: Computationally expensive.
|Multi-algorithm, genetically adaptive multi-objective method (AMALGAM), single evolutionary multi-objective optimization (SPEA-II and NSGA-II).|
Software: AMALGAM code in Visual Basic
|Objective: Comparative study of SWAT model calibration using single- and multi-evolutionary optimization algorithms. The study areas are the Yellow River Headwaters Watershed (Tibet Plateau, China), the Reynolds Creek Experimental Watershed (Idaho, USA), the Little River Experimental Watershed (Georgia, USA), and the Mahantango Creek Experimental Watershed (Pennsylvania, USA).|
Advantage: The advantage of the multi-evolutionary algorithm AMALGAM calibrating the SWAT model has been demonstrated. AMALGAM provides fast, reliable, and computationally efficient solutions to multi-objective optimization problems.
Limitations: With a small number of run schemes, multiple trials might be needed for implementing AMALGAM. The solution contribution of the different algorithms used in AMALGAM varies in the different watersheds.
|Single-objective and multi-objective particle swarm optimization (PSO) algorithms.|
Software: Not stated
|Objective: Automatic calibration of hydrologic engineering center- hydrologic modeling systems (HEC-HMS) rainfall-runoff model. The study area is the Tamar sub-basin of the Gorganroud River Basin, Iran.|
Advantages: Multi-objective PSO calibration outperforms the single-objective one. However, an appropriate combination of objective functions is essential in multi-objective calibration.
Limitations: Increasing the number of objective functions did not necessarily lead to a better performance than the bi-objective calibration. The increasing number of objective functions also introduces computational challenges. Insufficient data and flood events seriously affect the calibration performance.
|The deep learning gradient-based optimizer.|
Software: Python code, available online: https://github.com/ckrapu/gr4j_theano (accessed on 21 June 2022).
|Objective: Estimating unknown parameters for GR4J conceptual hydrological model.|
Advantages: The deep learning gradient-based optimizer is effective for high-dimensional inverse estimation problems for hydrological models.
Limitations: Need for hydrological model to be established at the site and high computational effort.
|Partial least squares regression (PLSR) and ANFIS.|
Software: Not stated
|Objective: Development of a smart irrigation decision support system, using PLSR and ANFIS as reasoning engines. The study area is in the southeast of Spain.|
Advantages: ANFIS prediction was better than PLSR for water requirement estimation and identifying timely crop irrigation needs.
Limitations: The model has been tested with a few soil and weather variables.
|Reinforcement learning (RL)|
Software: Python code, available online: https://github.com/kLabUM/rl-storm-control (accessed on 21 June 2022).
|Objective: To implement a smart stormwater real-time system control based on RL.|
Advantages: The RL-based model learned the control valve strategy in a distributed stormwater system by interacting with the system it controls under thousands of simulated storm scenarios. It effectively tries various control strategies until it achieves target water level and flow set points.
Limitations: RL performance is highly sensitive to the RL agent reward formulation and requires a significant amount of computational resources to achieve a good performance.
|Method and Software||Objective, Advantages, and Limitations||Reference|
Software: ANNs computed and fitted with the Keras package in R.
Available online: https://github.com/benoit-liquet/AD_ANN (accessed on 21 June 2022).
|Objective: To use ANN to detect anomalies caused by the technical error of in situ sensor monitoring of conductivity and turbidity.|
Advantage: Semi-supervised ANN very well suited to identifying short-term anomalous events. Supervised classification able to identify long-term anomalies.
Limitations: High rate of false positive detection. Large dataset required due to relative scarcity of anomalous events to train data.
|MLP, SVM-SMO, lazy-instance-based learning K nearest-neighbor (IBK), KStar, RF, random tree, and REPTree.|
|Objective: To compare ML techniques for soft sensor monitoring of biological oxygen demand (BOD).|
Advantage: IBK algorithm was best for estimating BOD based on turbidity, dissolved oxygen, pH, and water temperature sensors. IBK algorithm can also be used within a low-cost system to allow incorporation into IoT-based WQ systems.
Limitations: Overloading of servers may occur in IoT-based systems if prediction algorithms run on the cloud for a large number of sensing nodes.
|Feed-forward ANN (FF-ANN).|
Software: Python using the scikit-learn library.
|Objective: To compare FF-ANN and traditional methods for drift correction.|
Advantages: The FF-ANN model outperformed traditional methods for drift calibration and may increase calibration lifetime and reduce calibration frequency for water quality sensors.
