Food insecurity, dietary diversity and the right to adequate food among households in landslide-prone communities in Eastern Uganda: A cohort study.

We assessed food insecurity, dietary diversity and the right to adequate food among households in communities in Eastern Uganda that were affected by major landslides in 2010 and 2018. A prospective cohort study was applied to select 422 households during May-August (the food-plenty season) of 2019. In January-March (the food-poor season) of 2020, 388 households were re-assessed. Socio-demographic, food security, dietary diversity and right to adequate food data were collected using structured questionnaires. Four focus groups discussions and key informant interviews with 10 purposively sampled duty-bearers explored issues of food insecurity, dietary and the right to adequate food. The affected households had significantly higher mean (SE) food insecurity scores than controls, both during the food plenty season: 15.3 (0.5) vs. 10.8 (0.5), and during food-poor season: 15.9 (0.4) vs. 12.5 (0.0). The affected households had significantly lower mean (SE) dietary diversity scores than controls during the food plenty season: 5.4 (0.2) vs. 7.5 (0.2) and during the food poor season: 5.2 (0.2) vs. 7.3 (0.1). Multivariate analyses showed that the disaster event, education and main source of livelihood, were significantly associated with household food security and dietary diversity during the food-plenty season whereas during the food-poor season, the disaster event and education were associated with household food security and dietary diversity. During both food seasons, the majority of affected and control households reported to have consumed unsafe food. Cash-handout was the most preferred for ensuring the right to adequate food. Comprehension and awareness of human rights principles and state obligations were low. The severity of food-insecurity and dietary diversity differed significantly between the affected and control households during both food seasons. Moreover, the right to adequate food of landslide victims faced challenges to its realization. There is need for policy and planning frameworks that cater for seasonal variations, disaster effects and right to adequate food in order to reduce landslide victims' vulnerability to food insecurity and poor dietary diversity. In the long-term, education and income diversification program interventions need to be integrated into disaster recovery programs since they are central in enhancing the resilience of rural livelihoods to shocks and stressors on the food system.

Coverage survey and lessons learned from a pre-emptive cholera vaccination campaign in urban and rural communities affected by landslides and floods in Freetown Sierra Leone.

BACKGROUND: On 14 August 2017, massive landslides and floods hit Freetown (Sierra Leone). More than 1,000 people lost their lives while approximately 6,000 people were displaced. The areas most affected included parts of the town with challenged access to basic water and sanitation facilities, with communal water sources likely contaminated by the disaster. To avert a possible cholera outbreak following this emergency, the Ministry of Health and Sanitation (MoHS), supported by the World Health Organization (WHO) and international partners, including Médecins Sans Frontières (MSF) and UNICEF, launched a two-dose pre-emptive vaccination campaign using Euvichol™, an oral cholera vaccine (OCV). METHODS: We conducted a stratified cluster survey to estimate vaccination coverage during the OCV campaign and also monitor adverse events. The study population - subsequently stratified by age group and residence area type (urban/rural) - included all individuals aged 1 year or older, living in one of the 25 communities targeted for vaccination. RESULTS: In total 3,115 households were visited, 7,189 individuals interviewed; 2,822 (39%) people in rural and 4,367 (61%) in urban areas. The two-dose vaccination coverage was 56% (95% confidence interval (CI): 51.0-61.5), 44% (95%CI: 35.2-53.0) in rural and 57% (95%CI: 51.6-62.8) in urban areas. Vaccination coverage with at least one dose was 82% (95%CI: 77.3-85.5), 61% (95%CI: 52.0-70.2) in rural and 83% (95%CI: 78.5-87.1) in urban areas. CONCLUSIONS: The Freetown OCV campaign exemplified a timely public health intervention to prevent a cholera outbreak, even if coverage was lower than expected. We hypothesised that vaccination coverage in Freetown was sufficient in providing at least short-term immunity to the population. However, long-term interventions to ensure access to safe water and sanitation are needed.

Application of Bagging, Boosting and Stacking Ensemble and EasyEnsemble Methods for Landslide Susceptibility Mapping in the Three Gorges Reservoir Area of China.

