The application of an innovative inverse model for understanding and predicting landslide movements (Salazie cirque landslides,Reunion Island)

The prediction of landslide movement acceleration is a complex problem, among others identified for deep-seated landslides, and represents a crucial step for risk assessment. Within the scope of this problem, the objective of this paper is to explore a modelling method that enables the study of landslide function and facilitates displacement predictions based on a limited data set. An inverse modelling approach is proposed for predicting the temporal evolution of landslide movement based on rainfall and displacement velocities. Initially, the hydrogeology of the studied landslides was conceptualised based on correlative analyses. Subsequently, we applied an inverse model with a Gaussian-exponential transfer function to reproduce the displacements. This method was tested on the Grand Ilet (GI) and Mare-à-Poule-d’Eau (HB) landslides on Reunion Island in the Indian Ocean. We show that the behaviour of landslides can be modelled by inverse models with a bimodal transfer function using a Gaussian-exponential impulse response. The cumulative displacements over 7 years of modelling (2 years of calibration period for GI, and 4 years for HB) were reproduced with an RMSE above 0.9. The characteristics of the bimodal transfer function are directly related to the hydrogeological functioning demonstrated by the correlative analyses: the rapid reaction of a landslide can be associated with the effect of a preferential flow path on groundwater level variations. Thus, this study shows that the inverse model using a Gaussian-exponential transfer function is a powerful tool for predicting deep-seated landslide movements and for studying how they function. Beyond modelling displacements, our approach effectively demonstrates its ability to contribute relevant data for conceptualising the sliding mechanisms and hydrogeology of landslides.     

Surface and subsurface conceptual model of an arid environment with respect to mid- and late Holocene climate changes

The water demand in arid regions is commonly covered by groundwater resources that date back to more humid periods of the Pleistocene and Holocene. Within the investigated arid part of SE Saudi-Arabia information about climate, groundwater levels, and pumping rates are only available for regions where groundwater extractions occur at present-day. For the prediction of the impact of long-term climate changes on groundwater resources an understanding of the hydrogeological and hydrological past and the development of the aquifers is necessary. Therefore, all available information about hydrology and hydrogeology for the past 10,000 years BP were collected and compiled to a conceptual model of the aquifer development on the Arabian Peninsula since the last Ice-Age. The climatic history was displayed by changes in precipitation, temperature and recharge during the mid-S and late Holocene. The hydrogeological development is described by groundwater ages, sea level fluctuations, movement of the coastline, and the development of sabkhas. The most sensitive parameter to describe the development of aquifer system is recharge. Present-day recharge was calculated with the hydrological model system HEC-HMS accounting for current precipitation, temperature, wind, soil types, and geomorphology. With respect to changes in precipitation and temperature over the past 10,000 years the temporal and spatial variability of groundwater recharge was calculated using empirical equations valid for semi-arid and arid settings. Further inflow into the groundwater system results from surface water infiltration in wadi beds, while natural outflow from the groundwater system occurs by discharge to the Gulf, evaporation from sabkhas, and spring discharge. Backward predictions can be verified by sedimentological observations of palaeo-river systems and lakes indicating that groundwater levels reached temporarily the surface under wetter climate conditions and ¹⁴C groundwater ages displaying groundwater residence times.     

Susceptibility assessments of the Wenchuan earthquake-triggered landslides in Longnan using logistic regression

In this study a Wenchuan earthquake-induced landslide susceptibility assessment was carried out in the Longnan area in northwestern China using a GIS-based logistic regression model. This region has frequently been affected by landslides in the past, and was intensively affected by the 5.12 Wenchuan earthquake which received considerable international attention. The data used for this study consist of the landslides triggered by the Wenchuan earthquake and a landslide pre-disposing factor database. Information regarding the landslide causative factors came from additional data sources, such as a digital elevation model (DEM) with a 30 × 30 m² resolution, orthophotos, geological and land-use maps, precipitation records, and information on peak ground acceleration data from the 2008 earthquake. The statistical analysis of the relationship between the Wenchuan earthquake-triggered landslides and pre-disposing factors showed the great influence of lithological and topographical conditions on slope failures. The quality of susceptibility mapping was validated by splitting the study area into training and validation sections. The prediction capability analysis demonstrated that the landslide susceptibility map could be used for land planning as well as emergency planning by local authorities.     