Limitations: High error rate apparent when using logistic function.
|RF, SVM, and logistic regression|
|Objective: Evaluation of 3D wide-area WQ monitoring and analysis systems, using unmanned surface vehicles (USV) and ML algorithms.|
Advantage: RF demonstrated high precision for estimating WQ status, using WQ measured data (turbidity, total dissolved solid, and pH) by USV at multiple points and different water depth levels.
Limitations: USV drifting control, working performance, and system accuracy were evaluated just in one environmental condition (a small lake) and for a few WQ parameters.
|Gradient boosting (XGBoost), RF, coupled with denoising model called CEEMDAN.|
Software: Not stated
|Objective: Hourly prediction of several WQ parameters in Tualatin River, Oregon, USA.|
Advantages: The best model performance depended on the predicted WQ variable. CEEMDAN-RF and CEEMDAN-XGBoost show better performance, less errors, and higher stability than simple RF and XGBoost. New error metric is introduced for model performance evaluation and compared with conventional methods of model evaluation.
Limitations: The prediction model only depends on time-series data and no other explanatory variables were included.
|Multi-layer perceptron, radial basis function ANN, ANFIS, and ANFIS with wavelet denoising technique (WDT-ANFIS).|
Software: wavelet and fuzzy logic toolboxes of MATLAB
|Objective: Prediction of WQ parameters such as ammoniacal nitrogen, suspended solid, and pH in Johor river, Malaysia.|
Advantages: Several ML algorithms were compared, where the WDT-ANFIS model advantage was well illustrated.
Limitations: Complex models and not possible to identify a single network structure that can best predict WQ parameters.
|Decision tree (DT), RF, and deep cascade forest (DCF), trained by big data.|
Software: Python. RF and DT built using the package Scikit-learn v.019. DCF was built with the package gcForest
|Objective: Comparison of 7 traditional learning models vs. 3 ensemble models for prediction of 6 levels of WQ parameters for major rivers and lakes in China.|
Advantages: DT, RF, DCF trained by big data all had significantly better prediction of WQ compared with traditional learning models. DCF had the best performance overall for prediction of all 6 levels of WQ.
Limitations: DCF unable to learn directly from big raw data.
Software: Python using the libraries Scikit-Learn (0.20.1) and MLxtend (0.13.0)
|Objective: Comparison of random forest to multiple linear regression for estimation of high-frequency nutrient concentration.|
Advantages: RF outperformed linear models when more than one predictor was included.
Limitations: At least 3 predictors required to identify clear benefit of using RF compared to multiple linear regressions.
|Integrated LSTM, using cross-correlation and association rules (apriori).|
Software: Not stated
|Objective: GeoAI system to identify and trace point sources of pollution in Shandong Province, China.|
Advantages: LSTM algorithm had high prediction accuracy for tracing the main point sources of pollution.
Limitations: GeoAI model was not aware of the change in aquatic environment conditions. It requires multi-dimensional and multi-spatial perspectives to identify, analyze, and respond to data.
|Objective: Modeling daily total organic carbon (TOC), total nitrogen (TN), total phosphorous (TP), and predicting future fluxes under climate change scenarios for two streams in Finland.|
Advantages: ANN model managed to recreate most dynamics in TOC, TN, and TP.
Limitations: ANN model struggled to capture extreme values for TOC, TN, and TP.
|Least square-SVM (LS-SVM) and multivariate adaptive regression spline (MARS).|
Software: Not stated
|Objective: Prediction of 5-day biochemical oxygen demand (BOD) and chemical oxygen demand (COD) in natural streams in Karoun River, southwest Iran.|
Advantages: LS-SVM and MARS models performed better in terms of external validation criteria and F test compared with multiple-regression-based models and ANN and ANFIS equations.
Limitations: Intensive amount of data collection required for a wide variety of parameters.
|ANN using feed-forward network with Levenberg–Marquardt back-propagation learning.|
Software: Not stated
|Objective: Hybrid approach using a SWAT model as an input to an ANN to simulate monthly nitrate, ammonium, and phosphate loads in Atlanta, GA, USA.|
Advantages: Hybrid model outperformed standalone SWAT and ANN models for prediction of monthly loads. Hybrid models are useful for predictions in unmonitored catchments.
Limitations: Hybrid model for nitrate had substantially better predictions than the ammonium and phosphate models. Large peaks of ammonium and phosphate were underestimated.
|ANN compared with SVM and group method of data handling (GMDH).|
Software: Not stated
|Objective: ML techniques compared for predicting various WQ components in Tireh River, southwest Iran.|
Advantages: SVM models were most accurate, with less data dispersion.