Since the impoundment of the Three Gorges Reservoir area in 2003, the potential risks of geological disasters in the reservoir area have increased significantly, among which the hidden dangers of landslides are particularly prominent. To reduce casualties and damage, efficient and precise landslide susceptibility evaluation methods are important. Multiple ensemble models have been used to evaluate the susceptibility of the upper part of Badong County to landslides. In this study, EasyEnsemble technology was used to solve the imbalance between landslide and nonlandslide sample data. The extracted evaluation factors were input into three bagging, boosting, and stacking ensemble models for training, and landslide susceptibility mapping (LSM) was drawn. According to the importance analysis, the important factors affecting the occurrence of landslides are altitude, terrain surface texture (TST), distance to residences, distance to rivers and land use. The influences of different grid sizes on the susceptibility results were compared, and a larger grid was found to lead to the overfitting of the prediction results. Therefore, a 30 m grid was selected as the evaluation unit. The accuracy, area under the curve (AUC), recall rate, test set precision, and kappa coefficient of a multi-grained cascade forest (gcForest) model with the stacking method were 0.958, 0.991, 0.965, 0.946, and 0.91, respectively, which a significantly better than the values produced by the other models.

Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model.

The spatial heterogeneity of landslide influencing factors is the main reason for the poor generalizability of the susceptibility evaluation model. This study aimed to construct a comprehensive explanatory framework for landslide susceptibility evaluation models based on the SHAP (SHapley Additive explanation)-XGBoost (eXtreme Gradient Boosting) algorithm, analyze the regional characteristics and spatial heterogeneity of landslide influencing factors, and discuss the heterogeneity of the generalizability of the models under different landscapes. Firstly, we selected different regions in typical mountainous hilly region and constructed a geospatial database containing 12 landslide influencing factors such as elevation, annual average rainfall, slope, lithology, and NDVI through field surveys, satellite images, and a literature review. Subsequently, the landslide susceptibility evaluation model was constructed based on the XGBoost algorithm and spatial database, and the prediction results of the landslide susceptibility evaluation model were explained based on regional topography, geology, and hydrology using the SHAP algorithm. Finally, the model was generalized and applied to regions with both similar and very different topography, geology, meteorology, and vegetation, to explore the spatial heterogeneity of the generalizability of the model. The following conclusions were drawn: the spatial distribution of landslides is heterogeneous and complex, and the contribution of each influencing factor on the occurrence of landslides has obvious regional characteristics and spatial heterogeneity. The generalizability of the landslide susceptibility evaluation model is spatially heterogeneous and has better generalizability to regions with similar regional characteristics. Further explanation of the XGBoost landslide susceptibility evaluation model using the SHAP method allows quantitative analysis of the differences in how much various factors contribute to disasters due to spatial heterogeneity, from the perspective of global and local evaluation units. In summary, the integrated explanatory framework based on the SHAP-XGBoost model can quantify the contribution of influencing factors on landslide occurrence at both global and local levels, which is conducive to the construction and improvement of the influencing factor system of landslide susceptibility in different regions. It can also provide a reference for predicting potential landslide hazard-prone areas and for Explainable Artificial Intelligence (XAI) research.

Landslide susceptibility prediction improvements based on a semi-integrated supervised machine learning model.

Differences in model application effectiveness, insufficient numbers of disaster samples, and unreasonable selection of non-hazard samples are common problems in landslide susceptibility studies. Therefore, in this paper, we propose a semi-integrated supervised approach to improve the prediction performance of machine learning (ML) models in landslide susceptibility studies. First, taking the lower reaches of the Jinsha River as the study area, a geospatial dataset consisting of 349 landslides, an equal number of randomly selected non-landslide points, and 12 environmental factors were randomly divided into training (70%) and testing (30%) datasets. Then, K-nearest neighbors (KNN), random forest (RF), and Bayesian-regularized neural network (BRNN) models were built. Second, the three models were combined to form an integrated weighted model. Very high- and low-prone areas were selected and, combined with the prediction results and remote sensing images, landslide and non-landslide samples were identified. The identified samples were then combined with the original samples to form new samples, which were used to sequentially construct the ensemble-supervised K-nearest neighbors (ESKNN), ensemble-supervised random forest (ESRF), and ensemble-supervised Bayesian-regularized neural network (ESBRNN) models. Finally, the area under the curve (AUC), true skill statistic (TSS), and frequency ratio (FR) values were used to test the accuracy of each model. The traditional ML model results and accuracy were improved by the semi-integrated supervised method. The ESRF model had the best prediction effect (AUC = 0.939, TSS = 0.440, and FR = 95.8%). The proposed semi-integrated supervised ML model solved the problems observed in traditional landslide susceptibility studies and provided insights for reducing variations in model applications, expanding landslide data sources, and improving non-landslide sample selection.

