Assessment of CMIP5 historical simulations of rainfall over Southeast Asia

Theoretical and Applied Climatology - We present preliminary analyses of the historical (1986–2005) climate simulations of a ten-member subset of the Coupled Model Inter-comparison Project...     

Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes,multilayer perceptron neural networks,and functional trees methods

The objective of this study is to make a comparison of the prediction performance of three techniques, Functional Trees (FT), Multilayer Perceptron Neural Networks (MLP Neural Nets), and Naïve Bayes (NB) for landslide susceptibility assessment at the Uttarakhand Area (India). Firstly, a landslide inventory map with 430 landslide locations in the study area was constructed from various sources. Landslide locations were then randomly split into two parts (i) 70 % landslide locations being used for training models (ii) 30 % landslide locations being employed for validation process. Secondly, a total of eleven landslide conditioning factors including slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to lineaments, distance to rivers, and rainfall were used in the analysis to elucidate the spatial relationship between these factors and landslide occurrences. Feature selection of Linear Support Vector Machine (LSVM) algorithm was employed to assess the prediction capability of these conditioning factors on landslide models. Subsequently, the NB, MLP Neural Nets, and FT models were constructed using training dataset. Finally, success rate and predictive rate curves were employed to validate and compare the predictive capability of three used models. Overall, all the three models performed very well for landslide susceptibility assessment. Out of these models, the MLP Neural Nets and the FT models had almost the same predictive capability whereas the MLP Neural Nets (AUC = 0.850) was slightly better than the FT model (AUC = 0.849). The NB model (AUC = 0.838) had the lowest predictive capability compared to other models. Landslide susceptibility maps were final developed using these three models. These maps would be helpful to planners and engineers for the development activities and land-use planning.     

Landslide Hazard Assessment Using Random SubSpace Fuzzy Rules Based Classifier Ensemble and Probability Analysis of Rainfall Data: A Case Study at Mu Cang Chai District,Yen Bai Province (Viet Nam)

Landslide hazard assessment at the Mu Cang Chai district; Yen Bai province (Viet Nam) has been done using Random SubSpace fuzzy rules based Classifier Ensemble (RSSCE) method and probability analysis of rainfall data. RSSCE which is a novel classifier ensemble method has been applied to predict spatially landslide occurrences in the area. Prediction of temporally landslide occurrences in the present study has been done using rainfall data for the period 2008–2013. A total of fifteen landslide influencing factors namely slope, aspect, curvature, plan curvature, profile curvature, elevation, land use, lithology, rainfall, distance to faults, fault density, distance to roads, road density, distance to rivers, and river density have been utilized. The result of the analysis shows that RSSCE and probability analysis of rainfall data are promising methods for landslide hazard assessment. Finally, landslide hazard map has been generated by integrating spatial prediction and temporal probability analysis of landslides for the land use planning and landslide hazard management.     

Mechanism of two rapid and long-runout landslides in the 16 April 2016 Kumamoto earthquake using a ring-shear apparatus and computer simulation (LS-RAPID)

Around hundred landslides were triggered by the Kumamoto earthquakes in April 2016, causing fatalities and serious damage to properties in Minamiaso village, Kumamoto Prefecture, Japan. The landslides included many rapid and long-runout landslides which were responsible for much of the damage. To understand the mechanism of these earthquake-triggered landslides, we carried out field investigations with an unmanned aerial vehicle to obtain DSM and took samples from two major landslides (Takanodai landslide and Aso-ohashi landslide) to measure parameters of the initiation and the motion of landslides. A series of ring-shear tests and computer simulations were conducted using a measured Kumamoto earthquake acceleration record from KNet station KMM005, 10 km west of Aso-ohashi landslide. The research results supported our assumed mechanism of sliding-surface liquefaction for the rapid and long-runout motion of these landslides.     

