Conditioning factors determination for landslide susceptibility mapping using support vector machine learning

This study investigates the effectiveness of two sets of landslide conditioning variable(s). Fourteen landslide conditioning variables were considered in this study where they were duly divided into two sets G1 and G2. Two Support Vector Machine (SVM) classifiers were constructed based on each dataset (SVM-G1 and SVM-G2) in order to determine which set would be more suitable for landslide susceptibility prediction. In total, 160 landslide inventory datasets of the study area were used where 70% was used for SVM training and 30% for testing. The intra-relationships between parameters were explored based on variance inflation factors (VIF), Pearson's correlation and Cohen's kappa analysis. Other evaluation metrics are the area under curve (AUC)

A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides

This research aims at proposing a new artificial intelligence approach (namely RVM-ICA) which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive Algorithm (ICA) optimization for landslide susceptibility modeling. A Geographic Information System (GIS) spatial database was generated from Lang Son city in Lang Son province (Vietnam). This GIS database includes a landslide inventory map and fourteen landslide conditioning factors. The suitability of these factors for landslide susceptibility modeling in the study area was verified by the Information Gain Ratio (IGR) technique. A landslide susceptibility prediction model based on RVM-ICA and the GIS database was established by training and prediction phases. The predictive capability of the new approach was evaluated by calculations of sensitivity, specificity, accuracy, and the area under the Receiver Operating Characteristic curve (AUC). In addition, to assess the applicability of the proposed model, two state-of-the-art soft computing techniques including the support vector machine (SVM) and logistic regression (LR) were used as benchmark methods. The results of this study show that RVM-ICA with AUC = 0.92 achieved a high goodness-of-fit based on both the training and testing datasets. The predictive capability of RVM-ICA outperformed those of SVM with AUC = 0.91 and LR with AUC = 0.87. The experimental results confirm that the newly proposed model is a very promising alternative to assist planners and decision makers in the task of managing landslide prone areas

Geo-spatial Technology for Landslide Hazard Zonation and Prediction

Similar to other geo hazards, landslides cannot be avoided in mountainous terrain. It is the most common natural hazard in the mountain regions and can result in enormous damage to both property and life every year. Better understanding of the hazard will help people to live in harmony with the pristine nature. Since India has 15% of its land area prone to landslides, preparation of landslide susceptibility zonation (LSZ) maps for these areas is of utmost importance. These susceptibility zonation maps will give the areas that are prone to landslides and the safe areas, which in-turn help the administrators for safer planning and future development activities. There are various methods for the preparation of LSZ maps such as based on Fuzzy logic, Artificial Neural Network, Discriminant Analysis, Direct Mapping, Regression Analysis, Neuro-Fuzzy approach and other techniques. These different approaches apply different rating system and the weights, which are area and factors dependent. Therefore, these weights and ratings play a vital role in the preparation of susceptibility maps using any of the approach. However, one technique that gives very high accuracy in certain might not be applicable to other parts of the world due to change in various factors, weights and ratings. Hence, only one method cannot be suggested to be applied in any other terrain. Therefore, an understanding of these approaches, factors and weights needs to be enhanced so that their execution in Geographic Information System (GIS) environment could give better results and yield actual ground like scenarios for landslide susceptibility mapping. Hence, the available and applicable approaches are discussed in this chapter along with detailed account of the literature survey in the areas of LSZ mapping. Also a case study of Garhwal area where Support Vector Machine (SVM) technique is used for preparing LSZ is also given. These LSZ maps will also be an important input for preparing the risk assessment of LSZ

Random forest-based landslide susceptibility mapping in coastal regions of Artvin, Turkey

Natural disasters such as landslides often occur in the Eastern Black Sea region of Turkey owing to its geological, topographical, and climatic characteristics. Landslide events occur nearly every year in the Arhavi, Hopa, and Kemalpa¸sa districts located on the Black Sea coast in the Artvin province. In this study, the landslide susceptibility map of the Arhavi, Hopa, and Kemalpa¸sa districts was produced using the random forest (RF) model, which is widely used in the literature and yields more accurate results compared with other machine learning techniques. A total of 10 landslide-conditioning factors were considered for the susceptibility analysis, i.e., lithology, land cover, slope, aspect, elevation, curvature, topographic wetness index, and distances from faults, drainage networks, and roads. Furthermore, 70% of the landslides on the landslide inventory map were used for training, and the remaining 30% were used for validation. The RF-based model was validated using the area under the receiver operating characteristic (ROC) curve. Evaluation results indicated that the success and prediction rates of the model were 98.3% and 97.7%, respectively. Moreover, it was determined that incorrect land-use decisions, such as transforming forest areas into tea and hazelnut cultivation areas, induce the occurrence of landslides.Funding: This research was supported by the Scientific Research Projects Office of Artvin Çoruh University (AÇÜBAP) (Scientific Research Project No. 2016.F40.02.05)

