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.

Land use and climate change impacts on global soil erosion by water (2015-2070).

Soil erosion is a major global soil degradation threat to land, freshwater, and oceans. Wind and water are the major drivers, with water erosion over land being the focus of this work; excluding gullying and river bank erosion. Improving knowledge of the probable future rates of soil erosion, accelerated by human activity, is important both for policy makers engaged in land use decision-making and for earth-system modelers seeking to reduce uncertainty on global predictions. Here we predict future rates of erosion by modeling change in potential global soil erosion by water using three alternative (2.6, 4.5, and 8.5) Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios. Global predictions rely on a high spatial resolution Revised Universal Soil Loss Equation (RUSLE)-based semiempirical modeling approach (GloSEM). The baseline model (2015) predicts global potential soil erosion rates of [Formula: see text] Pg yr-1, with current conservation agriculture (CA) practices estimated to reduce this by ∼5%. Our future scenarios suggest that socioeconomic developments impacting land use will either decrease (SSP1-RCP2.6-10%) or increase (SSP2-RCP4.5 +2%, SSP5-RCP8.5 +10%) water erosion by 2070. Climate projections, for all global dynamics scenarios, indicate a trend, moving toward a more vigorous hydrological cycle, which could increase global water erosion (+30 to +66%). Accepting some degrees of uncertainty, our findings provide insights into how possible future socioeconomic development will affect soil erosion by water using a globally consistent approach. This preliminary evidence seeks to inform efforts such as those of the United Nations to assess global soil erosion and inform decision makers developing national strategies for soil conservation.

Forecasting of Landslide Displacement Using a Probability-Scheme Combination Ensemble Prediction Technique.

Data-driven models have been extensively employed in landslide displacement prediction. However, predictive uncertainty, which consists of input uncertainty, parameter uncertainty, and model uncertainty, is usually disregarded in deterministic data-driven modeling, and point estimates are separately presented. In this study, a probability-scheme combination ensemble prediction that employs quantile regression neural networks and kernel density estimation (QRNNs-KDE) is proposed for robust and accurate prediction and uncertainty quantification of landslide displacement. In the ensemble model, QRNNs serve as base learning algorithms to generate multiple base learners. Final ensemble prediction is obtained by integration of all base learners through a probability combination scheme based on KDE. The Fanjiaping landslide in the Three Gorges Reservoir area (TGRA) was selected as a case study to explore the performance of the ensemble prediction. Based on long-term (2006-2018) and near real-time monitoring data, a comprehensive analysis of the deformation characteristics was conducted for fully understanding the triggering factors. The experimental results indicate that the QRNNs-KDE approach can perform predictions with perfect performance and outperform the traditional backpropagation (BP), radial basis function (RBF), extreme learning machine (ELM), support vector machine (SVM) methods, bootstrap-extreme learning machine-artificial neural network (bootstrap-ELM-ANN), and Copula-kernel-based support vector machine quantile regression (Copula-KSVMQR). The proposed QRNNs-KDE approach has significant potential in medium-term to long-term horizon forecasting and quantification of uncertainty.

Spatial and temporal patterns and the socioeconomic impacts of landslides in the tropical and mountainous Colombian Andes.

Landslides are a natural hazard that presents a major threat to human life and infrastructure. Although they are a very common phenomenon in Colombia, there is a lack of analysis that entails national and comprehensive spatial, temporal, and socioeconomic evaluations of such events based on historical records. This study provides a detailed assessment of the spatial and temporal patterns and the socioeconomic impacts associated with landslides that occurred in the country between 1900 and 2018. Two national landslide databases were consulted and this information was complemented by local and regional landslide catalogues. A total of 30,730 landslides were recorded in the 118-year period. Rainfall is the most common trigger of landslides, responsible for 92 per cent of those registered, but most fatalities (68 per cent) are due to landslides caused by volcanic activity and earthquakes. An 'fN curve' revealed a very high frequency of small and moderate fatal landslides in the time frame.

A Public Health Response to a Mudslide in Freetown, Sierra Leone, 2017: Lessons Learnt.

