Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines

Preparation of landslide susceptibility map is the first step for landslide hazard mitigation and risk assessment. The main aim of this study is to explore potential applications of two new models such as two-class Kernel Logistic Regression (KLR) and Alternating Decision Tree (ADT) for landslide susceptibility mapping at the Yihuang area (China). The ADT has not been used in landslide susceptibility modeling and this paper attempts a novel application of this technique. For the purpose of comparison, a conventional method of Support Vector Machines (SVM) which has been widely used in the literature was included and their results were assessed. At first, a landslide inventory map with 187 landslide locations for the study area was constructed from various sources. Landslide locations were then spatially randomly split in a ratio of 70/30 for building landslide models and for the model validation. Then a spatial database with a total of fourteen landslide conditioning factors was prepared, including slope, aspect, altitude, topographic wetness index (TWI), stream power index (SPI),sediment transport index (STI), plan curvature, land use, normalized difference vegetation index (NDVI), lithology, distance to faults, distance to rivers, distance to roads, and rainfall. Using the KLR, the SVM, and the ADT, three landslide susceptibility models were constructed using the training dataset. The three resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and five statistical evaluation measures. In addition, pairwise comparisons of the area under the ROC curve were carried out to assess if there are significant differences on the overall performance of the three models. The goodness-of- fits are 92.5%(the KLR model), 88.8% (the SVM model), and 95.7% (the ADT model). The prediction capabilities are 81.1%,84.2%, and 93.3% for the KLR, the SVM, and the ADT models, respectively. The result shows that the ADT model yielded better overall performance and accurate results than the KLR and SVM models. The KLR model considered slightly better than SVM model in terms of the positive prediction values. The ADT and KLR are the two promising data mining techniques which might be considered to use in landslide susceptibility mapping. The results from this study may be useful for land use planning and decision making in landslide prone areas

Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines

The main objective of this study is to investigate the potential application of GIS-based Support Vector Machines (SVM) with four kernel functions, i.e., radial basis function (RBF), polynomial (PL), sigmoid (SIG), and linear (LN) for landslide susceptibility mapping at Luxi city in Jiangxi province, China. At the first stage of the study, a landslide inventory map with 282 landslide locations was identified using aerial photographs, satellite images, and field surveys. Of this, 70 % of the landslides (196 landslide locations) are used as a training dataset and the rest (86 landslide locations) were used as the validation dataset. Then, 15 landslide conditioning factors were prepared, i.e., altitude, aspect, slope, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), plan curvature, profile curvature, distance from river, distance from road, distance from fault, lithology, land use, NDVI, and rainfall. Using these conditioning factors, landslide susceptibility indexes were calculated using SVM with the four kernel functions. Subsequently, the results were exported and plotted in ArcGIS and four landslide susceptibility maps were produced. The four susceptibility maps were validated and compared using the landslide locations and the success rate and prediction rate methods. The validation results showed that success rates for the four SVM models are 82.0 % (RBF), 83.0 % (PL), 45.0 % (SIG), and 70.0 % (LN). The prediction rates for the four SVM models are 81.0 % (RBF), 71.0 % (PL), 40.0 % (SIG), and LN 63.0 % (SIG). The result shows that the RBF-SVM model has the highest overall performance. The produced susceptibility maps may be useful for general land-use planning in landslides

Step-wise Land-class Elimination Approach for extracting mixed-type built-up areas of Kolkata megacity

