The main purpose of the present study is to use three state-of-the-art data mining techniques, namely, logistic model tree (LMT), random forest (RF), and classification and regression tree (CART) models, to map landslide susceptibility. Long County was selected as the study area. First, a landslide inventory map was constructed using history reports, interpretation of aerial photographs, and extensive field surveys. A total of 171 landslide locations were identified in the study area. Twelve landslide-related parameters were considered for landslide susceptibility mapping, including slope angle, slope aspect, plan curvature, profile curvature, altitude, NDVI, land use, distance to faults, distance to roads, distance to rivers, lithology, and rainfall. The 171 landslides were randomly separated into two groups with a 70/30 ratio for training and validation purposes, and different ratios of non-landslides to landslides grid cells were used to obtain the highest classification accuracy. The linear support vector machine algorithm (LSVM) was used to evaluate the predictive capability of the 12 landslide conditioning factors. Second, LMT, RF, and CART models were constructed using training data. Finally, the applied models were validated and compared using receiver operating characteristics (ROC), and predictive accuracy (ACC) methods. Overall, all three models exhibit reasonably good performances; the RF model exhibits the highest predictive capability compared with the LMT and CART models. The RF model, with a success rate of 0.837 and a prediction rate of 0.781, is a promising technique for landslide susceptibility mapping. Therefore, these three models are useful tools for spatial prediction of landslide susceptibility.

A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility

Landslide is one of the repeated geological hazards during rainy season, which causes fatalities, damage to property and economic losses in Korea. Landslides are responsible for at least 17% of all fatalities from natural hazards worldwide, and nearly 25% of annual casualties caused by natural hazards in Korea. Due to global climate change, the frequency of landslide occurrence has been increased and subsequently, the losses and damages associated with landslides also have been increased. Therefore, accurate prediction of landslide occurrence, and monitoring and early warning for ground movements are very important tasks to reduce the damages and losses caused by landslides. Various studies on landslide prediction and reduction in landslide damage have been performed and consequently, much of the recent progress has been in these areas. In particular, the application of information and geospatial technologies such as remote sensing and geographic information systems (GIS) has greatly contributed to landslide hazard assessment studies over recent years. In this paper, the recent advances and the state-of-the-art in the essential components of the landslide hazard assessment, such as landslide susceptibility analysis, runout modeling, landslide monitoring and early warning, were reviewed. Especially, this paper focused on the evaluation of the landslide susceptibility using probabilistic approach and physically based method, runout evaluation using volume based model and dynamic model, in situ ground based monitoring techniques, remote sensing techniques for landslide monitoring, and landslide early warning using rainfall and physical thresholds.

Landslide prediction, monitoring and early warning: a concise review of state-of-the-art

Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques

Laboratory Investigation on Strength Characteristics of Expansive Soil Treated with Jute Fiber Reinforcement

An urban heat island study in Nanchang City, China based on land surface temperature and social-ecological variables

The impulsive wave is considered as one of the most notably secondary hazards induced by landslides in reservoir areas. The impulsive wave with considerable wave amplitude is able to cause serious damage to the dam body, shoreline properties and lives. To investigate and predict the wave characteristics, many experimental studies employed the generalized channels rather than the realistic topography. Deviation from the idealized geometries may result in non-negligible effects due to the wave refraction or reflection with complex topography. To consider the topography effect, a prototype scaled experiment was conducted. A series of tests with different collocation of parameters were performed. The experimental results were then summarized to propose empirical equations to predict the maximum wave amplitude, and wave decay in channel direction. The generalized empirical equations can obtain better results for wave features prediction by compared with those derived from the idealized models. Furthermore, a 3D numerical modeling corresponding to the physical experiment was conducted based on the SPH method. The wave characteristics in the sliding and channel directions were investigated in detail including the maximum wave amplitude, wave run-up, wave arrival time and wave crest amplitude decay. The comparison between the simulation and experiment indicates the promising accuracy of the SPH simulation in determining the general features even with complex river topography. Finally, the limitation and applicability of both the experimental and numerical methods in analyzing the practical engineering problems were discussed. Combination of the both methods can benefit the hazard prevention and reduction for landslide generated impulsive waves in reservoir area.

Modeling of landslide generated impulsive waves considering complex topography in reservoir area

A series of disaster risk reduction (DRR) programs were carried out in landslide-prone areas in the Three Gorges Reservoir (TGR) to improve community resilience. The Wanzhou District in the TGR was selected as a case to introduce the community-based DRR (CBDRR) system. The system is community-based and government-led with the assistance of experts, police, Red Cross, etc. It involves risk investigation, education and training, landslide monitoring, information analysis, early warning system and emergency response. The landslide mechanism and element at risk were ascertained after detailed field investigation. Based on investigation, a characterized education and training to local residents was carried out. The local residents living on landslides carried out the community-based landslide monitoring. Some simple but effective monitoring methods were applied, such as steel piles, mosaic, convergence meters and smart phone, which are widely used in community-based landslide monitoring. To comprehensively understand the landslide and the data monitored by local residents, a real-time landslide monitoring system was established on several typical landslides. At last, a successful landslide emergency response was introduced to well explain the early warning system and emergency response. After carrying out the CBDRR for a decade, the landslide resilience has enhanced and the local residents are more relieved to live on landslides.

A community-based disaster risk reduction system in Wanzhou, China

Trichloroacetic acid (TCA), a toxic substance produced in the disinfection process of wastewater treatment plants, will accumulate in the receiving water. The detection of TCA in the water can achieve the purpose of early warning. However, currently there are few reports on microbial sensors used for TCA detection, and the characteristics of their microbial communities are still unclear. In this work, a toxicity monitoring microbial system (TMMS) with nitrifying biofilm as a sensing element and cathode oxygen reduction as a current signal was successfully constructed for TCA detection. The current and nitrification rate showed a linear relationship with low TCA concentration from 0 to 50 μg/L (R2current = 0.9892, R2nitrification = 0.9860), and high concentration range from 50 to 5000 μg/L (R2current = 0.9883, R2nitrification = 0.9721). High-throughput sequencing revealed that the TMMS was composed of autotrophic/heterotrophic nitrifying and denitrifying microorganisms. Further analysis via symbiotic relationship network demonstrated that Arenimonas and Hyphomicrobium were the core nodes for maintaining interaction between autotropic and heterotrophic nitrifying bacteria. Kyoto Encyclopedia of Genes and Genomes analysis showed that after adding TCA to TMMS, the carbon metabolism and the abundance of the tricarboxylic acid cycle pathway were reduced, and the activity of microorganisms was inhibited. TCA stress caused a low abundance of nitrifying and denitrifying functional enzymes, resulting in low oxygen consumption in the nitrification process, but more oxygen supply for cathode oxygen reduction. This work explored a novel sensor combined with electrochemistry and autotrophic/heterotrophic nitrification, which provided a new insight into the development of microbial monitoring of toxic substances. 

Yiliang Liu

Papers

Modeling of landslide generated impulsive waves considering complex topography in reservoir area

A community-based disaster risk reduction system in Wanzhou, China

A novel electrochemical sensor based on autotropic and heterotrophic nitrifying biofilm for trichloroacetic acid toxicity monitoring.

The research progress, hotspots, challenges and outlooks of solid-phase denitrification process.

Visual signal sensor coupling to nitrification for sustainable monitoring of trichloroacetaldehyde and the response mechanisms.