Post-traumatic stress and depression following a landslide linked to the 2018 floods in Kerala, India: Relevance of screening.

There is scant information on early manifestation of trauma due to catastrophic natural events and its relation with stress-related disorders. The specific objective of this study was to estimate and compare the prevalence of post-traumatic stress and depression on day 3 (D3) and week 6 (W6) following the 2018 flood in Kerala, India. In a cross-sectional study, symptoms of post-traumatic stress and depression were studied at D3 using primary care Post-Traumatic Stress Disorder screen for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) (PC-PTSD-5), and then at W6 using PC-PTSD-5, Screening Questionnaire for Disaster Mental Health, PTSD Checklist for DSM-5 (PCL-5), and Becks Depression Inventory. Majority (70 percent) were screen positive at D3 (n = 20) compared with 30 percent at W6 (n = 50), with a decreased frequency of all symptoms. Being PC-PTSD-5 screen positive at W6 was significantly associated with the presence of threat to life, physical injury, and death of relatives or neighbors. According to PCL-5, at W6, 46 percent had possible PTSD. Except damage to property, other disaster related or sociodemographic variables were not associated with the risk of having PTSD. Positive predictive value of PC-PTSD-5 (D3) for PTSD (PCL-5) at W6 was 64.3 percent. Depression and possibility of PTSD were significantly associated. A considerable proportion of victims continued to have post-traumatic stress and depression although the -frequency decreased over time. A simple screening measure may help to identify victims with possible PTSD.

Identification and deformation analysis of potential landslides after the Jiuzhaigou earthquake by SBAS-InSAR.

A World Natural Heritage Site, Jiuzhaigou, is the first nature reserve in China whose primary purpose is to protect natural scenery. On August 8, 2017, a Ms 7.0 earthquake caused many unstable slopes in Jiuzhaigou, Sichuan Province, China. In the extreme storm conditions that follow, the unstable slopes tend to develop into potential landslides, which can cause many casualties and property losses in scenic areas. Sentinel-1A ascending orbit data were obtained in this paper to establish a SAR database. The large-scale deformation rate map of the study area was obtained using a small baseline set InSAR technology. The potential landslides in the deformation area are preliminarily confirmed with remote sensing interpretation. The field verification is further carried out by studying the deformation information of the characteristic points on the potential landslides. The results show that 13 deformation zones were preliminarily identified, and three typical deformation zones were selected for coupling verification and identified as potential landslides. At the same time, further analysis shows that the four potential landslides have been in continuous linear deformation for a long time since the earthquake, posing a severe threat to the safety of local people's lives and property. The research results provide a reference for the early identification and warning of potential landslides in earthquake-prone regions.

Landslide Displacement Prediction Based on Multivariate LSTM Model.

There are many frequent landslide areas in China, which badly affect local people. Since the 1980s, there have been more than 200 landslides in China with a death toll of 30 or more people at a time, economic losses of more than CNY 10 million or significant social impact. Therefore, the study of landslide displacement prediction is very important. The traditional ARIMA and LSTM models are commonly used for forecasting time series data. In our study, a multivariable LSTM landslide displacement prediction model is proposed based on the traditional LSTM model, which integrates rainfall and reservoir water level data. Taking the Baijiabao landslide in the Three Gorges Reservoir area as an example, the data of displacement, rainfall and reservoir water level of monitoring point ZG323 from November 2006 to December 2012 were selected for this study. Our results show that the displacement prediction results of the multivariable LSTM model are more accurate than those of the ARIMA and the univariate LSTM models, and the mean square, root mean square and mean absolute errors are the smallest, which are 0.64223, 0.8014 and 0.50453 mm, respectively. Therefore, the multivariable LSTM model method has higher accuracy and better application prospects in the displacement prediction of the Baijiabao landslide, which can provide a certain reference for the displacement prediction of the same type of landslide.