Towards hydrological triggering mechanisms of large deep-seated landslides

It is a widely accepted idea that hydrologically triggered deep-seated landslides are initiated by an increase in pore-water pressure on potential slip surface induced by rising groundwater level after prolonged period of intense rainfall although the process is not fully understood. In order to contribute to better understanding, the rainfall–groundwater relationships, hydrogeological monitoring and repeated geoelectrical imaging were carried out from March 2007 to April 2011 in large deep-seated landslide near ?ubietová (Western Carpathians) catastrophically reactivated at the end of February 1977. Based on our observations, groundwater level (GWL) response to precipitation differs considerably with respect to both overall hydrological conditions and GWL mean depth. While the rate of GWL increase up to 25 cm/day were measured after some rainfall events during wet periods, noticeably lower recharge rate (up to 1–2 cm/day) and delayed GWL response to rainfall (usually from 2 weeks to 2–4 months) were observed at the beginning of the wet season after considerable depression of GWLs due to previous effective rainfall deficit. Likewise, slow GWL fluctuations without short-term oscillations are typical for deeper GWLs. Thus, long-term (several seasons to several years) hydrological conditions affect markedly groundwater response to rainfall events in the studied landslide and can be crucial for its behaviour. Comparison of hydrological conditions characterising the analysed period with those that accompanied the landslide reactivation in 1977 allow us to assume that slightly above-average rainy season following the prolonged wet period can be far more responsible for movement acceleration (and possibly failure initiation) in deep landslides than the isolated season of extreme precipitation following a longer dry period. This is true especially for landslides in regions with significant seasonal temperature changes where potential effective precipitation (PEP), calculated as excess of precipitation (P) over potential evapotranspiration (PET), may be efficiently used for estimation of slope saturation condition.     

Field monitoring of groundwater responses to heavy rainfalls and the early warning of the Kualiangzi landslide in Sichuan Basin,southwestern China

The Kualiangzi landslide was triggered by heavy rainfalls in the “red beds” area of Sichuan Basin in southwestern China. Differing from other bedrock landslides, the movement of the Kualiangzi landslide was controlled by the subvertical cracks and a subhorizontal bedding plane (dip angle < 10°). The ingress of rainwater in the cracks formed a unique groundwater environment in the slope. Field measurement for rainfall, groundwater movement, and slope displacement has been made for the Kualiangzi landslide since 2013. The field monitoring system consists of two rainfall gauges, seven piezometers, five water-level gauges, and two GPS data loggers. The equipments are embedded near a longitudinal section of the landslide, where severe deformation has been observed in the past 3 years. The groundwater responses to four heavy rainfall events were analyzed between June 16 and July 24 in 2013 coincided with the flood season in Sichuan. Results showed that both of the water level and the pore-water pressure increased after each rainfall event with delay in the response time with respect to the precipitation. The maximum time lag reached 35 h occurred in a heavy rainfall event with cumulative precipitation of 127 mm; such lag effect was significantly weakened in the subsequent heavy rainfall events. In each presented rainfall event, longer infiltration period in the bedrock in the upper slope increased the response time of groundwater, compared to that of in the gravels in the lower slope. A translational landslide conceptual model was built for the Kualiangzi landslide, and the time lag was attributed to the gradual formation of the uplift pressure on the slip surface and the softening of soils at the slip surface. Another important observation is the effect on the slope movement which was caused by the water level (H _w) in the transverse tension trough developed at the rear edge of the landslide. Significant negative correlation was found for H _w and the slope stability factor (F _s), in particular for the last two heavy rainfall events, of which the drastic increase of water level caused significant deterioration in the slope stability. The rapid drop (Δ?=?22.5 kPa) of pore-water pressure in the deep bedrock within 1 h and the large increase (Δ?=?87.3 mm) of surficial displacement were both monitored in the same period. In the end, a four-level early warning system is established through utilizing H _w and the displacement rate D _r as the warning indicators. When the large deformation occurred in flood season, the habitants at the leading edge of the landslide can be evacuated in time.     