On August 14, 2017, a 6-kilometer mudslide occurred in Regent Area, Western Area District of Sierra Leone following a torrential downpour that lasted 3 days. More than 300 houses along River Juba were submerged; 1141 people were reported dead or missing and 5905 displaced. In response to the mudslide, the World Health Organization (WHO) Country Office in Sierra Leone moved swiftly to verify the emergency and constitute an incident management team to coordinate the response. Early contact was made with the Ministry of Health and Sanitation and health sector partners. A Public Health Emergency Operations Center was set up to coordinate the response. Joint assessments, planning, and response among health sector partners ensured effectiveness and efficiency. Oral cholera vaccination was administered to high-risk populations to prevent a cholera outbreak. Surveillance for 4 waterborne diseases was enhanced through daily reporting from 9 health facilities serving the affected population. Performance standards from the WHO Emergency Response Framework were used to monitor the emergency response. An assessment of the country's performance showed that the country's response was well executed. To improve future response, we recommend enhanced district level preparedness, update of disaster response protocols, and pre-disaster mapping of health sector partners.

Debris Flow Syndrome: Injuries and Outcomes after the Montecito Debris Flow.

On January 9, 2018, a catastrophic debris flow devastated Montecito, California. A 30-foot wall of boulders, mud, and debris ran down the hillsides at 15 miles per hour injuring dozens and causing 21 prehospital deaths. A retrospective review was conducted of the victims from the debris flow presenting to Cottage Health. Injury patterns, procedures performed, complications, length of stay, and outcomes were analyzed. Twenty-four patients were evaluated; 15 were admitted. Of the patients admitted, the most common presenting symptoms were soft tissue injuries (100%), hypothermia (67%), craniofacial injuries (67%), corneal abrasions (53%), and orthopedic injuries (47%), as well as loss of an immediate family member during the incident (73%). Procedures included skin irrigation (93%), operative soft tissue debridement (47%), body orifice irrigation due to mud impaction (40%), and orthopedic repair of fractures and ligaments (40%). All survived to discharge. "Debris flow syndrome" can be defined as a pattern of injuries, including soft tissue injuries, hypothermia, craniofacial trauma, corneal abrasions, orthopedic injuries, and mud impaction. Managing the debris flow syndrome requires co-ordinated and specialized care.

Using citizen science to expand the global map of landslides: Introducing the Cooperative Open Online Landslide Repository (COOLR).

Robust inventories are vital for improving assessment of and response to deadly and costly landslide hazards. However, collecting landslide events in inventories is difficult at the global scale due to inconsistencies in or the absence of landslide reporting. Citizen science is a valuable opportunity for addressing some of these challenges. The new Cooperative Open Online Landslide Repository (COOLR) supplements data in a NASA-developed Global Landslide Catalog (GLC) with citizen science reports to build a more robust, publicly available global inventory. This manuscript introduces the COOLR project and its methods, evaluates the initial citizen science results from the first 13 months, and discusses future improvements to increase the global engagement with the project. The COOLR project (https://landslides.nasa.gov) contains Landslide Reporter, the first global citizen science project for landslides, and Landslide Viewer, a portal to visualize data from COOLR and other satellite and model products. From March 2018 to April 2019, 49 citizen scientists contributed 162 new landslide events to COOLR. These events spanned 37 countries in five continents. The initial results demonstrated that both expert and novice participants are contributing via Landslide Reporter. Citizen scientists are filling in data gaps through news sources in 11 different languages, in-person observations, and new landslide events occurring hundreds and thousands of kilometers away from any existing GLC data. The data is of sufficient accuracy to use in NASA susceptibility and hazard models. COOLR continues to expand as an open platform of landslide inventories with new data from citizen scientists, NASA scientists, and other landslide groups. Future work on the COOLR project will seek to increase participation and functionality of the platform as well as move towards collective post-disaster mapping.

Mine landslide susceptibility assessment using IVM, ANN and SVM models considering the contribution of affecting factors.

The fragile ecological environment near mines provide advantageous conditions for the development of landslides. Mine landslide susceptibility mapping is of great importance for mine geo-environment control and restoration planning. In this paper, a total of 493 landslides in Shangli County, China were collected through historical landslide inventory. 16 spectral, geomorphic and hydrological predictive factors, mainly derived from Landsat 8 imagery and Global Digital Elevation Model (ASTER GDEM), were prepared initially for landslide susceptibility assessment. Predictive capability of these factors was evaluated by using the value of variance inflation factor and information gain ratio. Three models, namely artificial neural network (ANN), support vector machine (SVM) and information value model (IVM), were applied to assess the mine landslide sensitivity. The receiver operating characteristic curve (ROC) and rank probability score were used to validate and compare the comprehensive predictive capabilities of three models involving uncertainty. Results showed that ANN model achieved higher prediction capability, proving its advantage of solve nonlinear and complex problems. Comparing the estimated landslide susceptibility map with the ground-truth one, the high-prone area tends to be located in the middle area with multiple fault distributions and the steeply sloped hill.