Anatomical growth response of Fagus sylvatica L. to landslide movements.

Determining the age of landslide events is crucial for determining landslide risk, triggers, and also for predicting future landslide occurrence. Currently, the most accurate method for dating historical landslide events is dendrogeomorphic analysis. Unfortunately, the standard use of macroscopic growth responses of damaged trees for dating landslide activity suffers from many shortcomings. Thus, the aim of this study is to analyze in detail the growth response of trees to landslide movements at the anatomical level, a completely groundbreaking methodological approach. Ten specimens of European beech (Fagus sylvatica L.) were analyzed at two sampling heights, growing in two morphologically contrasting zones of the landslide area. Detailed anatomical analysis was focused on changes in morphometric parameters of the vessels and in the number of radial rays. The period (2008-2012) with the occurrence of the largest landslide movement (2010) recorded by long-term monitoring was analyzed. The results obtained revealed different anatomical responses in trees growing in different morphological zones of landslide. The tree responses on the ridge corresponded to the manifestations of tension wood formation, which corresponded to the stem tilting due to the landslide block movement. In the case of the trees in the trenches, root damage due to the subsidence of the landslide block blocked the flux of phytohormones, and their accumulation caused a significant reduction in the parameters of vessels and an increase in the number of rays. The study also includes recommendations in the future application of anatomical analyses in landslide research resulting from the obtained results. Thus, the obtained findings will improve the acquisition of chronological data for the purpose of landslide risk assessment.

Recording Historical Variations in Agricultural Areas Caused by Landslides and River Floods between 1977 and 2012 through Participatory Mapping in the Foothills of the Atacama Desert, Chile.

In arid areas, rural communities can be affected by erosive phenomena caused by intense rainfall. By involving such communities in participatory mapping over the last few decades, our ability to analyse the effects of these phenomena has been enhanced. The aim of this study was to evaluate participatory mapping as a tool for spatially analysing agricultural variations caused by erosive phenomena, using local people to identify chronologies of physical events so we could analyse their effects on agriculture. The study was conducted in Laonzana, Tarapacá Valley, in northern Chile. We selected the participants for the participatory mapping using specific criteria, and carried out field activities in different phases, which allowed the identification, georeferencing and registration (through participatory mapping) of the information collected in the field and from the collective memories of the participants. Three periods were studied. This provided evidence for a decrease in the number of productive sites, these being limited to the vicinity of the village. The participatory mapping technique has become a useful tool in desert and mountainous areas with low population densities for recovering experiential information from communities.

The impact of environmental change on landslides, fatal landslides, and their triggers in Pakistan (2003-2019).

The actual impact of landslides in Pakistan is highly underestimated and has not been addressed to its full extent. This study focuses on the impact which landslides had in the last 17 years, with focus on mortality, gender of deceased, main triggers (landslides and fatal landslides), and regional identification of the hotspots in Pakistan. Our study identified 1089 landslides (including rockfalls, rockslides, mudslides, mudflows, debris flows) out of which 180 landslides were fatal and claimed lives of 1072 people. We found that rain (rainfall and heavy rainfall)-related landslides were the deadliest over the entire study period. The main trigger of landslides in Pakistan is heavy rainfall which comprises over 50% of the triggers for the landslide, and combined with normal rainfall, this rate climbs to over 63%. The second main reason for landslide occurrence is spontaneous (due to rock instability, erosion, climate change, and other geological elements) with landslides accounting for 22.3% of all the landslides. Landslides caused by rain-related events amounted to 41.67% of the fatalities, whereas spontaneous landslides caused 29.44% of the deaths and the human induced events accounted for 25.5% of the fatalities. The fatal landslides accounted for 19.53% deaths of the children. Our study also found that more than 48% of the deadly landslides occurred between the months of January to April, whereas the least fatal landslides occurred in the month of June which accounted for only 3% of all the fatal landslides in Pakistan.