Feasibility of using artificial neural networks to forecast groundwater levels in real time

The purpose of this study is to develop the feed-forward back-propagation neural network (FFBPNN) to estimate the groundwater level (GL) of next hour according the current GL and past precipitation depth in the hillslope. The 72-h precipitation depth and the real-time groundwater levels are used as the model output layer determination variables. The output variables, are type 1, the GL, which has been used in many researches, and type 2, the groundwater level fluctuation (GLF), which is the difference between the current-time and the next-time groundwater level. The order of the water level fluctuation is less than that of the groundwater level by about one order of magnitude (ten times). The landslide area at the downstream of Wu-She Reservoir, Nantou County, Taiwan, is adopted as a field test area. Total 328 cases of Sinlaku typhoon were used to establish the prediction model of real-time GL. Another 327 cases of Jangmi typhoon were adopted to illustrate the model application. The result of model application shows that root-mean-square error of type 2 (=0.104 m) is smaller than that of type 1 (=0.408 m). In conclusion, the forecasting method used GLF gives a much better agreement with the measured values than that of GL.     

Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR)

Landslide hazard, vulnerability, and risk-zoning maps are considered in the decision-making process that involves land use/land cover (LULC) planning in disaster-prone areas. The accuracy of these analyses is directly related to the quality of spatial data needed and methods employed to obtain such data. In this study, we produced a landslide inventory map that depicts 164 landslide locations using high-resolution airborne laser scanning data. The landslide inventory data were randomly divided into a training dataset: 70 % for training the models and 30 % for validation. In the initial step, a susceptibility map was developed using logistic regression approach in which weights were assigned to every conditioning factor. A high-resolution airborne laser scanning data (LiDAR) was used to derive the landslide conditioning factors for the spatial prediction of landslide hazard areas. The resultant susceptibility was validated using the area under the curve method. The validation result showed 86.22 and 84.87 % success and prediction rates, respectively. In the second stage, a landslide hazard map was produced using precipitation data for 15 years. The precipitation maps were subsequently prepared and show two main categories (two temporal probabilities) for the study area (the average for any day in a year and abnormal intensity recorded in any day for 15 years) and three return periods (15-, 10-, and 5-year periods). Hazard assessment was performed for the entire study area. In the third step, an element at risk map was prepared using LULC, which was considered in the vulnerability assessment. A vulnerability map was derived according to the following criteria: cost, time required for reconstruction, relative risk of landslide, risk to population, and general effect to certain damage. These criteria were applied only on the LULC of the study area because of lack of data on the population and building footprint and types. Finally, risk maps were produced using the derived vulnerability and hazard information. Thereafter, a risk analysis was conducted. The LULC map was cross-matched with the results of the hazard maps for the return period, and the losses were aggregated for the LULC. Then, the losses were calculated for the three return periods. The map of the risk areas may assist planners in overall landslide hazard management.     

Prediction of ground displacements and velocities from groundwater level changes at the Vallcebre landslide (Eastern Pyrenees, Spain)

In active landslides, the prediction of acceleration of movement is a crucial issue for the design and performance of warning systems. The landslide of Vallcebre in the Eastern Pyreenes, Spain, has been monitored since 1996 and data on rainfall, groundwater levels and ground displacements are measured on a regular basis. Displacements observed in borehole wire extensometers have shown an immediate response of the landslide to rainfall episodes. This rapid response is likely due to the presence of preferential drainage ways. The occurrence of nearly constant rates of displacement in coincidence with steady groundwater levels suggests the presence of viscous forces developed during the movement. An attempt to predict both landslide displacements and velocities was performed at Vallcebre by solving the momentum equation in which a viscous term (Bingham and power law) was added. Results show that, using similar rheological parameters for the entire landslide, computed displacements reproduce quite accurately the displacements observed at three selected wire extensometers. These results indicate that prediction of displacements from groundwater level changes is feasible.     