Estimating landslides vulnerability in Rwanda using analytic hierarchy process and geographic information system.

Landslides are among hazards that undermine the social, economic, and environmental well-being of the vulnerable community. Assessment of landslides vulnerability reveals the damages that could be recorded, estimates the severity of the impact, and increases the preparedness, response, recovery, and mitigation as well. This study aims to estimate landslides vulnerability for the western province of Rwanda. Field survey and secondary data sources identified 96 landslides used to prepare a landslides inventory map. Ten factors-altitude, slope angles, normalized difference vegetation index (NVDI), land use, distance to roads, soil texture, rainfall, lithology, population density, and possession rate of communication tools-were analyzed. The Analytical Hierarchy Process (AHP) model was used to weight and rank the vulnerability conditioning factors. Then the Weighted Linear Combination (WLC) in geographic information system (GIS) spatially estimated landslides vulnerability over the study area. The results indicated the altitude (19.7%), slope angles (16.1%), soil texture (14.3%), lithology (13.5%), and rainfall (12.2%) as the major vulnerability conditioning parameters. The produced landslides vulnerability map is divided into 5 classes: very low, low, moderate, high and very high. The proposed method is validated by using the relative landslides density index (R-index) method, which revealed that 35.4%, 25%, and 23.9% of past landslides are observed within moderate, high, and very high vulnerability zones, respectively. The consistency of validation indicates good performance of the methodology used and the vulnerability map prepared. The results can be used by policy makers to recognize hazard vulnerability lessening and future planning needs. Integr Environ Assess Manag 2019;00:000-000. © 2019 SETAC.

Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China.

The main goal of this study was to use the synthetic minority oversampling technique (SMOTE) to expand the quantity of landslide samples for machine learning methods (i.e., support vector machine (SVM), logistic regression (LR), artificial neural network (ANN), and random forest (RF)) to produce high-quality landslide susceptibility maps for Lishui City in Zhejiang Province, China. Landslide-related factors were extracted from topographic maps, geological maps, and satellite images. Twelve factors were selected as independent variables using correlation coefficient analysis and the neighborhood rough set (NRS) method. In total, 288 soil landslides were mapped using field surveys, historical records, and satellite images. The landslides were randomly divided into two datasets: 70% of all landslides were selected as the original training dataset and 30% were used for validation. Then, SMOTE was employed to generate datasets with sizes ranging from two to thirty times that of the training dataset to establish and compare the four machine learning methods for landslide susceptibility mapping. In addition, we used slope units to subdivide the terrain to determine the landslide susceptibility. Finally, the landslide susceptibility maps were validated using statistical indexes and the area under the curve (AUC). The results indicated that the performances of the four machine learning methods showed different levels of improvement as the sample sizes increased. The RF model exhibited a more substantial improvement (AUC improved by 24.12%) than did the ANN (18.94%), SVM (17.77%), and LR (3.00%) models. Furthermore, the ANN model achieved the highest predictive ability (AUC = 0.98), followed by the RF (AUC = 0.96), SVM (AUC = 0.94), and LR (AUC = 0.79) models. This approach significantly improves the performance of machine learning techniques for landslide susceptibility mapping, thereby providing a better tool for reducing the impacts of landslide disasters.

Unmanned aerial vehicle (UAV)-based monitoring of a landslide: Gallenzerkogel landslide (Ybbs-Lower Austria) case study.

