Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression

The purpose of this study is to evaluate and compare the results of applying the statistical index and the logistic regression methods for estimating landslide susceptibility in the Hoa Binh province of Vietnam. In order to do this, first, a landslide inventory map was constructed mainly based on investigated landslide locations from three projects conducted over the last 10 years. In addition, some recent landslide locations were identified from SPOT satellite images, fieldwork, and literature. Secondly, ten influencing factors for landslide occurrence were utilized. The slope gradient map, the slope curvature map, and the slope aspect map were derived from a digital elevation model (DEM) with resolution 20 × 20 m. The DEM was generated from topographic maps at a scale of 1:25,000. The lithology map and the distance to faults map were extracted from Geological and Mineral Resources maps. The soil type and the land use maps were extracted from National Pedology maps and National Land Use Status maps, respectively. Distance to rivers and distance to roads were computed based on river and road networks from topographic maps. In addition, a rainfall map was included in the models. Actual landslide locations were used to verify and to compare the results of landslide susceptibility maps. The accuracy of the results was evaluated by ROC analysis. The area under the curve (AUC) for the statistical index model was 0.946 and for the logistic regression model, 0.950, indicating an almost equal predicting capacity.     

Study of landslide geological hazard prediction method based on probability migration

Natural Hazards - Based on construction of landslide displacement field detection system in Heifangtai (Lanzhou, China), landslide geological hazard prediction method based on probability migration...     

Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province,Vietnam

Natural Hazards - Vietnam’s central coastal region is the most vulnerable and always at flood risk, severely affecting people’s livelihoods and socio-economic development. In...     

Spatio-temporal estimation of shallow landslide hazard triggered by rainfall using a three-dimensional model

A shallow landslide triggered by rainfall can be forecast in real-time by modeling the relationship between rainfall infiltration and decrease of slope stability. This paper describes a promising approach that combines an improved three-dimensional slope stability model with an approximate method based on the Green and Ampt model, to estimate the time–space distribution of shallow landslide hazards. Once a forecast of rainfall intensity and slope stability-related data, e.g., terrain and geology data, are acquired, this approach is shown to have the ability to estimate the variation of slope stability of a wide natural area during rainfall and to identify the location of potential failure surfaces. The effectiveness of the estimation procedures described has been tested by comparison with a one-dimensional method and by application to a landslide-prone area in Japan.     

Distribution and genesis of high-fluoride groundwaters in Ninh Hoa district, Vietnam

In 2003, we initiated the first systematic investigation of the occurrence of high-fluoride groundwaters in the Ninh Hoa district, China. The study involved the measurement of F- and other ionic species （by FISE and HPIC techniques） in well and bore samples throughout the district. The results helped ascertain the precise extent of fluorosis risk within the local population. Treatment of the data within a GIS enables recognition of particular hot-spots of fluoride concentration, and provides a valuable tool for the minimizing of local health risks and identification of possible source, or sources, of fluoride.     

基于多元统计的甘肃永靖黑方台滑坡强度预测模型

预测滑坡强度是滑坡风险分析与控制的基础和关键.以黑方台为研究区,在野外调查的基础上,针对研究区35处滑坡几何参数的数理统计,系统地分析了滑距与滑坡几何特征参数的相关关系,并按照黄土滑坡、黄土-基岩滑坡分别建立了滑坡空间预测的一元回归和多元回归统计模型.在统计模型中,分别以原始边坡坡度、塌落角、滑体宽度等因素为自变量,以滑坡延伸角为因变量,采用单因素和多因素拟合的方法,实现滑坡强度的简便预测.     

