| 1. |
- Bui, Dieu Tien, et al.
(författare)
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A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers
- 2020
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Ingår i: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 12:3, s. 1-24
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Tidskriftsartikel (refereegranskat)abstract
- Local scour depth at complex piers (LSCP) cause expensive costs when constructing bridges. In this study, a hybrid artificial intelligence approach of random subspace (RS) meta classifier, based on the reduced error pruning tree (REPTree) base classifier, namely RS-REPTree, was proposed to predict the LSCP. A total of 122 laboratory datasets were used and portioned into training (70%: 85 cases) and validation (30%: 37 cases) datasets for modeling and validation processes, respectively. The statistical metrics such as mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (R), and Taylor diagram were used to check the goodness-of-fit and performance of the proposed model. The capability of this model was assessed and compared with four state-of-the-art soft-computing benchmark algorithms, including artificial neural network (ANN), support vector machine (SVM), M5P, and REPTree, along with two empirical models, including the Florida Department of Transportation (FDOT) and Hydraulic Engineering Circular No. 18 (HEC-18). The findings showed that machine learning algorithms had the highest goodness-of-fit and prediction accuracy (0.885 < R < 0.945) in comparison to the other models. The results of sensitivity analysis by the proposed model indicated that pile cap location (Y) was a more sensitive factor for LSCP among other factors. The result also depicted that the RS-REPTree ensemble model (R = 0.945) could well enhance the prediction power of the REPTree base classifier (R = 0.885). Therefore, the proposed model can be useful as a promising technique to predict the LSCP.
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| 2. |
- Chaudhry, Muhammad Hamid, et al.
(författare)
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Assessment of DSM Based on Radiometric Transformation of UAV Data
- 2021
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Ingår i: Sensors. - Switzerland : MDPI. - 1424-8220. ; 21:5
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Tidskriftsartikel (refereegranskat)abstract
- Unmanned Aerial Vehicle (UAV) is one of the latest technologies for high spatial resolution 3D modeling of the Earth. The objectives of this study are to assess low-cost UAV data using image radiometric transformation techniques and investigate its effects on global and local accuracy of the Digital Surface Model (DSM). This research uses UAV Light Detection and Ranging (LIDAR) data from 80 meters and UAV Drone data from 300 and 500 meters flying height. RAW UAV images acquired from 500 meters flying height are radiometrically transformed in Matrix Laboratory (MATLAB). UAV images from 300 meters flying height are processed for the generation of 3D point cloud and DSM in Pix4D Mapper. UAV LIDAR data are used for the acquisition of Ground Control Points (GCP) and accuracy assessment of UAV Image data products. Accuracy of enhanced DSM with DSM generated from 300 meters flight height were analyzed for point cloud number, density and distribution. Root Mean Square Error (RMSE) value of Z is enhanced from ±2.15 meters to 0.11 meters. For local accuracy assessment of DSM, four different types of land covers are statistically compared with UAV LIDAR resulting in compatibility of enhancement technique with UAV LIDAR accuracy.
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| 3. |
- Ghasemian, Bahareh, et al.
(författare)
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A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran
- 2022
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 22:4
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Tidskriftsartikel (refereegranskat)abstract
- We mapped landslide susceptibility in Kamyaran city of Kurdistan Province, Iran, using a robust deep-learning (DP) model based on a combination of extreme learning machine (ELM), deep belief network (DBN), back propagation (BP), and genetic algorithm (GA). A total of 118 landslide locations were recorded and divided in the training and testing datasets. We selected 25 conditioning factors, and of these, we specified the most important ones by an information gain ratio (IGR) technique. We assessed the performance of the DP model using statistical measures including sensitivity, specificity, accuracy, F1-measure, and area under-the-receiver operating characteristic curve (AUC). Three benchmark algorithms, i.e., support vector machine (SVM), REPTree, and NBTree, were used to check the applicability of the proposed model. The results by IGR concluded that of the 25 conditioning factors, only 16 factors were important for our modeling procedure, and of these, distance to road, road density, lithology and land use were the four most significant factors. Results based on the testing dataset revealed that the DP model had the highest accuracy (0.926) of the compared algorithms, followed by NBTree (0.917), REPTree (0.903), and SVM (0.894). The landslide susceptibility maps prepared from the DP model with AUC = 0.870 performed the best. We consider the DP model a suitable tool for landslide susceptibility mapping.
