scholarly journals Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with with GWO, BAT and COA algorithms

2020 ◽  
Author(s):  
Abdul-Lateef Balogun ◽  
Fatemeh Rezaie ◽  
Quoc Bao Pham ◽  
Ljubomir Gigović ◽  
Siniša Drobnjak ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Landslide Susceptibility ◽  
Spatial Prediction ◽  
Support Vector

scholarly journals Hybrids of Support Vector Regression with Grey Wolf Optimizer and Firefly Algorithm for Spatial Prediction of Landslide Susceptibility

2021 ◽  
Vol 13 (24) ◽  
pp. 4966
Author(s):  
Ru Liu ◽  
Jianbing Peng ◽  
Yanqiu Leng ◽  
Saro Lee ◽  
Mahdi Panahi ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Firefly Algorithm ◽  
Spatial Prediction ◽  
Training Dataset ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Conditioning Factors

Landslides are one of the most frequent and important natural disasters in the world. The purpose of this study is to evaluate the landslide susceptibility in Zhenping County using a hybrid of support vector regression (SVR) with grey wolf optimizer (GWO) and firefly algorithm (FA) by frequency ratio (FR) preprocessed. Therefore, a landslide inventory composed of 140 landslides and 16 landslide conditioning factors is compiled as a landslide database. Among these landslides, 70% (98) landslides were randomly selected as the training dataset of the model, and the other landslides (42) were used to verify the model. The 16 landslide conditioning factors include elevation, slope, aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, sediment transport index (STI), stream power index (SPI), topographic wetness index (TWI), normalized difference vegetation index (NDVI), landslide, rainfall, soil and lithology. The conditioning factors selection and spatial correlation analysis were carried out by using the correlation attribute evaluation (CAE) method and the frequency ratio (FR) algorithm. The area under the receiver operating characteristic curve (AUROC) and kappa data of the training dataset and validation dataset are used to evaluate the prediction ability and the relationship between the advantages and disadvantages of landslide susceptibility maps. The results show that the SVR-GWO model (AUROC = 0.854) has the best performance in landslide spatial prediction, followed by the SVR-FA (AUROC = 0.838) and SVR models (AUROC = 0.818). The hybrid models of SVR-GWO and SVR-FA improve the performance of the single SVR model, and all three models have good prospects for regional-scale landslide spatial modeling.

Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models

Geoderma ◽  
2021 ◽  
Vol 383 ◽  
pp. 114793
Author(s):  
Ruhollah Taghizadeh-Mehrjardi ◽  
Karsten Schmidt ◽  
Norair Toomanian ◽  
Brandon Heung ◽  
Thorsten Behrens ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Soil Salinity ◽  
Regression Models ◽  
Wavelet Transformation ◽  
Arid Regions ◽  
Spatial Prediction ◽  
Support Vector

scholarly journals A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility

Forests ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 118 ◽  
Author(s):  
Viet-Hung Dang ◽  
Nhat-Duc Hoang ◽  
Le-Mai-Duyen Nguyen ◽  
Dieu Tien Bui ◽  
Pijush Samui
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Landslide Susceptibility ◽  
Spatial Prediction ◽  
Shallow Landslide ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Conditioning Factors ◽  
Susceptibility Modeling

This study developed and verified a new hybrid machine learning model, named random forest machine (RFM), for the spatial prediction of shallow landslides. RFM is a hybridization of two state-of-the-art machine learning algorithms, random forest classifier (RFC) and support vector machine (SVM), in which RFC is used to generate subsets from training data and SVM is used to build decision functions for these subsets. To construct and verify the hybrid RFM model, a shallow landslide database of the Lang Son area (northern Vietnam) was prepared. The database consisted of 101 shallow landslide polygons and 14 conditioning factors. The relevance of these factors for shallow landslide susceptibility modeling was assessed using the ReliefF method. Experimental results pointed out that the proposed RFM can help to achieve the desired prediction with an F1 score of roughly 0.96. The performance of the RFM was better than those of benchmark approaches, including the SVM, RFC, and logistic regression. Thus, the newly developed RFM is a promising tool to help local authorities in shallow landslide hazard mitigations.

Landslide Susceptibility Analysis using Gradient Boosting Models: A Case Study in Penang Island, Malaysia

2021 ◽  
pp. 22-37
Author(s):  
Han Gao ◽  
Pei Shan Fam ◽  
Lea Tien Tay ◽  
Heng Chin Low
Keyword(s):  
Feature Selection ◽  
Landslide Susceptibility ◽  
Roc Curves ◽  
Spatial Prediction ◽  
Prediction Performance ◽  
Gradient Boosting ◽  
Support Vector ◽  
Prediction Ability ◽  
Light Gradient ◽  
Extreme Gradient Boosting

Tree-based gradient boosting (TGB) models gain popularity in various areas due to their powerful prediction ability and fast processing speed. This study aims to compare the landslide spatial prediction performance of TGB models and non-tree-based machine learning (NML) models in Penang Island, Malaysia. Two specific instances of TGB models, eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) and two specific instances of NML models, artificial neural network (ANN) and support vector machine (SVM), are applied to make predictions of landslide susceptibility. Feature selection and oversampling techniques are considered to improve the prediction performance as well. The results are analyzed and discussed mainly based on receiver operating characteristic (ROC) curves as well as the area under the curves (AUC). The results show that TGB models give better prediction performance compared to NML models, no matter what the sample size is. The TGB models’ performances are improved when training with the dataset considering either feature selection or oversampling techniques. The highest AUC value of 0.9525 is obtained from the combination of XGBoost and SMOTE. The landslide susceptibility maps (LSMs) produced by XGBoost and LightGBM can provide valuable information in landslide management and mitigation in Penang Island, Malaysia.