Limitations: All models overestimated some properties.
|Twelve hybrid data mining algorithms compared, spanning two main groups. Group one featured decision tree algorithms, and group two featured meta-classifier or hybrid algorithms.|
Software: Not stated
|Objective: Comparison of 12 hybrid GeoAI models predicting WQ indices in Talar Catchment, Iran.|
Advantages: Hybrid models showed improved predictive power compared to standalone algorithms.
Hybrid bagging random tree (BA-RT) model showed greatest predictive power and was able to produce reliable results despite a dataset spanning a short time period.
Limitations: BA-RT model struggled to accurately predict extreme WQ index values, while most models also overestimated WQI values.
|SVM and RF|
Software: e1071 R package used to build SVM model and random forest R package used to build RF model.
|Objective: SVM and RF algorithms compared to predict high-frequency variation in stream solutes in Hubbard Brook, New Hampshire, USA.|
Advantages: Both ML algorithms were capable of effectively predicting concentrations of major ions.
Limitations: Solutes with atmospheric, episodic, or strong biotic and abiotic controls were much more poorly predicted than solutes least affected by ecosystem dynamics.
|Method and Software||Objective, Advantages and Limitations||Reference|
Software: Python based. CNN supervised classification available for PyQGIS
|Objective: To classify fluvial scenes in 11 rivers in Canada, Italy, Japan, the United Kingdom, and Costa Rica.|
Advantages: The NasNet-CNN model outperformed other supervised classifiers, e.g., maximum likelihood, MLP, and RF. The NasNet-CNN model can be transferable to other rivers with no training data, obtaining good classification accuracy of fluvial land cover.
Limitations: CNN is sensitive to the hyperparameters definition. CNN was also affected by imbalanced training data size in land cover classes. Some features were misclassified.
Software: Python based. Dependency packages: TensorFlow
|Objective: To predict vegetation, bare sediment, and water bodies at a sub-pixel scale with Sentinel-2 images, trained with high resolution UAV images. Study areas were Sesia, Po, and Paglia Rivers in Italy.|
Advantages: Fuzzy-CNN models were successfully used to provide continuous and crisp subpixel classification of Sentinel-2 imagery. The model was transferable to satellite images with different acquisition time. It can be used for annual change detection.
Limitations: UAV reference data obtained with manual OBIA was highly time-consuming. The process was computationally expensive due to the “super-resolution” process used to feed the CNN. The model was tested only in Mediterranean drainage basins.
Software: MATLAB-based software. Leaf area index calculation (LAIC)
|Objective: Hydromorphological features classification using very high resolution UAV images (2.5 cm) in a reach of the River Dee, Wales, UK.|
Advantages: ANN LAIC model showed satisfactory classification accuracy and potential to identify multiple hydromorphological classes that can be attributed to site features based, e.g., on their hydraulic, habitat, or vegetation types. The model settings seem to be transferable to other rivers without training data.
Limitations: The algorithm showed misclassification of small fluvial entities, e.g., shallow water areas with rippled surface, water areas affected by tree shadow, and vegetated banks and/or areas obscured by brown submerged vegetation.
The authors provided very limited information about the model parameter setting in the paper.
Software: Matlab- based software. Visualization and image processing for environmental research (VIPER)
|Objective: To measure river wetted width (RWW) with a novel approach at the subpixel scale by using MODIS and Landsat OLI images in Bay of Bengal, India, and Landsat TM in Columbia River, USA.|
Advantages: CNN-based sub-pixel scale classification resulted in a more accurate estimation of RWW than the conventional hard image classification.
Limitations: No full spectral unmixing was possible due to the spectral variations of land cover classes and the nonlinear mixture phenomenon. Misclassification issues were reported when shadows, bridges, or trees were located along riverbanks. In such situations, RWW was unmeasured.
|GEOBIA, EL, RF, extra tree (ET), gradient tree boosting (GTB), extreme gradient boosting (XGB). Then, it was combined with a voting classifier.|
Software: Python-based with scikit-learn package.
|Objective: To map the main hydromorphological types that characterize fluvial landscapes in Europe by using Copernicus image mosaic and EU DEM. Target classes: water, sediment bars, riparian vegetation, other floodplain units.|
Advantages: RF outperformed any other tested classifier, e.g., ET, GTB, and XGB. Hierarchical object-based segmentation is robust for combining spectral and topographical data at different spatial resolutions and enhancing low spectral resolutions. Area-based validations were the preferred method to validate the quality of the object-based maps.
