| 1. |
- Paryani, Sina, et al.
(författare)
-
Hybrid-based approaches for the flood susceptibility prediction of Kermanshah province, Iran
- 2023
-
Ingår i: Natural Hazards. - : Springer Science and Business Media LLC. - 0921-030X .- 1573-0840. ; 116:1, s. 837-868
-
Tidskriftsartikel (refereegranskat)abstract
- This study aims at optimizing the support vector regression (SVR) model using four metaheuristic methods, Harris hawks optimization (HHO), particle swarm optimization (PSO), gray wolf optimizer (GWO), and bat algorithm (BA). The intent is to create a reliable flood susceptibility map (FSM). In this regard, a flood inventory map for 617 flood locations was generated from the Google earth engine (GEE). Four hundred and thirty-two random locations (70%) were used for spatial flood susceptibility modeling, and 185 random locations (30%) were selected for testing hybrid approaches. Based on the available data and literature, the following eleven factors were selected: altitude, slope angle, slope aspect, plan curvature, stream power index (SPI), topographic wetness index (TWI), distance to river, lithology, drainage density, land use, and rainfall. The normalized frequency ratio (NFR) method was used to obtain a weight for each class of each factor. Next, flood susceptibility maps were produced by SVR-HHO, SVR-PSO, SVR-GWO, and SVR-BA hybrid models. The prediction power of hybrid models was assessed using various indicators of sensitivity, specificity, accuracy, kappa coefficient, receiver operating curve (ROC) diagram, mean square error (MSE), and root-mean-square error (RMSE). Validation results indicated the area under the curve (AUC) of 85.8%, 85.7%, 85.5%, and 84.6% for the SVR-HHO, SVR-GWO, SVR-BA, and SVR-PSO hybrid models, respectively. The results from testing phase reveal the best performance of the SVR-HHO model (RMSE = 0.401, MSE = 0.160, sensitivity = 0.822, specificity = 0.800, accuracy = 0.811, and kappa = 0.622). The SVR-PSO model had a poor performance (RMSE = 0.406, MSE = 0.164, sensitivity = 0.827, specificity = 0.773, accuracy = 0.80, and kappa = 0.60). It can be concluded that the map produced by SVR-HHO is a feasible approach for modeling flood susceptibility.
|
|
| 2. |
- Ghayur Sadigh, Armin, et al.
(författare)
-
Comparison of optimized data-driven models for landslide susceptibility mapping
- 2023
-
Ingår i: Environment, Development and Sustainability. - 1387-585X .- 1573-2975.
-
Tidskriftsartikel (refereegranskat)abstract
- Locations prone to landslides must be identified and mapped to prevent landslide-related damage and casualties. Machine learning approaches have proven effective for such tasks and have thus been widely applied. However, owing to the rapid development of data-driven approaches, deep learning methods that can exhibit enhanced prediction accuracies have not been fully evaluated. Several researchers have compared different methods without optimizing them, whereas others optimized a single method using different algorithms and compared them. In this study, the performances of different fully optimized methods for landslide susceptibility mapping within the landslide-prone Kermanshah province of Iran were compared. The models, i.e., convolutional neural networks (CNNs), deep neural networks (DNNs), and support vector machine (SVM) frameworks were developed using 14 conditioning factors and a landslide inventory containing 110 historical landslide points. The models were optimized to maximize the area under the receiver operating characteristic curve (AUC), while maintaining their stability. The results showed that the CNN (accuracy = 0.88, root mean square error (RMSE) = 0.37220, and AUC = 0.88) outperformed the DNN (accuracy = 0.79, RMSE = 0.40364, and AUC = 0.82) and SVM (accuracy = 0.80, RMSE = 0.42827, and AUC = 0.80) models using the same testing dataset. Moreover, the CNN model exhibiting the highest robustness among the three models, given its smallest AUC difference between the training and testing datasets. Notably, the dataset used in this study had a low spatial accuracy and limited sample points, and thus, the CNN approach can be considered useful for susceptibility assessment in other landslide-prone regions worldwide, particularly areas with poor data quality and quantity. The most important conditioning factors for all models were rainfall and the distances from roads and drainages.
|
|
| 3. |
- Khosravi, Khabat, et al.
(författare)
-
Fluvial bedload transport modelling: advanced ensemble tree-based models or optimized deep learning algorithms?