Flood spatial prediction modeling using a hybrid of meta-optimization and support vector regression modeling

CATENA ◽  
2021 ◽  
Vol 199 ◽  
pp. 105114
Author(s):  
Mahdi Panahi ◽  
Esmaeel Dodangeh ◽  
Fatemeh Rezaie ◽  
Khabat Khosravi ◽  
Hiep Van Le ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Spatial Prediction ◽  
Regression Modeling ◽  
Support Vector ◽  
Prediction Modeling ◽  
Meta Optimization

scholarly journals Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS

2018 ◽  
Vol 10 (10) ◽  
pp. 3434 ◽  
Author(s):  
Omer Azeez ◽  
Biswajeet Pradhan ◽  
Helmi Shafri
Keyword(s):  
Support Vector Regression ◽  
Urban Areas ◽  
Road Traffic ◽  
Spatial Prediction ◽  
Absolute Error ◽  
Support Vector ◽  
Vehicular Emissions ◽  
Support Vector Regression Model ◽  
Promising Tool ◽  
Proposed Model

Transportation infrastructures play a significant role in the economy as they provide accessibility services to people. Infrastructures such as highways, road networks, and toll plazas are rapidly growing based on changes in transportation modes, which consequently create congestions near toll plaza areas and intersections. These congestions exert negative impacts on human health and the environment because vehicular emissions are considered as the main source of air pollution in urban areas and can cause respiratory and cardiovascular diseases and cancer. In this study, we developed a hybrid model based on the integration of three models, correlation-based feature selection (CFS), support vector regression (SVR), and GIS, to predict vehicular emissions at specific times and locations on roads at microscale levels in an urban areas of Kuala Lumpur, Malaysia. The proposed model comprises three simulation steps: first, the selection of the best predictors based on CFS; second, the prediction of vehicular carbon monoxide (CO) emissions using SVR; and third, the spatial simulation based on maps by using GIS. The proposed model was developed with seven road traffic CO predictors selected via CFS (sum of vehicles, sum of heavy vehicles, heavy vehicle ratio, sum of motorbikes, temperature, wind speed, and elevation). Spatial prediction was conducted based on GIS modelling. The vehicular CO emissions were measured continuously at 15 min intervals (recording 15 min averages) during weekends and weekdays twice per day (daytime, evening-time). The model’s results achieved a validation accuracy of 80.6%, correlation coefficient of 0.9734, mean absolute error of 1.3172 ppm and root mean square error of 2.156 ppm. In addition, the most appropriate parameters of the prediction model were selected based on the CFS model. Overall, the proposed model is a promising tool for traffic CO assessment on roads.

scholarly journals Development of a Novel Hybrid Intelligence Approach for Landslide Spatial Prediction

2019 ◽  
Vol 9 (14) ◽  
pp. 2824 ◽  
Author(s):  
Nguyen ◽  
Tuyen ◽  
Shirzadi ◽  
Pham ◽  
Shahabi ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Naive Bayes ◽  
Spatial Prediction ◽  
Absolute Error ◽  
Naïve Bayes ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Operating Characteristics ◽  
Conditioning Factors ◽  
Hybrid Intelligence

We proposed an innovative hybrid intelligent approach, namely, the multiboost based naïve bayes trees (MBNBT) method for the spatial prediction of landslides in the Mu Cang Chai District of Yen Bai Province, Vietnam. The MBNBT, which is an ensemble of the multiboost (MB) and naïve bayes trees (NBT) base classifier, has rarely been applied for landslide susceptibility mapping around the world. For the modeling, we selected 248 landslide locations in the hilly terrain of the study area. Fifteen landslide conditioning factors were selected for the construction of the database based on the one-R attribute evaluation (ORAE) technique. Model validation was done using statistical metrics, namely, sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and the area under the receiver operating characteristics curve (AUC). Performance of the hybrid model was evaluated and compared with popular soft computing benchmark models, namely, multiple perceptron neural network (MLPN), Support Vector Machines (SVM), and single NBT. Results indicated that the proposed MBNBT (AUC = 0.824) model outperformed the popular models, namely, the MLPN (AUC = 0.804), SVM (AUC = 0.804), and NBT (AUC = 0.800) models. Analysis of the model results also suggested that the MB meta classifier ensemble model could enhance the prediction power of the NBT model. Therefore, the MBNBT is a suitable method for the assessment of landslide susceptibility in landslide prone areas.

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