Integrating multiple information sources for landslide hazard assessment: the case of Italy.

Landslides are the most frequent and diffuse natural hazards in Italy causing the greatest number of fatalities and damage to urban areas. The integration of natural hazard information and social media data could improve warning systems to enhance the awareness of disaster managers and citizens about emergency events. The news about landslide events in newspapers or crowdsourcing platforms allows fast observation, surveying and classification. Currently, few studies have been produced on the combination of social media data and traditional sensors. This gap indicates that it is unclear how their integration can effectively provide emergency managers with appropriate knowledge. In this work, rainfall, human lives, and earmarked fund data sources were correlated to "landslide news". Analysis was applied to obtain information about temporal (2010-2019) and spatial (regional and warning hydrological zone scale) distribution. The temporal distribution of the data shows a continuous increase from 2015 until 2019 for both landslide and rainfall events. The number of people involved and the amount of earmarked funds do not exhibit any clear trend. The spatial distribution displays good correlation between "landslide news", traditional sensors (e.g., pluviometers) and possible effects in term of fatalities. In addition, the cost of soil protection, in monetary terms, indicates the effects of events.

Landslide disasters in eastern Uganda: post-traumatic stress disorder and its correlates among survivors in Bududa district.

BACKGROUND: Post-traumatic stress disorder is the commonly reported psychiatric morbidity among the survivors of natural disasters. However, its prevalence particularly in Bududa, Eastern Uganda is largely unknown. AIMS AND OBJECTIVES: To assess the prevalence and correlates of post-traumatic stress disorder among Bududa landslide survivors. METHODS: A community-based cross-sectional study was conducted on a sample of 587 participants. The study setting was the Bududa district, with a multistage sampling method. Pre-tested, administered interviewer PTSD Checklist-civilian version was used as screening tools between 4th and April 29th 2022. Data were analyzed using descriptive and binary logistic approaches at a 5% level of significance. RESULTS: Of the study participants, 276 (46.8%) had PTSD symptoms. Among the respondents, 250 (42.6%) were in the age range of 40 and above, 396 (67.3%) were female, 284 (48.4%) had no formal education, and 333 (56.7%) were married. Results showed that male gender (AOR: 0.47; 95% CI 0.31-0.73; p = 0.001), widowhood (AOR: 0.44; 95% CI 0.21-0.94; p = 0.034), lack of counseling (AOR: 0.44; 95% CI 0.21-0.94; p = 0.001), and duration since the landslide (AOR: 0.35; 95% CI 0.23-0.52; p = 0.001) were associated with an increased likelihood of screening for PTSD in landslide survivors. CONCLUSION: Landslides pose significant effects on the mental health of survivors. In the present study, the extent of PTSD among survivors is substantially high. male gender,, widowhood, lack of counselling, low social support, and duration since the landslide were significantly associated with PTSD. Effective screening and awareness programs among survivors should be strengthened for the prevention and treatment of psychiatric morbidity among the survivors of landslides.

Seasonality- and disaster effects on food variety and food insecurity coping strategies among a landslide-prone cohort.

BACKGROUND & AIMS: Landslides may impact on nutritional health among vulnerable populations. However, there is limited data on the seasonal effects of landslides on diet and food security. Among the 2010 and 2018 households affected by the landslides in Eastern Uganda, we assessed seasonality and disaster effects on food varieties consumed and food insecurity coping strategies. This study is among the first to report on seasonal- and disaster effects on food varieties and food insecurity coping strategies among vulnerable populations in Uganda. METHODS: We used a three-stage simple random technique to select a total of 422 households during May-August (food-plenty season) in 2019, of whom 211 had been affected by the landslides and 211 had not (controls). Six months later, in January-March (food-poor season) of 2020, 388 households were re-assessed (191 affected and 197 controls). We analyzed data only from the households that participated in both food seasons to compare results between the two food seasons. Food variety scores (FVS) were obtained by summing the frequency of weekly intakes of 86 food items while a coping index was derived based on the severity weighting of household food insecurity coping strategies. RESULTS: After adjusting for covariates, significantly lower mean (SE) FVS were among the affected than controls during the food-plenty season: 9.3 (0.5) vs 11.4 (0.3), and during the food-poor season: 7.6 (0.5) vs 10.1 (0.1) (P < 0.001 for both). The affected households were more likely to use food insecurity coping strategies compared to controls (mean [SE]: 35.2 [2.1] vs. 27.1 [1.8], P < 0.001) during the food-plenty season and the severity further increased during the food-poor season: 42.1 (2.1) vs. 28.2 (2.1) (P < 0.001). Disaster exposure was associated with both household food varieties and food insecurity coping strategies during both food seasons (P < 0.001). The adjusted models, showed that, the affected compared to the controls had a significantly higher likelihood to rely on 5 of the 11 coping strategies during food-plenty season and 9 of the 11 coping strategies during the food-poor season. CONCLUSION: Low variety diets and coping strategies among disaster affected individuals cut across seasons and implies needs for strong social protection and targeted safety nets irrespective of season.