Landslide Susceptibility Mapping Using the Stacking Ensemble Machine Learning Method in Lushui, Southwest China

Landslide susceptibility mapping is considered to be a prerequisite for landslide prevention and mitigation. However, delineating the spatial occurrence pattern of the landslide remains a challenge. This study investigates the potential application of the stacking ensemble learning technique for landslide susceptibility assessment. In particular, support vector machine (SVM), artificial neural network (ANN), logical regression (LR), and naive Bayes (NB) were selected as base learners for the stacking ensemble method. The resampling scheme and Pearson’s correlation analysis were jointly used to evaluate the importance level of these base learners. A total of 388 landslides and 12 conditioning factors in the Lushui area (Southwest China) were used as the dataset to develop landslide modeling. The landslides were randomly separated into two parts, with 70% used for model training and 30% used for model validation. The models’ performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) and statistical measures. The results showed that the stacking-based ensemble model achieved an improved predictive accuracy as compared to the single algorithms, while the SVM-ANN-NB-LR (SANL) model, the SVM-ANN-NB (SAN) model, and the ANN-NB-LR (ANL) models performed equally well, with AUC values of 0.931, 0.940, and 0.932, respectively, for validation stage. The correlation coefficient between the LR and SVM was the highest for all resampling rounds, with a value of 0.72 on average. This connotes that LR and SVM played an almost equal role when the ensemble of SANL was applied for landslide susceptibility analysis. Therefore, it is feasible to use the SAN model or the ANL model for the study area. The finding from this study suggests that the stacking ensemble machine learning method is promising for landslide susceptibility mapping in the Lushui area and is capable of targeting areas prone to landslides

Evaluation of the landslide susceptibility and its spatial difference in the whole Qinghai-Tibetan Plateau region by five learning algorithms

AbstractLandslides are considered as major natural hazards that cause enormous property damages and fatalities in Qinghai-Tibetan Plateau (QTP). In this article, we evaluated the landslide susceptibility, and its spatial differencing in the whole Qinghai-Tibetan Plateau region using five state-of-the-art learning algorithms; deep neural network (DNN), logistic regression (LR), Naïve Bayes (NB), random forest (RF), and support vector machine (SVM), differing from previous studies only in local areas of QTP. The 671 landslide events were considered, and thirteen landslide conditioning factors (LCFs) were derived for database generation, including annual rainfall, distance to drainage $${(\mathrm{Ds}}_{\mathrm{d}})$$ ( Ds d ) , distance to faults $${(\mathrm{Ds}}_{\mathrm{f}})$$ ( Ds f ) , drainage density ($${D}_{d})$$ D d ) , elevation (Elev), fault density $$({F}_{d})$$ ( F d ) , lithology, normalized difference vegetation index (NDVI), plan curvature $${(\mathrm{Pl}}_{\mathrm{c}})$$ ( Pl c ) , profile curvature $${(\mathrm{Pr}}_{\mathrm{c}})$$ ( Pr c ) , slope $${(S}^{^\circ })$$ ( S ∘ ) , stream power index (SPI), and topographic wetness index (TWI). The multi-collinearity analysis and mean decrease Gini (MDG) were used to assess the suitability and predictability of these factors. Consequently, five landslide susceptibility prediction (LSP) maps were generated and validated using accuracy, area under the receiver operatic characteristic curve, sensitivity, and specificity. The MDG results demonstrated that the rainfall, elevation, and lithology were the most significant landslide conditioning factors ruling the occurrence of landslides in Qinghai-Tibetan Plateau. The LSP maps depicted that the north-northwestern and south-southeastern regions ( 45% of total area). Moreover, among the five models with a high goodness-of-fit, RF model was highlighted as the superior one, by which higher accuracy of landslide susceptibility assessment and better prone areas management in QTP can be achieved compared to previous results. Graphical Abstrac

Predicting landslide susceptibility and risks using GIS-based machine learning simulations, case of upper Nyabarongo catchment