In the present study, UAV-based monitoring of the Gallenzerkogel landslide (Ybbs, Lower Austria) was carried out by three flight missions. High-resolution digital elevation models (DEMs), orthophotos, and density point clouds were generated from UAV-based aerial photos via structure-from-motion (SfM). According to ground control points (GCPs), an average of 4 cm root mean square error (RMSE) was found for all models. In addition, light detection and ranging (LIDAR) data from 2009, representing the prefailure topography, was utilized as a digital terrain model (DTM) and digital surface model (DSM). First, the DEM of difference (DoD) between the first UAV flight data and the LIDAR-DTM was determined and according to the generated DoD deformation map, an elevation difference of between - 6.6 and 2 m was found. Over the landslide area, a total of 4380.1 m3 of slope material had been eroded, while 297.4 m3 of the material had accumulated within the most active part of the slope. In addition, 688.3 m3 of the total eroded material had belonged to the road destroyed by the landslide. Because of the vegetation surrounding the landslide area, the Multiscale Model-to-Model Cloud Comparison (M3C2) algorithm was then applied to compare the first and second UAV flight data. After eliminating both the distance uncertainty values of higher than 15 cm and the nonsignificant changes, the M3C2 distance obtained was between - 2.5 and 2.5 m. Moreover, the high-resolution orthophoto generated by the third flight allowed visual monitoring of the ongoing control/stabilization work in the area.

The Vulnerability of People to Landslides: A Case Study on the Relationship between the Casualties and Volume of Landslides in China.

The lack of a detailed landslide inventory makes research on the vulnerability of people to landslides highly limited. In this paper, the authors collect information on the landslides that have caused casualties in China, and established the Landslides Casualties Inventory of China. 100 landslide cases from 2003 to 2012 were utilized to develop an empirical relationship between the volume of a landslide event and the casualties caused by the occurrence of the event. The error bars were used to describe the uncertainty of casualties resulting from landslides and to establish a threshold curve of casualties caused by landslides in China. The threshold curve was then applied to the landslide cases occurred in 2013 and 2014. The validation results show that the estimated casualties of the threshold curve were in good agreement with the real casualties with a small deviation. Therefore, the threshold curve can be used for estimating potential casualties and landslide vulnerability, which is meaningful for emergency rescue operations after landslides occurred and for risk assessment research.

Landslides susceptibility change over time according to terrain conditions in a mountain area of the tropic region.

Susceptibility to landslides in mountain areas results from the interaction of various factors related to relief formation and soil development. The assessment of landslide susceptibility has generally taken into account individual events, or it has been aimed at establishing relationships between landslide-inventory maps and maps of environmental factors, without considering that such relationships can change in space and time. In this work, temporal and space changes in landslides were analysed in six different combinations of date and geomorphological conditions, including two different geological units, in a mountainous area in the north-centre of Venezuela, in northern South America. Landslide inventories from different years were compared with a number of environmental factors by means of logistic regression analysis. The resulting equations predicted landslide susceptibility from a range of geomorphometric parameters and a vegetation index, with diverse accuracy, in the study area. The variation of the obtained models and their prediction accuracy between geological units and dates suggests that the complexity of the landslide processes and their explanatory factors changed over space and time in the studied area. This calls into question the use of a single model to evaluate landslide susceptibility over large regions.

Injury Patterns After the Landslide Disaster in Oshima, Tokyo, Japan on October 16, 2013.

INTRODUCTION: Landslides represent a frequent and threatening natural disaster. The aim of this study was to investigate the injury patterns observed after a landslide and to discuss how to minimize the damage caused by a landslide disaster. METHODS: A landslide occurred on Oshima Island, Japan, on October 16, 2013. A total of 49 victims with landslide-related injuries were identified and analyzed. RESULTS: The patients ranged in age from 5 to 89 years with an average age of 61.0±19.3 years. Of all patients, 69.4% were triaged as black. Of 15 patients who were treated in the nearest hospital (the only hospital on the island), 8 were triaged as red and yellow with severe chest or pelvic injury and a high Injury Severity Score (average score, 25.6; range, 4-45). Of these, 75% had chest injury and 75% had pelvic injury. The percentage of chest and/or pelvic injury was 100% in patients triaged as red or yellow. Traumatic asphyxia was diagnosed in 62.5% of these patients. CONCLUSIONS: Compression of the trunk was the main injury in patients triaged as red or yellow after this landslide disaster. Evacuation in advance, the rapid launch of emergency medical support, and knowledge of this specific injury pattern are essential to minimize the potential damage resulting from landslide disasters.

Weather-Related Flood and Landslide Damage: A Risk Index for Italian Regions.

