Haoyuan Hong

Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy

Abstract The main objective of the study was to evaluate and compare the overall performance of three methods, frequency ratio (FR), certainty factor (CF) and index of entropy (IOE), for rainfall-induced landslide susceptibility mapping at the Chongren area (China) using geographic information system and remote sensing. First, a landslide inventory map for the study area was constructed from field surveys and interpretations of aerial photographs. Second, 15 landslide-related factors such as elevation, slope, aspect, plan curvature, profile curvature, stream power index, sediment transport index, topographic wetness index, distance to faults, distance to rivers, distance to roads, landuse, NDVI, lithology and rainfall were prepared for the landslide susceptibility modelling. Using these data, three landslide susceptibility models were constructed using FR, CF and IOE. Finally, these models were validated and compared using known landslide locations and the receiver operating characteristics curve. The result shows that all the models perform well on both the training and validation data. The area under the curve showed that the goodness-of-fit with the training data is 79.12, 80.34 and 80.42% for FR, CF and IOE whereas the prediction power is 80.14, 81.58 and 81.73%, for FR, CF and IOE, respectively. The result of this study may be useful for local government management and land use planning.

GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models

ABSTRACT The main purpose of this paper is to explore some potential applications of sophisticated machine learning techniques such as the kernel logistic regression, Naïve-Bayes tree and alternating decision tree models for landslide susceptibility analysis at Taibai county (China). Initially, a landslide inventory map containing the information of 212 historical landslide locations was prepared. Seventy percentage (148) of landslides were randomly selected for training models and the remaining were used for validation. Additionally, 12 landslide conditioning factors were considered and the thematic layers were prepared in GIS. Subsequently, these three models were applied to build landslide susceptibility maps. The performances of the models were compared using the receive operating characteristic curves, kappa index, and statistical evaluation measures. The results show that the KLR model has the highest AUC values of 0.910 and 0.936 for training and validation datasets, respectively. The KLR model also has the highest degree of goodness-of-fits (84.5%) for the training dataset. The NBTree model has the highest goodness-of-fits (91.4%) for the validation dataset. However, the KLR model has the preferable balance performance for both the training and validation process. The results of this study demonstrate the benefit of selecting the optimal machine learning techniques in landslide susceptibility mapping.

A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China

ABSTRACT The main objective of this study was to produce landslide susceptibility maps for Langao County, China, using a novel hybrid artificial intelligence method based on rotation forest ensembles (RFEs) and naïve Bayes tree (NBT) classifiers labeled the RF-NBT model. The spatial database consisted of eighteen conditioning factors that were selected using the information gain ratio (IGR) method. The model was evaluated using quantitative statistical criteria, including the sensitivity, specificity, accuracy, root mean squared error (RMSE), and area under the receiver operating characteristic curve (AUC). Furthermore, the new model was compared with the NBT, functional tree (FT), logistic model tree (LMT) and reduced-error pruning tree (REPTree) soft computing benchmark models. The findings indicated that the RF-NBT model showed an increased prediction accuracy relative to the NBT model using both the training and validation datasets, and the RF-NBT model exhibited a greater capability for landslide susceptibility mapping. The new RF-NBT model also showed the most preferable results compared with the FT, LMT and REPTree models. Finally, an analysis of the landslide density (LD) using the RF-NBT model demonstrated that the very high susceptibility (VHS) class had the highest LD (3.552) among the landslide susceptibility maps. These results can be used for the planning and management of areas vulnerable to landslides in order to prevent damages caused by such natural disasters.

Comparison of four kernel functions used in support vector machines for landslide susceptibility mapping: a case study at Suichuan area (China)