- 2024
-
Ingår i: Engineering Applications of Computational Fluid Mechanics. - : Informa UK Limited. - 1994-2060 .- 1997-003X. ; 18:1
-
Tidskriftsartikel (refereegranskat)abstract
- The potential of advanced tree-based models and optimized deep learning algorithms to predict fluvial bedload transport was explored, identifying the most flexible and accurate algorithm, and the optimum set of readily available and reliable inputs. Using 926 datasets for 20 rivers, the performance of three groups of models was tested: (1) standalone tree-based models Alternating Model Tree (AMT) and Dual Perturb and Combine Tree (DPCT); (2) ensemble tree-based models Iterative Absolute Error Regression (IAER), ensembled with AMT and DPCT; and (3) optimized deep learning models Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) ensembled with Grey Wolf Optimizer. Comparison of the predictive performance of the models with that of commonly used empirical equations and sensitivity analysis of the driving variables revealed that: (i) the coarse grain-size percentile D90 was the most effective variable in bedload transport prediction (where Dx is the xth percentile of the bed surface grain size distribution), followed by D84, D50, flow discharge, D16, and channel slope and width; (ii) all tree-based models and optimized deep learning algorithms displayed ‘very good’ or ‘good’ performance, outperforming empirical equations; and (iii) all algorithms performed best when all input parameters were used. Thus, a range of different input variable combinations must be considered in the optimization of these models. Overall, ensemble algorithms provided more accurate predictions of bedload transport than their standalone counterpart. In particular, the ensemble tree-based model IAER-AMT performed most strongly, displaying great potential to produce robust predictions of bedload transport in coarse-grained rivers based on a few readily available flow and channel variables.
|
|
| 4. |
- Panahi, Mahdi, et al.
(författare)
-
Large-scale dynamic flood monitoring in an arid-zone floodplain using SAR data and hybrid machine-learning models
- 2022
-
Ingår i: Journal of Hydrology. - : Elsevier BV. - 0022-1694 .- 1879-2707. ; 611, s. 128001-
-
Tidskriftsartikel (refereegranskat)abstract
- Although the growing number of synthetic aperture radar (SAR) satellites has increased their application in flood-extent mapping, predictive models for the analysis of flood dynamics that are independent of sensor characteristics must be developed to fully extract information from SAR images for flood mitigation. This study aimed to develop hybrid machine-learning models for flood mapping in the Ahvaz region, Iran, based on SAR data. Each hybrid model consists of a support vector machine (SVM) algorithm coupled with one of the following metaheuristic optimization procedures: grey wolf optimization (GWO), differential evolution, and the imperialist competitive algorithm. Sentinel-1 acquired SAR images before and during flooding between 20 March and 26 May of 2019. The goodness-of-fit level and predictive capability of each model were scrutinized using overall accuracy, producer accuracy, and user accuracy. The SVM-GWO approach yielded the highest accuracy with overall accuracies of 96.07% and 93.39% in the training and validation steps, respectively. Furthermore, this hybrid model provided the most accurate classification of water-inundation class based on producer accuracy (96.67%) and user accuracy (95.05%). The results highlight that wetland is the last land-use/land-cover type to return to normal conditions due to the many previously dry oxbow lakes that could trap water for a long time. Furthermore, the nine most suitable sites for flood-protection structures (e.g., embankments and levees) were identified based on floodwater distribution analysis. This work describes a robust, data-parsimonious approach that will benefit flood mitigation studies seeking to identify the most suitable locations for embankments based on spatio-temporal flood dynamics.
|
|
| 5. |
- Rezaie, Fatemeh, et al.
(författare)
-
Improving landslide susceptibility mapping using integration of ResU-Net technique and optimized machine learning algorithms
- 2023
-
Ingår i: Remote Sensing of Soil and Land Surface Processes: Monitoring, Mapping, and Modeling. - : Elsevier BV. ; , s. 419-438
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Landslides are the most common natural disasters in mountainous areas that follow major seismic events, volcanic activity, melting snow, or prolonged and intense rainfalls and cause severe disruptions to ecosystems, economies, and societies worldwide. Therefore, minimizing their negative effects through landslide-susceptibility assessment is essential. In this study, the standard support vector regression (SVR) integrated with the gray wolf optimizer (GWO) and particle swarm optimization (PSO) algorithms were used to map landslide-prone areas. The landslide inventory map was automatically generated using a pixel-based technique based on residual U-Net algorithm from the Sentinel-2 data. In total, 4900 landslide samples were identified and divided randomly into two groups, creating training (70%) and testing (30%) datasets. In addition, nine factors that affect landslides were selected to construct a model using each algorithm. Finally, the performance of the models (SVR, SVR-GWO, and SVR-PSO) were validated and compared using the area under the receiver operating characteristic curve. The findings showed that the hybrid SVR-GWO model performed better than the standard model and is recommended for landslide susceptibility assessment due to its accuracy and efficiency.
|
|