The frequency of natural hazards has been increasing in the last decades in Europe and specifically in Mediterranean regions due to climate change. For example heavy precipitation events can lead to disasters through the interaction with exposed and vulnerable people and natural systems. It is therefore necessary a prevention planning to preserve human health and to reduce economic losses. Prevention should mainly be carried out with more adequate land management, also supported by the development of an appropriate risk prediction tool based on weather forecasts. The main aim of this study is to investigate the relationship between weather types (WTs) and the frequency of floods and landslides that have caused damage to properties, personal injuries, or deaths in the Italian regions over recent decades. In particular, a specific risk index (WT-FLARI) for each WT was developed at national and regional scale. This study has identified a specific risk index associated with each weather type, calibrated for each Italian region and applicable to both annual and seasonal levels. The risk index represents the seasonal and annual vulnerability of each Italian region and indicates that additional preventive actions are necessary for some regions. The results of this study represent a good starting point towards the development of a tool to support policy-makers, local authorities and health agencies in planning actions, mainly in the medium to long term, aimed at the weather damage reduction that represents an important issue of the World Meteorological Organization mission.

Emerald ash borer and the urban forest: Changes in landslide potential due to canopy loss scenarios in the City of Pittsburgh, PA.

Emerald ash borer is expected to kill thousands of ash trees in the eastern U.S. This research develops tools to predict the effect of ash tree loss from the urban canopy on landslide susceptibility in Pittsburgh, PA. A spatial model was built using the SINMAP (Stability INdex MAPping) model coupled with spatially explicit scenarios of tree loss (0%, 25%, 50%, and 75% loss of ash trees from the canopy). Ash spatial distributions were estimated via Monte Carlo methods and available vegetation plot data. Ash trees are most prevalent on steeper slopes, likely due to urban development patterns. Therefore, ash loss disproportionately increases hillslope instability. A 75% loss of ash resulted in roughly 800 new potential landslide initiation locations. Sensitivity testing reveals that variations in rainfall rates, and friction angles produce minor changes to model results relative to the magnitude of parameter variation, but reveal high model sensitivity to soil density and root cohesion values. The model predictions demonstrate the importance of large canopy species to urban hillslope stability, particularly on steep slopes and in areas where soils tend to retain water. To improve instability predictions, better characterization of urban soils, particularly spatial patterns of compaction and species specific root cohesion is necessary. The modeling framework developed in this research will enhance assessment of changes in landslide risk due to tree mortality, improving our ability to design economically and ecologically sustainable urban systems.

Spatial prediction of landslide susceptibility in parts of Garhwal Himalaya, India, using the weight of evidence modelling.

Garhwal Himalaya in northern India has emerged as one of the most prominent hot spots of landslide occurrences in the Himalaya mainly due to geological causes related to mountain building processes, steep topography and frequent occurrences of extreme precipitation events. As this region has many pilgrimage and tourist centres, it is visited by hundreds of thousands of people every year, and in the recent past, there has been rapid development to provide adequate roads and building infrastructure. Additionally, attempts are also made to harness hydropower by constructing tunnels, dams and reservoirs and thus altering vulnerable slopes at many places. As a result, the overall risk due to landslide hazards has increased many folds and, therefore, an attempt was made to assess landslide susceptibility using 'Weights of Evidence (WofE)', a well-known bivariate statistical modelling technique implemented in a much improved way using remote sensing and Geographic Information System. This methodology has dual advantage as it demonstrates how to derive critical parameters related to geology, geomorphology, slope, land use and most importantly temporal landslide distribution in one of the data scarce region of the world. Secondly, it allows to experiment with various combination of parameters to assess their cumulative effect on landslides. In total, 15 parameters related to geology, geomorphology, terrain, hydrology and anthropogenic factors and 2 different landslide inventories (prior to 2007 and 2008-2011) were prepared from high-resolution Indian remote sensing satellite data (Cartosat-1 and Resourcesat-1) and were validated by field investigation. Several combinations of parameters were carried out using WofE modelling, and finally using best combination of eight parameters, 76.5 % of overall landslides were predicted in 24 % of the total area susceptible to landslide occurrences. The study has highlighted that using such methodology landslide susceptibility assessment can be carried out in vast stretches of Himalaya in short time in order to assess the impact of development as well as climate change/variability. The resultant map can play a critical role in selecting areas for remedial measures for slope stabilisation as well planning for future development of the region.