ABSTRACT Suichuan is a mountainous area at the Jiangxi province in Central China, where rainfall-induced landslides occur frequently. The purpose of this study is to assess landslide susceptibility of this region using support vector machine (SVM) with four kernel functions: polynomial (PL), radial basis function (RBF), sigmoid (SIG), and linear (LN). A total of 178 landslides were used to accomplish this approach, of which, 125 (70%) landslides were randomly selected for training the landslide susceptibility models, whereas the remaining 53 (30%) were used for the model validation. Fifteen landslide conditioning factors were considered including slope-angle, altitude, slope-aspect, topographic wetness index (TWI), sediment transport index (STI), stream power index (SPI), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, precipitation, landuse, normalized difference vegetation index (NDVI), and lithology. Using the training dataset, nine landslide susceptibility models for the Suichuan area were constructed with the four kernel functions. To evaluate the performance of these models, the receiver-operating characteristic curve (ROC) and area under the curve (AUC) were used. Using the training dataset, AUC values for the SVM-PL models with six degrees PL function (1–6) are 0.715, 0.801, 0.856, 0.891, 0.919, 0.953, respectively, and for the SVM-RBF model, the SVM-SIG model, and the SVM-LN model are 0.716, 0.741, and 0.740, respectively. Using the validation dataset, AUC values for the SVM-PL models with six degrees PL function (1–6) are 0.738, 0.730, 0.683, 0.648, 0.608, and 0.598, respectively, and for the SVM-RBF model, the SVM-SIG model, and the SVM-LN model are 0.716, 0.741, and 0.740, respectively. Our results suggested that the SVM-RBF model is the most suitable for landslide susceptibility assessment for the study area.

Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China

In China, floods are considered as the most frequent natural disaster responsible for severe economic losses and serious damages recorded in agriculture and urban infrastructure. Based on the international experience prevention of flood events may not be completely possible, however identifying susceptible and vulnerable areas through prediction models is considered as a more visible task with flood susceptibility mapping being an essential tool for flood mitigation strategies and disaster preparedness. In this context, the present study proposes a novel approach to construct a flood susceptibility map in the Poyang County, JiangXi Province, China by implementing fuzzy weight of evidence (fuzzy-WofE) and data mining methods. The novelty of the presented approach is the usage of fuzzy-WofE that had a twofold purpose. Firstly, to create an initial flood susceptibility map in order to identify non-flood areas and secondly to weight the importance of flood related variables which influence flooding. Logistic Regression (LR), Random Forest (RF) and Support Vector Machines (SVM) were implemented considering eleven flood related variables, namely: lithology, soil cover, elevation, slope angle, aspect, topographic wetness index, stream power index, sediment transport index, plan curvature, profile curvature and distance from river network. The efficiency of this new approach was evaluated using area under curve (AUC) which measured the prediction and success rates. According to the outcomes of the performed analysis, the fuzzy WofE-SVM model was the model with the highest predictive performance (AUC value, 0.9865) which also appeared to be statistical significant different from the other predictive models, fuzzy WofE-RF (AUC value, 0.9756) and fuzzy WofE-LR (AUC value, 0.9652). The proposed methodology and the produced flood susceptibility map could assist researchers and local governments in flood mitigation strategies.

A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment

Abstract This study addresses landslide susceptibility mapping (LSM) using a novel ensemble approach of using a bivariate statistical method (weights of evidence [WoE] and evidential belief function [EBF])-based logistic model tree (LMT) classifier. The performance and prediction capability of the ensemble models were assessed using the area under the ROC curve (AUROC), standard error, 95% confidence intervals and significance level P. Model performance analyses indicated that the AUROC values of the WoE–LMT ensemble model using the training and validation data-sets were 86.02 and 85.9%, respectively, whereas those of the EBF–LMT ensemble model were 88.2 and 87.8%, respectively. On the other hand, the AUC curves for the four landslide susceptibility maps indicated that the AUC values of the ensemble models of WoE–LMT (85.11 and 83.98%) and EBF–LMT (86.21 and 85.23%) could improve the performance and prediction accuracy of single WoE (84.23 and 82.46%) and EBF (85.39 and 81.33%) models for the training and validation data-sets.

Spatial Prediction of Landslide Hazard at the Yihuang Area (China): A Comparative Study on the Predictive Ability of Backpropagation Multi-layer Perceptron Neural Networks and Radial Basic Function Neural Networks

The aim of this study is to investigate potential applications of multi-layer perceptron neural networks (MLP Neural Nets) and radial basis function neural networks (RBF Neural Nets) for landslide susceptibility mapping in the Yihuang area (China). First, a landslide inventory map with 187 landslide locations was generated, and then the map was randomly partitioned into a ratio of 70/30 for training and validating models. Second, 14 landslide conditioning factors (slope, altitude, aspect, topographic wetness, sediment transport index (STI), stream power index (SPI), plan curvature, distance to rivers, distance to faults, distance to roads, land use, normalized difference vegetation index (NDVI), lithology, rainfall) were prepared. Using MLP Neural Nets and RBF Neural Nets, two landslide susceptibility models were constructed and two landslide susceptibility maps were generated. Finally, the two resulting landslide susceptibility maps were validated using the landslide locations and the receiver operating characteristic (ROC) method. The validation results showed that the areas under the ROC curve (AUC) for the two landslide models produced by MLP Neural Nets and RBF Neural Nets are 0.932 and 0.765 for success rate curve and 0.757 and 0.725 for prediction rate curve, respectively. The results showed that the MLP Neural Nets model is better than the RBF Neural Nets model in this study. The results may be useful for general land use planning and hazard mitigation purposes.