Limitations: Vegetation units and sediment bars were not very well classified. Main source of error was related to the high mixture of riparian vegetation, sediment bars, and other floodplain features.
|Object-based RF and pixel-based RF, combined with recursive feature elimination and PCAs|
Software: rpart and caret R packages, and EnMAP-Box (environmental mapping and analysis program) 2.1.1 software.
|Objective: To reveal uncertainty, overfitting, and efficiency of terrain attribute identification in fluvial landforms using morphometric variables derived from a LiDAR DTM from Tisza River, Hungary.|
Advantages: Object-based RF method had a better classification accuracy (95%) than the pixel-based RF method (78%) when identifying 4 different river landforms (crevasse channels, swales, point bars, levees). Overfitting was controlled in the study by limiting the number of input variables.
Limitations: Object-based RF classifications needed visual interpretation, field observations, and high-resolution data. PCAs did not help to select more efficient and important variables.
|RF and SVM|
Software: eCognition developer 9 software.
|Objective: Semi-automatic map of riverscape units and in-stream microhabitats, providing continuous, objective, and multi-scale classification using very-high-resolution near-infrared aerial imagery and LiDAR DTM from the Orco River, Italy.|
Advantages: RF better identified riverscape elements, e.g., channel and bars, while SVM did better when classifying in-stream meso-habitats. Topographical data, in particular detrended DTM (DDTM), was a relevant data source for an accurate classification of the riverscape units. Near-infrared imagery combined with DDTM was the best predictor.
Limitations: Extensive expert-based training was necessary for detailed post-classification. Several subjective rules added to the process. Most confusion in the classification was detected between the floodplain and sparse vegetation classes, where the DDTM was not helpful.
Software: ArcGIS based. Geoprocessing tool (multivariate clustering)
|Objective: To delineate valley bottom extent across large catchments and automatically classify valley bottom segments of variable length by using DEM-based derivatives from Richmond River, Australia.|
Advantages: The k-means successfully clustered the entire river network into 6 valley bottom segments of varying length.
Limitations: The resulting cluster is unlabeled and needs expert recognition. Only used a low number of the variables selected (slope and valley bottom width). The model was validated with a basin-scale expert mapping of valley types. This is time-consuming and not available for other areas.
The model was only proposed as a preliminary assessment for further studies.
|Modified Hebbian algorithms and k-mean clustering.|
Software: On-line batch Hebbian algorithm and CoSA (clustering of sparse approximations) packages.
|Objective: To investigate the applicability of ML classifier in Arctic regions using DigitalGlobe Worldview-2 visible/near-infrared, high-resolution imagery from Mackenzie River, Canada, and Selawik and Barrow Rivers in Alaska (USA).|
Advantages: Allows automatic discretization of landscape units in large areas. Useful as a preliminary method to learn which scale of clustering is suitable to study different processes or focuses of the study (e.g., hydrology versus vegetation). Capable of detecting vegetation changes as it recognizes vegetation levels in different classes.
Limitations: No error assessment was performed, nor was there ground truth validation. The selection of an appropriate number of clusters depends on the expert’s decision.
|SVM, RF, ANN, partial least squares, multivariate adaptive regression splines, flexible discriminant analysis, k-NN, regression tree, bagged trees, linear discriminant analysis, regularized linear discriminant analysis, and naive Bayes.|
Software: Caret and h2o R packages.
|Objective: To extrapolate a geomorphic classification of channel types to a regional stream network using DEMs and thematic maps (e.g., lithology, soil, stream network, etc.) from Sacramento River, USA.|
Advantages: Multiple algorithms compared. RF outperformed other models with more accuracy and stability and lower entropy in reach-scale river type classification. Rigorous approach in model design and evaluation of performance with 20 × 10-fold cross validation used for clarification of some black box aspects of ML.
Limitations: Needs large expert-based field survey data. It is unclear if ML is able to integrate predictors at different scales and to show different uncertainty across the watershed.
|RF and RF combined with recursive feature elimination (RF-RFE).|
Software: R program
|Objective: To detect structural and/or neotectonic controls influencing the knickpoints of the drainage network using DEM and thematic maps (geology and geomorphology) from Abaeté Watershed, Brazil.|
Advantages: Simple and reproducible methodology that provides causal relationship of knickpoint formation to lithological contacts and neotectonic configuration and activity. RF succeeded in partially predicting geomorphic indices (e.g., stream length gradient index or normalized channel steepness index) and can be used to predict them in unsampled areas without overfitting.