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| 4. |
- Ghasemian, Bahareh, et al.
(författare)
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Application of a Novel Hybrid Machine Learning Algorithm in Shallow Landslide Susceptibility Mapping in a Mountainous Area
- 2022
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Ingår i: Frontiers in Environmental Science. - : Frontiers Media S.A.. - 2296-665X. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Landslides can be a major challenge in mountainous areas that are influenced by climate and landscape changes. In this study, we propose a hybrid machine learning model based on a rotation forest (RoF) meta classifier and a random forest (RF) decision tree classifier called RoFRF for landslide prediction in a mountainous area near Kamyaran city, Kurdistan Province, Iran. We used 118 landslide locations and 25 conditioning factors from which their predictive usefulness was measured using the chi-square technique in a 10-fold cross-validation analysis. We used the sensitivity, specificity, accuracy, F1-measure, Kappa, and area under the receiver operating characteristic curve (AUC) to validate the performance of the proposed model compared to the Artificial Neural Network (ANN), Logistic Model Tree (LMT), Best First Tree (BFT), and RF models. The validation results demonstrated that the landslide susceptibility map produced by the hybrid model had the highest goodness-of-fit (AUC = 0.953) and higher prediction accuracy (AUC = 0.919) compared to the benchmark models. The hybrid RoFRF model proposed in this study can be used as a robust predictive model for landslide susceptibility mapping in the mountainous regions around the world.
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| 5. |
- Ghayour, Laleh, et al.
(författare)
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Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison between Machine Learning Algorithms
- 2021
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Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 13:7
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Tidskriftsartikel (refereegranskat)abstract
- With the development of remote sensing algorithms and increased access to satellite data, generating up-to-date, accurate land use/land cover (LULC) maps has become increasingly feasible for evaluating and managing changes in land cover as created by changes to ecosystem and land use. The main objective of our study is to evaluate the performance of Support Vector Machine (SVM), Artificial Neural Network (ANN), Maximum Likelihood Classification (MLC), Minimum Distance (MD), and Mahalanobis (MH) algorithms and compare them in order to generate a LULC map using data from Sentinel 2 and Landsat 8 satellites. Further, we also investigate the effect of a penalty parameter on SVM results. Our study uses different kernel functions and hidden layers for SVM and ANN algorithms, respectively. We generated the training and validation datasets from Google Earth images and GPS data prior to pre-processing satellite data. In the next phase, we classified the images using training data and algorithms. Ultimately, to evaluate outcomes, we used the validation data to generate a confusion matrix of the classified images. Our results showed that with optimal tuning parameters, the SVM classifier yielded the highest overall accuracy (OA) of 94%, performing better for both satellite data compared to other methods. In addition, for our scenes, Sentinel 2 date was slightly more accurate compared to Landsat 8. The parametric algorithms MD and MLC provided the lowest accuracy of 80.85% and 74.68% for the data from Sentinel 2 and Landsat 8. In contrast, our evaluation using the SVM tuning parameters showed that the linear kernel with the penalty parameter 150 for Sentinel 2 and the penalty parameter 200 for Landsat 8 yielded the highest accuracies. Further, ANN classification showed that increasing the hidden layers drastically reduces classification accuracy for both datasets, reducing zero for three hidden layers.
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| 6. |
- Mohammadi, Ayub, et al.