Seismic hazard analysis of Sinop province,Turkey using probabilistic and statistical methods

Using 4.0 and greater magnitude earthquakes which occurred between 1 January 1900 and 31 Dec 2008 in the Sinop province of Turkey this study presents a seismic hazard analysis based on the probabilistic and statistical methods. According to the earthquake zonation map, Sinop is divided into first, second, third and fourth-degree earthquake regions. Our study area covered the coordinates between 40.66°– 42.82°N and 32.20°– 36.55°E. The different magnitudes of the earthquakes during the last 108 years recorded on varied scales were converted to a common scale (Mw). The earthquake catalog was then recompiled to evaluate the potential seismic sources in the aforesaid province. Using the attenuation relationships given by Boore et al. (1997) and Kalkan and Gülkan (2004), the largest ground accelerations corresponding to a recurrence period of 475 years are found to be 0.14 g for bedrock at the central district. Comparing the seismic hazard curves, we show the spatial variations of seismic hazard potential in this province, enumerating the recurrence period in the order of 475 years.     

降雨型滑坡失稳概率计算方法

为了解决滑坡风险评价中的滑坡失稳概率计算问题,利用前人在降雨阈值的研究成果,结合气象学中降雨概率分布理论,以云南省盐津县庙坝滑坡为例进行计算,建立降雨型滑坡失稳概率计算模型。结果表明,盐津县降雨型滑坡的降雨阈值类型为累积降雨量-历时关系阈值,即为单日降雨阈值,降雨阈值为29.7 mm;盐津县在当日降雨量达到或超过阈值水平时可能诱发滑坡,对滑坡影响的滞后天数最大为5天;庙坝滑坡在8月20—25日6天内单日降雨达到或超过29.7 mm的降雨概率为46.49%;庙坝滑坡在8月25日因前5天或当天单日降雨量超过29.7 mm而失稳的概率为0.2853%。     

Regional hazard prediction of rock bursts using microseismic energy attenuation tomography in deep mining

Rock burst prediction is a worldwide challenge that we have long tried to overcome. This study tentatively proposed a method to regionally predict rock burst hazards using microseismic energy attenuation. To verify the feasibility of the proposal, first, the mechanism of microseismic energy propagation and attenuation in rock medium was explored, and dominant attenuation characteristics of microseismic waves were analyzed. Second, a spatial attenuation model of microseismic energy was established, and the average energy attenuation coefficient for each wave path was defined. A 3D seismic energy attenuation inversion algorithm was put forward, and the corresponding computation matrix was developed. Third, a continuous microseismic field investigation was carried out in a deep coal mine. Seismic energy attenuation coefficient was confirmed using the calibrated focus position and energy determination. Based on data discretization processing, energy attenuation inversion and tomography, potential rock burst hazard regions were strictly zoned in mining areas. Finally, regional prediction results obtained from the microseismic energy attenuation were compared with the direct measurement results obtained from the classical drilling dust method to verify the reliability of proposed approach. It turns out that rock burst hazard regions predicted by the microseismic energy attenuation agreed well with the objective hazardous situations. Seismic energy attenuation coefficient is a significant evaluation factor that directly mirrors the inelastic performance of rock medium. Energy attenuation coefficient threshold used for determining the rock burst hazard regions was 3.0 km^?1. Reliability of the seismic energy attenuation inversion and tomography was closely related to the spatial distribution of microseisms in a localized region. The optimum spatial density of microseisms was 0.2 m^?3. Regional rock burst prediction using microseismic energy attenuation is an effective approach for revealing potential hazardous regions in deep mining conditions. This approach improves the pertinence of geological hazard prevention and provides a beforehand reference for targeted hazard management.     