A Digital Template for the Generic Multi-Risk (GenMR) Framework: A Virtual Natural Environment.

Extreme disasters, defined as low-probability-high-consequences events, are often due to cascading effects combined to amplifying environmental factors. While such a risk complexity is commonly addressed by the modeling of site-specific multi-risk scenarios, there exists no harmonized approach that considers the full space of possibilities, based on the general relationships between the environment and the perils that populate it. In this article, I define the concept of a digital template for multi-risk R&D and prototyping in the Generic Multi-Risk (GenMR) framework. This digital template consists of a virtual natural environment where different perils may occur. They are geological (earthquakes, landslides, volcanic eruptions), hydrological (river floods, storm surges), meteorological (windstorms, heavy rains), and extraterrestrial (asteroid impacts). Both geological and hydrological perils depend on the characteristics of the natural environment, here defined by two environmental layers: topography and soil. Environmental objects, which alter the layers, are also defined. They are here geomorphic structures linked to some peril source characteristics. Hazard intensity footprints are then generated for primary, secondary, and tertiary perils. The role of the natural environment on intensity footprints and event cascading is emphasized, one example being the generation of a "quake lake". Future developments, à la SimCity, are finally discussed.

Paleoenvironmental changes in river channel systems in alpine rockslide deposits exemplified by the Fernpass rockslide in the Tyrolian Außerfern District, Austria.

This paper describes the instability of river channel systems in alpine rockslide deposits using the Fernpass Rockslide and the river Loisach in the Tyrolian Außerfern District (Austria) as an example of paleoenvironmental developments. This is the first investigation of this kind of the Fernpass, one of the most important Alpine north-south transport connections since the bronze age. It uses geomorphological, sedimentological, onomastic and hydrogeological investigations to reconstruct the course of a late Holocene river in this area and a probabilistic simulation for dating. Tracer tests assisted in investigating the potential groundwater connections of the river systems. The findings show that the Palaeoloisach runs on the orographically right side in a marginal valley of the Fernpass furrow and changes to the orographically left side of the furrow within the Rauth suburb in the village of Biberwier. A probabilistic simulation of the Narrenbichl slip event, which changed the course of the Palaeoloisach, dates the event to an age of 664 ± 116 BC. This investigation is an important contribution to understanding Quaternary postrockslide developments, how groundwater contributes to forming postrockslide channel systems and archaeological findings occurring in populated areas.

Evaluation of Landslide Susceptibility Based on CF-SVM in Nujiang Prefecture.