A comparison of logistic regression-based models of susceptibility to landslides in western Colorado,USA

The Paonia-McClure Pass area of Colorado has been recognized as a region highly susceptible to mass movement. Because of the dynamic nature of this landscape, accurate methods are needed to predict susceptibility to movement of these slopes. The area was evaluated by coupling a geographic information system (GIS) with logistic regression methods to assess susceptibility to landslides. We mapped 735 shallow landslides in the area. Seventeen factors, as predictor variables of landslides, were mapped from aerial photographs, available public data archives, ETM + satellite data, published literature, and frequent field surveys. A logistic regression model was run using landslides as the dependent factor and landslide-causing factors as independent factors (covariates). Landslide data were sampled from the landslide masses, landslide scarps, center of mass of the landslides, and center of scarp of the landslides, and an equal amount of data were collected from areas void of discernible mass movement. Models of susceptibility to landslides for each sampling technique were developed first. Second, landslides were classified as debris flows, debris slides, rock slides, and soil slides and then models of susceptibility to landslides were created for each type of landslide. The prediction accuracies of each model were compared using the Receiver Operating Characteristic (ROC) curve technique. The model, using samples from landslide scarps, has the highest prediction accuracy (85 %), and the model, using samples from landslide mass centers, has the lowest prediction accuracy (83 %) among the models developed from the four techniques of data sampling. Likewise, the model developed for debris slides has the highest prediction accuracy (92 %), and the model developed for soil slides has the lowest prediction accuracy (83 %) among the four types of landslides. Furthermore, prediction from a model developed by combining the four models of the four types of landslides (86 %) is better than the prediction from a model developed by using all landslides together (85 %).     

基于相空间重构和小波分析-粒子群向量机的滑坡地下水位预测

预测滑坡地下水位的动态演变过程对滑坡稳定性分析具有重要意义, 三峡库区库岸滑坡地下水位时间序列受多种因素影响, 呈现出高度非线性非平稳的特征.为对其进行预测, 提出一种基于相空间重构的小波分析-粒子群优化支持向量机(wavelet analysis-support vector machine, 简称WA-PSVM)模型.该模型引入小波变换法对地下水位序列进行时频分解, 将非平稳的地下水位序列转变为多个不同分辨率尺度下的较平稳的地下水位子序列; 然后重构各子序列的相空间, 再利用PSVM(全称support vector machine)模型对地下水位各子序列进行预测, 最后将各子序列预测值相加得到最终预测结果.以三峡库区三舟溪滑坡前缘STK-1水文孔日平均地下水位序列为例, 首先分析滑坡前缘地下水位变化的影响因素, 再将WA-PSVM模型应用于地下水位预测, 并与单独PSVM模型和小波分析-BP网络模型(wavelet analysis-back propagation, 简称WA-BP)作对比.结果表明: 滑坡前缘地下水位受降雨和库水位影响较大, 利用WA-PSVM模型对STK-1水文孔地下水位进行预测的均方根误差为0.073m、拟合优度为0.966, WA-PSVM模型预测精度高于单独PSVM模型和WA-BP模型.WA-PSVM模型解决了地下水位序列非线性非平稳的问题, 在不考虑影响因素的情况下能获得满意的预测效果, 具有较高的建模效率和较强的实用性.      