Limitations: Low performance of the methods, obtaining R2 = 0.38 as the highest correlation between predicted and direct estimation values of geomorphic indices. It may have been affected by the selection of the covariables.
|Template-matching (object-based) algorithm (TMA) and pixel-based SVM.|
Software: Feature Analyst (Overwatch Systems Ltd.). Not specified for SVM.
|Objective: To delineate water surface boundaries and assess the influence of river and bank characteristics in the efficacy of a template-matching compared to a pixel-based algorithm, using high-resolution images with false-color infrared, from the Brazos River, USA.|
Advantages: Both algorithms adequately delineate the water surface. SVM performed better and handled complex and noisy class relationships. TMA performed better than SVM in spatially complex channel morphologies (e.g., partially submerged sediment deposits, sediment bar structures) due to its capability to incorporate both spectral and spatial information.
Limitations: Validation relies on expert knowledge and previous maps. Selection of ancillary data types depends on expert decision and the delineation accuracy of TMA. In addition, the low spectral dimension of the images limited the pixel-based classification. Both algorithms encounter problems when classifying multiple complex features (e.g., overhanging trees) and illumination conditions (e.g., shading). The TMA performance was less spatially consistent than that of SVM.
Software: R based (R v3.5.1). Package “kohonen” v3.0.7
|Objective: To produce waterbody typology from 22 GIS-derived continuous catchment characteristics to capture the dominant controls that influence river reaches across England and Wales.|
Advantages: SOM-based water body topology reflects catchment functional feature controls on river reaches. The method is extendable to other areas where reach-level monitoring is relevant. The SOM combined with hierarchical clustering can be applied over a wide range of catchment, e.g., a national-level waterbody typology map.
Limitations: The method could not isolate individual effects from catchment controls as they are dependent on each other. It does not detect temporal change and local controls such as dams, channelization, and others not taken into account.
|U-Net convolutional neural networks (CNNs).|
Software: Not stated.
|Objective: To introduce the BathyNet framework, a photogrammetry and radiometric-based combined retrieval of water depth using U-Net CNNs. Study area was Lech river, Augsburg, Bavaria, Germany.|
Advantages: U-Net CNNs approximate arbitrary functions and include spatial context. The U-Net CNN-based depth retrieval outperformed traditional regression-based optical inversion methods.
Limitations: U-Net CNNs require large amounts of training data and their application might be unfeasible in areas with scarce water-depth field samples.
|Process-Based Model||GeoAI Model|
|Based on general physical laws.||A data-driven approach, inductive model building, may not fully respect physical laws.|
|All input variables and parameter ranges are well-structured and known.||Unstructured data, not all input variables’ role in the model is known, making the output less interpretable.|
|Limited to the current state-of-the-art.||Able to reveal unknown associations and patterns.|
|It is a deductive, hypothesis testing approach. It can be used for causal inference.||It is an inductive, exploratory approach. Their use in causal inference depends on the GeoAI model building and selected variables.|
|No uniqueness problem due to inverse modeling in model parameterization.||No uniqueness problem due to GeoAI hyper-parameter optimizations.|
|Mostly deterministic, the system is represented by the average values of variables.||Deterministic and probabilistic, depending on the GeoAI method, variables can be treated as probabilistic.|
|Reductionist, considerable simplification of complex processes which can result in prediction bias.||Integrative, GeoAI can be integrated into several types of observation and may be able to reveal patterns not represented by a physical-based model. Therefore, GeoAI prediction can be less biased.|
|It is assumed to be of general application.||It is assumed to be applied only within the range of the training data.|
|Fixed to the model basis data requirement, and unable to deal with multisource data.||Flexible to data input, from minimal input to big data. GeoAI can maximize the use of all types of available data, from different sources, types, and quality.|
|High computing demand for high-frequency and large-scale modeling.||High computing efficiency and suitable for high-frequency and large-scale modeling.|
|One-time calibration, once the model is calibrated, the parameters are usually fixed.||Continuous learning model, the model calibration is constantly updated with past and new data.|
|Well-defined framework for model performance, uncertainty, and error propagation evaluation.||Diverse and developing approaches for model performance, uncertainty, and error propagation evaluation.|
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Gonzales-Inca, C.; Calle, M.; Croghan, D.; Torabi Haghighi, A.; Marttila, H.; Silander, J.; Alho, P. Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends. Water 2022, 14, 2211. https://doi.org/10.3390/w14142211
Gonzales-Inca C, Calle M, Croghan D, Torabi Haghighi A, Marttila H, Silander J, Alho P. Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends. Water. 2022; 14(14):2211. https://doi.org/10.3390/w14142211Chicago/Turabian Style
Gonzales-Inca, Carlos, Mikel Calle, Danny Croghan, Ali Torabi Haghighi, Hannu Marttila, Jari Silander, and Petteri Alho. 2022. "Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends" Water 14, no. 14: 2211. https://doi.org/10.3390/w14142211