Regional frequency analysis of daily rainfall extremes using L-moments approach

Daily extreme precipitation values are among environmental events with the most disastrous consequences for human society. Information on the magnitudes and frequencies of extreme precipitations is essential for sustainable water resources management, planning for weather-related emergencies, and design of hydraulic structures. In the present study, regional frequency analysis of maximum daily rainfalls was investigated for Golestan province located in the northeastern Iran. This study aimed to find appropriate regional frequency distributions for maximum daily rainfalls and predict the return values of extreme rainfall events (design rainfall depths) for the future. L-moment regionalization procedures coupled with an index rainfall method were applied to maximum rainfall records of 47 stations across the study area. Due to complex geographic and hydro-climatological characteristics of the region, an important research issue focused on breaking down the large area into homogeneous and coherent sub-regions. The study area was divided into five homogeneous regions, based on the cluster analysis of site characteristics and tests for the regional homogeneity. The goodness-of-fit results indicated that the best fitting distribution is different for individual homogeneous regions. The difference may be a result of the distinctive climatic and geographic conditions. The estimated regional quantiles and their accuracy measures produced by Monte Carlo simulations demonstrate that the estimation uncertainty as measured by the RMSE values and 90% error bounds is relatively low when return periods are less than 100 years. But, for higher return periods, rainfall estimates should be treated with caution. More station years, either from longer records or more stations in the regions, would be required for rainfall estimates above T=100 years. It was found from the analyses that, the index rainfall (at-site average maximum rainfall) can be estimated reasonably well as a function of mean annual precipitation in Golestan province. Index rainfalls combined with the regional growth curves, can be used to estimate design rainfalls at ungauged sites. Overall, it was found that cluster analysis together with the L-moments based regional frequency analysis technique could be applied successfully in deriving design rainfall estimates for northeastern Iran. The approach utilized in this study and the findings are of great scientific and practical merit, particularly for the purpose of planning for weather-related emergencies and design of hydraulic engineering structures.     

Effects of urbanization on groundwater level in aquifers of Binh Duong Province,Vietnam

The purpose of this paper was to assess the impact of urbanization on the groundwater level(GWL)in aquifers of Binh Duong(BD)Province.The research method is to analyze the trend of GWL,the recharge capacity of surface over time and the relationship between them.The data of the GWL used in the study are the average values in the dry and rainy seasons of 35 observation wells from 2011 to 2018,which are in Pleistocene and Pliocene aquifers.The ability to recharge groundwater from the surface in this study was represented by the curve number(CN),a parameter used in hydrology for calculating direct runoff or infiltration from rainfall.The land use data to identify the CN was analyzed from the Landsat images.The results show that besides over-exploitation,the change of surface characteristic due to the urbanization development process is also the cause of the GWL decline.The analysis of seasonal GWL data shows that the increase in impervious surface area is the cause of GWL decline in the Pleistocene aquifer,which is more evident in the rainy season than in the dry season.The statistical results also show that in the rainy season and in shallow aquifers,a higher CN change can be found with the wells that had a remarkable GWL decline compared to the remaining wells.     

DEM-based deterministic landslide hazard analysis in the Lesser Himalaya of Nepal

In Nepal, people live in widely spread settlements in the fragile Himalayan terrains, and suffer more from landslides than from any other type of natural disaster. The small-scale rainfall-triggered landslides in the Lesser Himalaya of Nepal are generally shallow (about 0.5 to 2.5 m) and are triggered by changes in the physical property of soil layers during rainfall. The relation between landslides and slope hydrology has received little attention in Himalayan landslide research. Thus, this paper deals with the probability of slope failure during extreme rainfall events by considering a digital elevation model (DEM)-based hydrological model for soil saturation depth and an infinite slope stability model. Deterministic distributed analysis in a geographic information system (GIS) was carried out to calculate the probability of slope failure. A simple method of error propagation was used to calculate the variance of the safety factors and the probability of failure. When normally distributed failure probability values were checked against existing landslides, it was found that more than 50% of the pixels of existing landslides coincided with a high calculated probability of failure. Although the deterministic distributed analysis has certain drawbacks, as described by previous researchers, this study concluded that the calculated failure probability can be utilised to predict the probability of slope failure in Himalayan terrain during extreme rainfall events.     

Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland,Malaysia

This paper presents landslide susceptibility analysis around the Cameron Highlands area, Malaysia using a geographic information system (GIS) and remote sensing techniques. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten landslide occurrence factors were selected as: topographic slope, topographic aspect, topographic curvature and distance from drainage, lithology and distance from lineament, soil type, rainfall, land cover from SPOT 5 satellite images, and the vegetation index value from SPOT 5 satellite image. These factors were analyzed using an advanced artificial neural network model to generate the landslide susceptibility map. Each factor’s weight was determined by the back-propagation training method. Then, the landslide susceptibility indices were calculated using the trained back-propagation weights, and finally, the landslide susceptibility map was generated using GIS tools. The results of the neural network model suggest that the effect of topographic slope has the highest weight value (0.205) which has more than two times among the other factors, followed by the distance from drainage (0.141) and then lithology (0.117). Landslide locations were used to validate the results of the landslide susceptibility map, and the verification results showed 83% accuracy. The validation results showed sufficient agreement between the computed susceptibility map and the existing data on landslide areas.     

The role of rainfall in the landslide hazard: the case of the Avigliano urban area (Southern Apennines, Italy)

Mass movements varying in type and size, some of which are periodically reactivated, affect the urban area of Avigliano. The disturbed and remoulded masses consist of sandy–silty or silty–clayey plastic material interbedded with stone fragments and conglomerate blocks. Five landslides that were markedly liable to rainfall-associated instability phenomena were selected.

The relationships between landslides and rainfall were investigated using a hydrological and statistical model based on long-term series of daily rainfall data. The model was used to determine the return period of cumulative daily rainfall over 1–180 days. The resulting hydrological and statistical findings are discussed with the aim of identifying the rainfall duration most critical to landslides.

The concept of a precipitation threshold was generalized by defining some probability classes of cumulative rainfall. These classes indicate the thresholds beyond which reactivation is likely to occur. The probability classes are defined according to the return period of the cumulative rainfall concomitant with landslide reactivation.     

Regional-scale back-analysis using TRIGRS: an approach to advance landslide hazard modeling and prediction in sparse data regions

Landslides in Kerala, India, have been shown to be preceded not only by critical rainfall over a short period but also a much longer period of elevated pore pressure. Such rainfall-triggered landslides are difficult to monitor due to a lack of adequate data on the locations of failures and precipitation. Here, a method is presented using Transient Rainfall Infiltration and Grid-based Regional Slope stability (TRIGRS) as a tool to model the relationship between critical rainfall and antecedent pore pressure as they relate to slope stability, which can be useful for hazard assessment in sparse data regions. This is demonstrated by parameterizing the model with a combination of regional data sources, remote sensing, and temporal back-analysis based on two known failure events (June 2004 and July 2007). Ranges of possible geotechnical and hydraulic parameters were obtained from various local and regional sources, and soil thickness was modeled as a function of slope angle. Rainfall was estimated using satellite microwave radiometry data. For back-analysis, combinations of cohesion, friction angle, and water table depth were then tested in TRIGRS using trial and error until the predicted and observed failure times coincided for the two failure events. While the spatial prediction accuracy of the model is low and multiple solution sets are expected to exist, the results confirm that information regarding the critical pre-failure conditions and stability changes over time can be derived despite data-poor circumstances. Future studies can be undertaken extending this method to characterize many parameter combinations and incorporate more failure cases to develop probabilistic early-warning thresholds.     

The October 13, 2020, deadly rapid landslide triggered by heavy rainfall in Phong Dien,Thua Thien Hue,Vietnam

Landslides - At about 12:00 a.m., on October 13, 2020, a rapid rotational landslide induced by rainfall swept over Ranger Station-7 in Phong Xuan commune, Phong Dien district, Thua Thien Hue...     

Aftershock probabilistic seismic hazard analysis for Bushehr province in Iran using ETAS model

Natural Hazards - Aftershock probabilistic seismic hazard analysis (APSHA) has a key role in risk management after a major earthquake. The main goal of the current study is to assess aftershock...