Sustainable landslide mitigation requires appropriate approaches to predict susceptible zones. This study compared the performance of Logistic Model Tree (LMT), Random Forest (RF) and Naïve-Bayes Tree (NBT) in predicting landslide susceptibility for the upper Nyabarongo catchment (Rwanda). 196 past landslides were mapped using field investigations. Thus, the inventory map was split into training and testing datasets. Fifteen predisposing factors were analysed and information gain (IG) technique was used to analyse the correlation between factors and observed landslides. Therefore, the area under receiver operating characteristic (AUROC) with other statistical estimators including accuracy, precision, and root mean square error (RMSE) were employed to compare the models. The AUC values were 78.7%, 80.9% and 82.4% for RF, LMT and NBT models, respectively. Additionally, the NBT produced the highest accuracy and precision values (0.799 and 0.745, respectively). Regarding RMSE values, the NBT model achieved an optimized prediction than RF and LMT models (0.301; 0.428 and 0.364, respectively). The results of the current study may inform further studies and appropriate landslide risk reduction and mitigation measures. They can also be instrumental for policy and decision making in regards with natural risk management

A novel ensemble artificial intelligence approach for gully erosion mapping in a semi-arid watershed (Iran)

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811)

Hybrid machine learning approach for gully erosion mapping susceptibility at a watershed scale

Gully erosion is a serious threat to the state of ecosystems all around the world. As a result, safeguarding the soil for our own benefit and from our own actions is a must for guaranteeing the longterm viability of a variety of ecosystem services. As a result, developing gully erosion susceptibility maps (GESM) is both suggested and necessary. In this study, we compared the effectiveness of three hybrid machine learning (ML) algorithms with the bivariate statistical index frequency ratio (FR), named random forest-frequency ratio (RF-FR), support vector machine-frequency ratio (SVM-FR), and naïve Bayes-frequency ratio (NB-FR), in mapping gully erosion in the GHISS watershed in the northern part of Morocco. The models were implemented based on the inventory mapping of a total number of 178 gully erosion points randomly divided into 2 groups (70% of points were used for training the models and 30% of points were used for the validation process), and 12 conditioning variables (i.e., elevation, slope, aspect, plane curvature, topographic moisture index (TWI), stream power index (SPI), precipitation, distance to road, distance to stream, drainage density, land use, and lithology). Using the equal interval reclassification method, the spatial distribution of gully erosion was categorized into five different classes, including very high, high, moderate, low, and very low. Our results showed that the very high susceptibility classes derived using RF-FR, SVM-FR, and NB-FR models covered 25.98%, 22.62%, and 27.10% of the total area, respectively. The area under the receiver (AUC) operating characteristic curve, precision, and accuracy were employed to evaluate the performance of these models. Based on the receiver operating characteristic (ROC), the results showed that the RF-FR achieved the best performance (AUC = 0.91), followed by SVM-FR (AUC = 0.87), and then NB-FR (AUC = 0.82), respectively. Our contribution, in line with the Sustainable Development Goals (SDGs), plays a crucial role for understanding and identifying the issue of “where and why” gully erosion occurs, and hence it can serve as a first pathway to reducing gully erosion in this particular area

A Novel Hybrid Machine Learning-Based Model for Rockfall Source Identification in Presence of Other Landslide Types Using LiDAR and GIS

© 2019, King Abdulaziz University and Springer Nature Switzerland AG. Abstract: Rockfall is a common phenomenon in mountainous and hilly areas worldwide, including Malaysia. Rockfall source identification is a challenging task in rockfall hazard assessment. The difficulty rise when the area of interest has other landslide types with nearly similar controlling factors. Therefore, this research presented and assessed a hybrid model for rockfall source identification based on the stacking ensemble model of random forest (RF), artificial neural network, Naive Bayes (NB), and logistic regression in addition to Gaussian mixture model (GMM) using high-resolution airborne laser scanning data (LiDAR). GMM was adopted to automatically compute the thresholds of slope angle for various landslide types. Chi square was utilised to rank and select the conditioning factors for each landslide type. The best fit ensemble model (RF–NB) was then used to produce probability maps, which were used to conduct rockfall source identification in combination with the reclassified slope raster based on the thresholds obtained by the GMM. Next, landslide potential area was structured to reduce the sensitivity and the noise of the model to the variations in different conditioning factors for improving its computation performance. The accuracy assessment of the developed model indicates that the model can efficiently identify probable rockfall sources with receiver operating characteristic curve accuracies of 0.945 and 0.923 on validation and training datasets, respectively. In general, the proposed hybrid model is an effective model for rockfall source identification in the presence of other landslide types with a reasonable generalisation performance. Graphic Abstract: [Figure not available: see fulltext.]