A Comparative Study of Least Square Support Vector Machines and Multiclass Alternating Decision Trees for Spatial Prediction of Rainfall-Induced Landslides in a Tropical Cyclones Area

The objective of this study is to explore and compare the least square support vector machine (LSSVM) and multiclass alternating decision tree (MADT) techniques for the spatial prediction of landslides. The Luc Yen district in Yen Bai province (Vietnam) has been selected as a case study. LSSVM and MADT are effective machine learning techniques of classification applied in other fields but not in the field of landslide hazard assessment. For this, Landslide inventory map was first constructed with 95 landslide locations identified from aerial photos and verified from field investigations. These landslide locations were then divided randomly into two parts for training (70 % locations) and validation (30 % locations) processes. Secondly, landslide affecting factors such as slope, aspect, elevation, curvature, lithology, land use, distance to roads, distance to faults, distance to rivers, and rainfall were selected and applied for landslide susceptibility assessment. Subsequently, the LSSVM and MADT models were built to assess the landslide susceptibility in the study area using training dataset. Finally, receiver operating characteristic curve and statistical index-based evaluations techniques were employed to validate the predictive capability of these models. As a result, both the LSSVM and MADT models have high performance for spatial prediction of landslides in the study area. Out of these, the MADT model (AUC = 0.853) outperforms the LSSVM model (AUC = 0.803). From the landslide study of Luc Yen district in Yen Bai province (Vietnam), it can be conclude that the LSSVM and MADT models can be applied in other areas of world also for and spatial prediction. Landslide susceptibility maps obtained from this study may be helpful in planning, decision making for natural hazard management of the areas susceptible to landslide hazards.     

Paleomagnetic results from deep-sea sediment of the Korea Deep Ocean Study (KODOS) area (northern equatorial Pacific) and their paleodepositional implications

Paleomagnetic properties of sediment cores were examined to reconstruct paleodepositional conditions in the Korea Deep Ocean Study (KODOS) area, located in the northeastern equatorial Pacific. The studied KODOS sediments have a stable remanent magnetization with both normal and reversed polarities, which are well correlated with the geomagnetic polarity timescale for the late Pliocene and Pleistocene. Average sedimentation rates are 1.56 and 0.88 mm/kiloyear for the Pleistocene and late Pliocene, respectively. Clay mineralogy and scanning electron microscope analyses of the sediments indicate that terrestrial material was transported to the deep-sea floor during these times. The variations of sedimentation rates with age may be explained by the onset of the northern hemisphere glaciation and subsequent climatic deterioration during the Pliocene and Pleistocene. For the Pleistocene, an increasing sedimentation rate implies that input of terrestrial materials was high, and also a high input of biogenic materials was detected as a result of increased primary production in the surface water. The down-core variations in paleomagnetic and rock-magnetic properties of the KODOS sediments were affected by dissolution processes in an oxic depositional regime. As shown by magnetic intensity and hysteresis parameters, the high natural remanent magnetization (NRM) stability in the upper, yellowish brown layers indicates that the magnetic carrier was in pseudo-single domain states. In the lower, dark brown sediments, only coarse magnetic grains survived dissolution and the NRM was carried by more abundant, multi-domain grains of low magnetic stability. The down-core variation of magnetic properties suggests that the KODOS sediments were subjected to dissolution processes resulting in a loss of the more stable components of the magnetic fraction with increasing core depth.     

Susceptibility assessment of the precursor stage of a landslide threatening Haivan Railway Station,Vietnam

Haivan Station is an important station on the North-South railway line in central Vietnam. Field investigation has identified a precursor stage of a landslide that would threaten this railway. Therefore, a landslide susceptibility assessment for Haivan Station was urgently needed to protect passenger safety and the national railway. Conducted investigations included air-photo interpretation, drilling, ground water and inclinometer monitoring, laboratory testing, and landslide simulation. This research applied the undrained dynamic loading ring shear apparatus ICL-2 to drill-core samples from the precursor landslide. Samples for ring shear tests were taken from sandy soil layers found at depths of ~21, ~31, and ~50 m in the cores. Each of these was believed to be a possible sliding surface of a landslide, and all were tested to shear failure in the ICL-2 apparatus. The boundary between highly weathered granitic rock and weathered granitic rock was identified at about 50 m depth. The inclinometer monitoring detected slight movement at this depth. Therefore, the present day risk of a landslide forming at 50 m is higher than for one forming at either 21 or 31 m. The landslide dynamic parameters obtained from the ring shear test of the 50-m-deep sample were used in an integrated numerical simulation model LS-RAPID. The simulation result gave the critical pore-pressure ratio for landslide occurrence, and landslide’s likely maximum speed, total volume, and depth of landslide debris that could cover the railway. These estimates serve to raise awareness of the vulnerability of the Vietnam national railway sector to landslide impact.