The extraction of urban built-up areas is an important aspect of urban planning and understanding the complex drivers and biophysical mechanism of urban climate processes. However, built-up area extraction using Landsat data is a challenging task due to spatio-temporal dynamics and spatially intermixed nature of Land Use and Land Cover (LULC) in the cities of the developing countries, particularly in tropics. In the light of advantages and drawbacks of the Normalized Difference Built-up Index (NDBI) and Built-up Area Extraction Method (BAEM), a new and simple method i.e. Step-wise Land-class Elimination Approach (SLEA) is proposed for rapid and accurate mapping of urban built-up areas without depending exclusively on the band specific normalized indices, in order to pursue a more generalized approach. It combines the use of a single band layer, Normalized Difference Vegetation Index (NDVI) image and another binary image obtained through Logit model. Based on the spectral designation of the satellite image in use, a particular band is chosen for identification of water pixels. The Double-window Flexible Pace Search (DFPS) approach is employed for finding the optimum threshold value that segments the selected band image into water and non-water categories. The water pixels are then eliminated from the original image. The vegetation pixels are similarly identified using the NDVI image and eliminated. The residual pixels left after elimination of water and vegetation categories belong either to the built-up areas or to bare land categories. Logit model is used for separation of the built-up areas from bare lands. The effectiveness of this method was tested through the mapping of built-up areas of the Kolkata Metropolitan Area (KMA), India from Thematic Mapper (TM) images of 2000, 2005 and 2010, and Operational Land Imager (OLI) image of 2015. Results of the proposed SLEA were 95.33% accurate on the whole, while those derived by the NDBI and BAEM approaches returned an overall accuracy of 83.67% and 89.33%, respectively. Comparisons of the results obtained using this method with those obtained from NDBI and BAEM approaches demonstrate that the proposed approach is quite reliable. The SLEA generates new patterns of evidence and hypotheses for built-up areas extraction research, providing an integral link with statistical science and encouraging trans-disciplinary collaborations to build robust knowledge and problem solving capacity in urban areas. It also brings landscape architecture, urban and regional planning, landscape and ecological engineering, and other practice-oriented fields to bear together in processes for identifying problems and analyzing, synthesizng, and evaluating desirable alternatives for urban change. This method produced very accurate results in a more efficient manner compared to the earlier built-up area extraction approaches for the landscape and urban planning

Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping

The landslide hazard occurred in Taibai County has the characteristics of the typical landslides in mountain hinterland. The slopes mainly consist of residual sediments and locate along the highway. Most of them are in the less stable state and in high risk during rainfall in flood season especially. The main purpose of this paper is to produce landslide susceptibility maps for Taibai County (China). In the first stage, a landslide inventory map and the input layers of the landslide conditioning factors were prepared in the geographic information system supported by field investigations and remote sensing data. The landslides conditioning factors considered for the study area were slope angle, altitude, slope aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, normalized difference vegetation index, lithological unit, rainfall and land use. Subsequently, the thematic data layers of conditioning factors were integrated by frequency ratio (FR), weights of evidence (WOE) and evidential belief function (EBF) models. As a result, landslide susceptibility maps were obtained. In order to compare the predictive ability of these three models, a validation procedure was conducted. The curves of cumulative area percentage of ordered index values vs. the cumulative percentage of landslide numbers were plotted and the values of area under the curve (AUC) were calculated. The predictive ability was characterized by the AUC values and it indicates that all these models considered have relatively similar and high accuracies. The success rate of FR, WOE and EBF models was 0.9161, 0.9132 and 0.9129, while the prediction rate of the three models was 0.9061, 0.9052 and 0.9007, respectively. Considering the accuracy and simplicity comprehensively, the FR model is the optimum method. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose

Single nucleotide polymorphisms of Kit gene in Chinese indigenous horses

Kit gene is a genetic determinant of horse white coat color which has been a highly valued trait in horses for at least 2,000 years. Single nucleotide polymorphisms (SNPs) in Kit are of importance due to their strong associations with melanoblast survival during embryonic development. In this study, a mutation analysis of all 21 Kit exons in 14 Chinese domestic horse breeds revealed six SNPs (g.91214T＞G, g.143245T＞G, g.164297C＞T, g.170189C＞T, g.171356C＞G, and g.171471G＞A), which located in 5’-UTR region, intron 6, exon 15, exon 20, intron 20, and exon 21 of the equine Kit gene, respectively. Subsequently, these six SNPs loci were genotyped in 632 Chinese horses by PCR-RFLP or direct sequencing. The six SNPs together defined 18 haplotypes, demonstrating abundant haplotype diversities in Chinese horses. All the mutant alleles and haplotypes were shared among different breeds. But fewer mutations were detected in horses from China than that from abroad, indicating that Chinese horses belong to a more ancient genetic pool. This study will provide fundamental genetic information for evaluating the genetic diversity of Kit gene in Chinese indigenous horse breeds