GIS-based statistical analysis of the spatial distribution of earthquake-induced landslides in the island of Lefkada, Ionian Islands, Greece

This is the first landslide inventory map in the island of Lefkada integrating satellite imagery and reports from field surveys. In particular, satellite imagery acquired before and after the 2003 earthquake were collected and interpreted with the results of the field survey that took place 1 week after this strong (Mw?=?6.3) event. The developed inventory map indicates that the density of landslides decreases from west to east. Furthermore, the spatial distribution of landslides was statistically analyzed in relation to the geology and topography for investigating their influence to landsliding. This was accomplished by overlaying these causal factors as thematic layers with landslide distribution data. Afterwards, weight values of each factor were calculated using the landslide index method and a landslide susceptibility map was developed. The susceptibility map indicates that the highest susceptibility class accounts for 38 % of the total landslide activity, while the three highest classes that cover the 10 % of the surface area, accounting for almost the 85 % of the active landslides. Our model was validated by applying the approaches of success and prediction rate to the dataset of landslides that was previously divided into two groups based on temporal criteria, estimation and validation group. The outcome of the validation dataset was that the highest susceptibility class concentrates 18 % of the total landslide activity. However, taking into account the frequency of landslides within the three highest susceptibility classes, more than 85 %, the model is characterized as reliable for a regional assessment of earthquake-induced landslides hazard.     

A small-scale landslide susceptibility assessment for the territory of Western Carpathians

This study presented herein compares the bivariate and multivariate landslide susceptibility mapping methods and presents the landslide susceptibility map of the territory of Western Carpathians in small scale. This study also describes pioneer work for the territory of Western Carpathians, overreaching state borders, using verified sophisticated statistical methods. In the susceptibility mapping, digital elevation model was first constructed using a GIS software, and parameter maps affecting the slope stability such as geology, seismicity, precipitation, topographical elevation, slope angle, slope aspect and land cover were considered. In the last stage of the analyses, landslide susceptibility maps were produced using bivariate and multivariate analyses, and they were then compared by means of their validations. The validation of the bivariate analysis data was performed using the results of bivariate analysis for landslide areas of Slovakia containing five classes of susceptibility in scale 1:500,000. The validation area is the area of Western Carpathians within Slovakia. Eighty-two per cent of area does not differ in more than one class. The validation of the multivariate analysis data was performed using the results from the Kysuce region in the northern part of Slovakia in scale 1:10,000. The raster calculator was used to express the difference between each pair of pixels within these two layers. Seventy-seven per cent of the pixels do not differ in more than 25 %, 94 % of the pixels do not differ in more than 50 %. The maximal possible difference is 100 % (one pixel with value 0 and other with value 1, or vice versa). Receiver operating characteristic analysis was also performed, the area under curve value for bivariate model was calculated to be 0.735, while it was 0.823 for multivariate. The results of the validation can be considered as satisfactory.     

Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms

Landslide susceptibility and hazard assessments are the most important steps in landslide risk mapping. The main objective of this study was to investigate and compare the results of two artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 136 landside locations were constructed from various sources. Then the landslide inventory map was randomly split into a training dataset 70 % (95 landslide locations) for training the ANN model and the remaining 30 % (41 landslides locations) was used for validation purpose. Nine landslide conditioning factors such as slope, slope aspect, altitude, land use, lithology, distance from rivers, distance from roads, distance from faults, and rainfall were constructed in geographical information system. In this study, both MLP and RBF algorithms were used in artificial neural network model. The results showed that MLP with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in landslide susceptibility mapping for the study area. Finally the landslide susceptibility maps were validated using the validation data (i.e., 30 % landslide location data that was not used during the model construction) using area under the curve (AUC) method. The success rate curve showed that the area under the curve for RBF and MLP was 0.9085 (90.85 %) and 0.9193 (91.93 %) accuracy, respectively. Similarly, the validation result showed that the area under the curve for MLP and RBF models were 0.881 (88.1 %) and 0.8724 (87.24 %), respectively. The results of this study showed that landslide susceptibility mapping in the Vaz Watershed of Iran using the ANN approach is viable and can be used for land use planning.     