Improving the accuracy of landslide susceptibility model using a novel region-partitioning approach

Landslide is a natural disaster that threatens human lives and properties worldwide. Numerous have been conducted on landslide susceptibility mapping (LSM), in which each has attempted to improve the accuracy of final outputs. This study presents a novel region-partitioning approach for LSM to understand the effects of partitioning a focused region into smaller areas on the prediction accuracy of common regression models. Results showed that the partitioning of the study area into two regions using the proposed method improved the prediction rate from 0.77 to 0.85 when support vector machine was used, and from 0.87 to 0.88 when logistic regression model was utilized. The spatial agreements of the models were also improved after partitioning the area into two regions based on Shannon entropy equations. Our comparative study indicated that the proposed method outperformed the geographically weighted regression model that considered the spatial variations in landslide samples. Overall, the main advantages of the proposed method are improved accuracy and the reduction of the effects of spatial variations exhibited in landslide-conditioning factors.

Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive Bayes and RBFNetwork models for the Long County area (China)

The main goal of this study is to assess and compare three advanced machine learning techniques, namely, kernel logistic regression (KLR), naïve Bayes (NB), and radial basis function network (RBFNetwork) models for landslide susceptibility modeling in Long County, China. First, a total of 171 landslide locations were identified within the study area using historical reports, aerial photographs, and extensive field surveys. All the landslides were randomly separated into two parts with a ratio of 70/30 for training and validation purposes. Second, 12 landslide conditioning factors were prepared for landslide susceptibility modeling, including slope aspect, slope angle, plan curvature, profile curvature, elevation, distance to faults, distance to rivers, distance to roads, lithology, NDVI (normalized difference vegetation index), land use, and rainfall. Third, the correlations between the conditioning factors and the occurrence of landslides were analyzed using normalized frequency ratios. A multicollinearity analysis of the landslide conditioning factors was carried out using tolerances and variance inflation factor (VIF) methods. Feature selection was performed using the chi-squared statistic with a 10-fold cross-validation technique to assess the predictive capabilities of the landslide conditioning factors. Then, the landslide conditioning factors with null predictive ability were excluded in order to optimize the landslide models. Finally, the trained KLR, NB, and RBFNetwork models were used to construct landslide susceptibility maps. The receiver operating characteristics (ROC) curve, the area under the curve (AUC), and several statistical measures, such as accuracy (ACC), F-measure, mean absolute error (MAE), and root mean squared error (RMSE), were used for the assessment, validation, and comparison of the resulting models in order to choose the best model in this study. The validation results show that all three models exhibit reasonably good performance, and the KLR model exhibits the most stable and best performance. The KLR model, which has a success rate of 0.847 and a prediction rate of 0.749, is a promising technique for landslide susceptibility mapping. Given the outcomes of the study, all three models could be used efficiently for landslide susceptibility analysis.

GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

ABSTRACT The aim of this research was to evaluate the predictive performances of frequency ratio (FR), logistic regression (LR) and weight of evidence (WoE), in flood susceptibility mapping in China. In addition, the ensemble WoE and LR and ensemble FR and LR techniques were applied and used in the evaluation. The flood inventory map, consisting of 196 flood locations, was extracted from a number of sources. The flood inventory data were randomly divided into a testing data-set, allocating 70% for training, and the remaining 30% for validation. The 15 flood conditioning factors included in the spatial database were altitude, slope, aspect, geology, distance from river, distance from road, distance from fault, soil type, land use/cover, rainfall, Normalized Difference Vegetation Index, Stream Power Index, Topographic Wetness Index, Sediment Transport Index and curvature. For validation, success and prediction rate curves were developed using area under the curve (AUC) method. The results indicated that the highest prediction rate of 90.36% was achieved using the ensemble technique of WoE and LR. The standalone WoE produced the highest prediction rate among the individual methods. It can be concluded that WoE offers a more advanced method of mapping prone areas, compared with the FR and LR methods.