At present, landslide susceptibility assessment (LSA) based on landslide characteristics in different areas is an effective measure for landslide management. Nujiang Prefecture in China has steep mountain slopes, a large amount of water and loose soil, and frequent landslide disasters, which have caused a large number of casualties and economic losses. This paper aims to understand the characteristics and formation mechanism of regional landslides through the evaluation of landslide susceptibility so as to provide relevant references and suggestions for spatial planning and disaster prevention and mitigation in Nujiang Prefecture. Based on the grid cell, this study selected 10 parameters, namely elevation, slope, aspect, lithology, proximity to faults, proximity to road, proximity to rivers, normalized difference vegetation index (NDVI), land-use type, and precipitation. Support vector machine (SVM), certainty factor method (CF), and deterministic coefficient method-support vector machine (CF-SVM) were used to evaluate the landslide susceptibility in Nujiang Prefecture. According to these three models, the study area was divided into five landslide susceptibility grades, including extremely high susceptibility, high susceptibility, moderate susceptibility, low susceptibility, and very low susceptibility. Receiver operating characteristic curve (ROC) was applied to verify the accuracy of the model. The results showed that CF model (ROC = 0.865), SVM model (ROC = 0.892), CF-SVM model (ROC = 0.925), and CF-SVM model showed better performance. Therefore, CF-SVM model results were selected for analysis. The study found that the characteristics of high and extremely high landslide-prone areas in Nujiang Prefecture have the following characteristics: intense human activities, large density of buildings and arable land, rich water resources, good economic development, perfect transportation facilities, and complex topography and landform. In addition, there is a finding inconsistent with our common sense that the distribution of landslide disasters in the study area does not decrease with the increase of NDVI value. This is because the Nujiang River basin is a high mountain canyon area with low rock strength, barren soil, and underdeveloped vegetation and root system. In an area with large slope, the probability of landslide disaster will increase with the increase of NDVI. The CF-SVM coupling model adopted in this study is a good first attempt in the study of landslide hazard susceptibility in Nujiang Prefecture.

Landslide Susceptibility Evaluation Based on Potential Disaster Identification and Ensemble Learning.

Catastrophic landslides have much more frequently occurred worldwide due to increasing extreme rainfall events and intensified human engineering activity. Landslide susceptibility evaluation (LSE) is a vital and effective technique for the prevention and control of disastrous landslides. Moreover, about 80% of disastrous landslides had not been discovered ahead and significantly impeded social and economic sustainability development. However, the present studies on LSE mainly focus on the known landslides, neglect the great threat posed by the potential landslides, and thus to some degree constrain the precision and rationality of LSE maps. Moreover, at present, potential landslides are generally identified by the characteristics of surface deformation, terrain, and/or geomorphology. The essential disaster-inducing mechanism is neglected, which has caused relatively low accuracies and relatively high false alarms. Therefore, this work suggests new synthetic criteria of potential landslide identification. The criteria involve surface deformation, disaster-controlling features, and disaster-triggering characteristics and improve the recognition accuracy and lower the false alarm. Furthermore, this work combines the known landslides and discovered potential landslides to improve the precision and rationality of LSE. This work selects Chaya County, a representative region significantly threatened by landslides, as the study area and employs multisource data (geological, topographical, geographical, hydrological, meteorological, seismic, and remote sensing data) to identify potential landslides and realize LSE based on the time-series InSAR technique and XGBoost algorithm. The LSE precision indices of AUC, Accuracy, TPR, F1-score, and Kappa coefficient reach 0.996, 97.98%, 98.77%, 0.98, and 0.96, respectively, and 16 potential landslides are newly discovered. Moreover, the development characteristics of potential landslides and the cause of high landslide susceptibility are illuminated. The proposed synthetic criteria of potential landslide identification and the LSE idea of combining known and potential landslides can be utilized to other disaster-serious regions in the world.

Landslides associated with recent road constructions in the Río Lucma catchment, eastern Cordillera Blanca, Peru.

Extensive road construction works recently took place in the remote eastern part of the Peruvian Cordillera Blanca, aiming at a better connection of isolated mountain communities with regional administrative centres. Here we document and characterize landslides associated with these road construction efforts in the Río Lucma catchment, Peru. We show that a total area of 321,332 m2 has been affected by landslides along the 47.1 km of roads constructed between 2015 and 2018. While landslides downslope the roads (48.2%) and complex landslides crossing the roads (46.4%) were the most frequent landslide types in relation to the position of the road; slide-type movement (60.7%) prevails over the flow-type movement (39.3%). Timewise, we found that 75.0% of landslides were observed simultaneously with road construction work, while the remaining 25.0% occurred up to seven months after the roads had been constructed. We plotted the lagged occurrence of these subsequent landslides against precipitation data, showing that 85.7% of them were observed during the wet season (November to April). We conclude that the majority of mapped landslides were directly associated with road constructions and that the road constructions also may set preconditions for landslides, which mainly occurred during the subsequent wet season.