Displacement prediction in colluvial landslides, Three Gorges Reservoir, China

The prediction of active landslide displacement is a critical component of an early warning system and helps prevent property damage and loss of human lives. For the colluvial landslides in the Three Gorges Reservoir, the monitored displacement, precipitation, and reservoir level indicated that the characteristics of the deformations were closely related to the seasonal fluctuation of rainfall and reservoir level and that the displacement curve versus time showed a stepwise pattern. Besides the geological conditions, landslide displacement also depended on the variation in the influencing factors. Two typical colluvial landslides, the Baishuihe landslide and the Bazimen landslide, were selected for case studies. To analyze the different response components of the total displacement, the accumulated displacement was divided into a trend and a periodic component using a time series model. For the prediction of the periodic displacement, a back-propagation neural network model was adopted with selected factors including (1) the accumulated precipitation during the last 1-month period, (2) the accumulated precipitation over a 2-month period, (3) change of reservoir level during the last 1 month, (4) the average elevation of the reservoir level in the current month, and (5) the accumulated displacement increment during 1 year. The prediction of the displacement showed a periodic response in the displacement as a function of the variation of the influencing factors. The prediction model provided a good representation of the measured slide displacement behavior at the Baishuihe and the Bazimen sites, which can be adopted for displacement prediction and early warning of colluvial landslides in the Three Gorges Reservoir.     

A deep-seated slow movement controlled by structural setting in marly formations of Central Italy

The site investigation of low-gradient slopes composed by marly rocks usually focuses on shallow slides in weathered mantling material as it is assumed that the underlying bedrock has higher strength, but deeper investigations may reveal larger, active, deep-seated movements. A typical example of this is found in Montemartano (Perugia, Central Italy). Here aerial photo interpretation and field observations indicate that active movements involve the shallower portion of the slope, formed by a very old and large landslide body extending over an area of about 0.5 km². Borehole core logging and probe inclinometer monitoring reveal that the area corresponding to the deep-seated landslide is moving at a maximum rate of 70 mm/year down to a maximum depth of 40 m. A comparison of inclinometer and piezometer data indicates that the movement seasonally reactivates even when rainfall and piezometer levels are below average values and suggests that structural setting of the whole slope influences both groundwater flow and movement kinematics. This hypothesis is reinforced by seepage analyses and stability analyses yielding a mobilized shear strength close to residual strength of the clayey interbeds of the marly limestone formations. This implies that instability occurs along bedding over a large part of the slide. The importance of these phenomena in land management policy is discussed and the critical aspects of their investigation and monitoring are addressed. The reconstruction of landslide geometry/stratigraphy and geotechnical characterization of the materials is closely considered, particularly as these are complicated by the limited representativeness of field and laboratory investigations in this type of material.     

Tank模型在模拟滑坡地下水位中的应用

季节性的降雨及其所引起的地下水状态的变化是促使日本大型结晶片岩滑坡活动和诱发灾害发生的重要原因。基于对一典型结晶片岩滑坡、降雨和地下水位的长期观测,利用Tank模型建立了一种模拟滑坡地下水位变化的方法。通过对滑体内不同观测点地下水位实际观测数据与模拟结果的对比分析,证明所采用的模拟方法能够很好地再现地下水位随降雨的变化形态,从而为预测和评价降雨型滑坡的地下水状态变化提供了依据。     

Landslide susceptibility assessment by Dempster–Shafer and Index of Entropy models,Sarkhoun basin,Southwestern Iran

Landslides are natural disasters often activated by interaction of different controlling environmental factors, especially in mountainous terrains. In this research, the landslide susceptibility map was developed for the Sarkhoun catchment using Index of Entropy (IoE) and Dempster–Shafer (DS) models. For this purpose, 344 landslides were mapped in GIS environment. 241 (70%) out of the landslides were selected for the modeling and the remaining (30%) were employed for validation of the models. Afterward, 10 landslide conditioning factor layers were prepared including land use, distance to drainage, slope gradient, altitude, lithology, distance to roads, distance to faults, slope aspect, Topography Wetness Index, and Stream Power Index. The relationship between the landslide conditioning factors and landslide inventory maps was determined using the IoE and DS models. In order to verify the models, the results were compared with validation landslide data not employed in training process of the models. Accordingly, Receiver Operating Characteristic (ROC) curves were applied, and Area Under the Curve (AUC) was calculated for the obtained susceptibility maps using the success (training data) and prediction (validation data) rate curves. The land use was found to be the most important factor in the study area. The AUC are 0.82, and 0.81 for success rates of the IoE, and DS models, respectively, while the prediction rates are 0.76 and 0.75. Therefore, the results of the IoE model are more accurate than the DS model. Furthermore, a satisfactory agreement is observed between the generated susceptibility maps by the models and true location of the landslides.     

Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS

The main goal of this study is to investigate the application of the probabilistic-based frequency ratio (FR) model in groundwater potential mapping at Langat basin in Malaysia using geographical information system. So far, the approach of probabilistic frequency ratio model has not yet been used to delineate groundwater potential in Malaysia. Moreover, this study includes the analysis of the spatial relationships between groundwater yield and various hydrological conditioning factors such as elevation, slope, curvature, river, lineament, geology, soil, and land use for this region. Eight groundwater-related factors were collected and extracted from topographic data, geological data, satellite imagery, and published maps. About 68 groundwater data with high potential yield values of ≥11 m³/h were randomly selected using statistical software of SPSS. Then, the groundwater data were randomly split into a training dataset 70 % (48 borehole data) for training the model and the remaining 30 % (20 borehole data) was used for validation purpose. Finally, the frequency ratio coefficients of the hydrological factors were used to generate the groundwater potential map. The validation dataset which was not used during the FR modeling process was used to validate the groundwater potential map using the prediction rate method. The validation results showed that the area under the curve for frequency model is 84.78 %. As far as the performance of the FR approach is concerned, the results appeared to be quite satisfactory, i.e., the zones determined on the map being zones of relative groundwater potential. This information could be used by government agencies as well as private sectors as a guide for groundwater exploration and assessment in Malaysia.     

Rainfall-induced landslides in Hulu Kelang area, Malaysia

Hulu Kelang is known as one of the most landslide-prone areas in Malaysia. The area has been constantly hit by landslide hazards since 1990s. This paper provides an insight into the mechanism of rainfall-induced landslide in the Hulu Kelang area. Rainfall patterns prior to the occurrences of five selected case studies were first analyzed. The results showed that daily rainfall information is insufficient for predicting landslides in the area. Rainfalls of longer durations, i.e., 3–30 days prior to the landslides should be incorporated into the prediction model. Numerical simulations on a selected case study demonstrated that both matric suction and factor of safety decreased steadily over time until they reached the lowest values on the day of landslide occurrence. Redistribution of infiltrated rainwater in the soil mass could be a reason for the slow response of failure mechanism to rainfall. Based on 21 rainfall-induced landslides that had occurred in the area, three rainfall thresholds were developed as attempts to predict the occurrence of rainfall-induced landslide. The rainfall intensity–duration threshold developed based on the local rainfall conditions provided a reasonably good prediction to the landslide occurrence. The cumulative 3- versus 30-day antecedent precipitation index threshold chart was capable of giving the most reliable prediction with the limiting threshold line for major landslide yielded a reliability of 97.6 %.     

A study of landslide deformation fields with a digital correlation method

Landslides are a form of geological disaster. Landslide development around the Three Gorges Dam is affected by many factors, such as the dam’s water storage cycle, flood discharge and precipitation. In this work, we investigated the Woshaxi landslide in Zigui County, Hubei Province. We gathered landslide images from April to May 2015, using digital cameras to observe the landslide surface. The landslide images were analyzed with a digital correlation method to obtain a landslide deformation field. The overall displacement was distributed non-uniformly, with displacements of up to 50 cm. We found landslide movement is a non-uniform and non-rigid body motion. By integrating the speckle method, grayscale feature search and other related methods, we not only succeeded in dealing with landslide data at various scales and levels, but also solved problems such as the registration of collected images and peering of gray levels. We calculated the displacement variation and direction of all landslide points and obtained the landslide displacement distribution. The method’s indoor calibration test error was within an acceptable range. This method is a good candidate for landscape monitoring due to its convenient operation, low cost and ability to extract useful information from a huge amount of data.