(författare)
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A Multi-Sensor Comparative Analysis on the Suitability of Generated DEM from Sentinel-1 SAR Interferometry Using Statistical and Hydrological Models
- 2020
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Ingår i: Sensors. - Switzerland : MDPI. - 1424-8220. ; 20:24
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Tidskriftsartikel (refereegranskat)abstract
- Digital elevation model (DEM) plays a vital role in hydrological modelling and environmental studies. Many essential layers can be extracted from this land surface information, including slope, aspect, rivers, and curvature. Therefore, DEM quality and accuracy will affect the extracted features and the whole process of modeling. Despite freely available DEMs from various sources, many researchers generate this information for their areas from various observations. Sentinal-1 synthetic aperture radar (SAR) images are among the best Earth observations for DEM generation thanks to their availabilities, high-resolution, and C-band sensitivity to surface structure. This paper presents a comparative study, from a hydrological point of view, on the quality and reliability of the DEMs generated from Sentinel-1 data and DEMs from other sources such as AIRSAR, ALOS-PALSAR, TanDEM-X, and SRTM. To this end, pair of Sentinel-1 data were acquired and processed using the SAR interferometry technique to produce a DEM for two different study areas of a part of the Cameron Highlands, Pahang, Malaysia, a part of Sanandaj, Iran. Based on the estimated linear regression and standard errors, generating DEM from Sentinel-1 did not yield promising results. The river streams for all DEMs were extracted using geospatial analysis tool in a geographic information system (GIS) environment. The results indicated that because of the higher spatial resolution (compared to SRTM and TanDEM-X), more stream orders were delineated from AIRSAR and Sentinel-1 DEMs. Due to the shorter perpendicular baseline, the phase decorrelation in the created DEM resulted in a lot of noise. At the same time, results from ground control points (GCPs) showed that the created DEM from Sentinel-1 is not promising. Therefore, other DEMs’ performance, such as 90-meters’ TanDEM-X and 30-meters’ SRTM, are better than Sentinel-1 DEM (with a better spatial resolution).
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| 7. |
- Mohammadi, Ayub, et al.
(författare)
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Flood Detection and Susceptibility Mapping Using Sentinel-1 Time Series, Alternating Decision Trees, and Bag-ADTree Models
- 2020
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Ingår i: Complexity. - London : Hindawi Publishing Corporation. - 1076-2787 .- 1099-0526. ; 2020
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Tidskriftsartikel (refereegranskat)abstract
- Flooding is one of the most damaging natural hazards globally. During the past three years, floods have claimed hundreds of lives and millions of dollars of damage in Iran. In this study, we detected flood locations and mapped areas susceptible to floods using time series satellite data analysis as well as a new model of bagging ensemble-based alternating decision trees, namely, bag-ADTree. We used Sentinel-1 data for flood detection and time series analysis. We employed twelve conditioning parameters of elevation, normalized difference’s vegetation index, slope, topographic wetness index, aspect, curvature, stream power index, lithology, drainage density, proximities to river, soil type, and rainfall for mapping areas susceptible to floods. ADTree and bag-ADTree models were used for flood susceptibility mapping. We used software of Sentinel application platform, Waikato Environment for Knowledge Analysis, ArcGIS, and Statistical Package for the Social Sciences for preprocessing, processing, and postprocessing of the data. We extracted 199 locations as flooded areas, which were tested using a global positioning system to ensure that flooded areas were detected correctly. Root mean square error, accuracy, and the area under the ROC curve were used to validate the models. Findings showed that root mean square error was 0.31 and 0.3 for ADTree and bag-ADTree techniques, respectively. More findings illustrated that accuracy was obtained as 86.61 for bag-ADTree model, while it was 85.44 for ADTree method. Based on AUC, success and prediction rates were 0.736 and 0.786 for bag-ADTree algorithm, in order, while these proportions were 0.714 and 0.784 for ADTree. This study can be a good source of information for crisis management in the study area.
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| 8. |
- Nhu, Viet-Ha, et al.
(författare)
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Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran
- 2020
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Ingår i: Applied Sciences. - Switzerland : MDPI. - 2076-3417. ; 10:15
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Tidskriftsartikel (refereegranskat)abstract
- This paper aims to apply and compare the performance of the three machine learningalgorithms–support vector machine (SVM), bayesian logistic regression (BLR), and alternatingdecision tree (ADTree)–to map landslide susceptibility along the mountainous road of the SalavatAbad saddle, Kurdistan province, Iran. We identified 66 shallow landslide locations, based on fieldsurveys, by recording the locations of the landslides by a global position System (GPS), Google Earthimagery and black-and-white aerial photographs (scale 1: 20,000) and 19 landslide conditioningfactors, then tested these factors using the information gain ratio (IGR) technique. We checked thevalidity of the models using statistical metrics, including sensitivity, specificity, accuracy, kappa,root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC).We found that, although all three machine learning algorithms yielded excellent performance, theSVM algorithm (AUC=0.984) slightly outperformed the BLR (AUC=0.980), and ADTree (AUC=0.977) algorithms. We observed that not only all three algorithms are useful and effective tools foridentifying shallow landslide-prone areas but also the BLR algorithm can be used such as the SVMalgorithm as a soft computing benchmark algorithm to check the performance of the models in future.
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| 9. |
- Nhu, Viet-Ha, et al.
(författare)
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Daily Water Level Prediction of Zrebar Lake (Iran) : A Comparison between M5P, Random Forest, Random Tree and Reduced Error Pruning Trees Algorithms
- 2020
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Ingår i: ISPRS International Journal of Geo-Information. - Switzerland : MDPI. - 2220-9964. ; 9:8
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Tidskriftsartikel (refereegranskat)abstract
- Zrebar Lake is one of the largest freshwater lakes in Iran and it plays an important role in the ecosystem of the environment, while its desiccation has a negative impact on the surrounded ecosystem. Despite this, this lake provides an interesting recreation setting in terms of ecotourism. The prediction and forecasting of the water level of the lake through simple but practical methods can provide a reliable tool for future lake water resource management. In the present study, we predict the daily water level of Zrebar Lake in Iran through well-known decision tree-based algorithms, including the M5 pruned (M5P), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). We used five different water input combinations to find the most effective one. For our modeling, we chose 70% of the dataset for training (from 2011 to 2015) and 30% for model evaluation (from 2015 to 2017). We evaluated the models’ performances using different quantitative (root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), percent bias (PBIAS) and ratio of the root mean square error to the standard deviation of measured data (RSR)) and visual frameworks (Taylor diagram and box plot). Our results showed that water level with a one-day lag time had the highest effect on the result and, by increasing the lag time, its effect on the result was decreased. This result indicated that all the developed models had a good prediction capability, but the M5P model outperformed the others, followed by RF and RT equally and then REPT. Our results showed that these algorithms can predict water level accurately only with a one-day lag time in water level as an input and they are cost-effective tools for future predictions.
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| 10. |
- Nhu, Viet-Ha, et al.
(författare)
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Landslide Detection and Susceptibility Modeling on Cameron Highlands (Malaysia) : A Comparison between Random Forest, Logistic Regression and Logistic Model Tree Algorithms
- 2020
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Ingår i: Forests. - Switzerland : MDPI. - 1999-4907 .- 1999-4907. ; 11:8
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Tidskriftsartikel (refereegranskat)abstract
- We used remote sensing techniques and machine learning to detect and map landslides,and landslide susceptibility in the Cameron Highlands, Malaysia. We located 152 landslides using a combination of interferometry synthetic aperture radar (InSAR), Google Earth (GE), and field surveys. Of the total slide locations, 80% (122 landslides) were utilized for training the selected algorithms, and the remaining 20% (30 landslides) were applied for validation purposes. We employed 17 conditioning factors, including slope angle, aspect, elevation, curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), lithology, soil type, land cover, normalized difference vegetation index (NDVI), distance to river, distance to fault, distance to road, river density, fault density, and road density, which were produced from satellite imageries, geological map, soil maps, and a digital elevation model (DEM). We used these factors to produce landslide susceptibility maps using logistic regression (LR), logistic model tree (LMT), and random forest (RF) models. To assess prediction accuracy of the models we employed the following statistical measures: negative predictive value (NPV), sensitivity, positive predictive value (PPV), specificity, root-mean-squared error (RMSE), accuracy, and area under the receiver operating characteristic (ROC) curve (AUC). Our results indicated that the AUC was 92%, 90%, and 88% for the LMT, LR, and RF algorithms, respectively. To assess model performance, we also applied onparametric statistical tests of Friedman and Wilcoxon, where the results revealed that there were no practical differences among the used models in the study area. While landslide mapping in tropical environment such as Cameron Highlands remains difficult, the remote sensing (RS) along with machine learning techniques, such as the LMT model, show promise for landslide susceptibility mapping in the study area.
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