Assessing the impact of governance policies on landslide risk in Brazilian municipalities

In addition to global population growth due to migration from rural areas to urban areas, population density is constantly increasing in certain regions, thereby necessitating the introduction of new settlements in these regions. However, in the selection of settlement areas, no sufficient preliminary examinations have been conducted; consequently, various natural disasters may cause significant life and property losses. Herein, the most suitable settlement areas were determined using GIS (geographic information systems) in Canik District, where the population is continuously increasing. Therefore, this study aimed to incorporate a new perspective into studies on this subject. Within the scope of the study, landslide and flood risks, which are among the most important natural disasters in the region, were primarily evaluated, and high-risk areas were determined. Elevation, slope, aspect, curvature, lithology, topographic humidity index (TWI), and proximity to river parameters were used to produce flood susceptibility maps. A digital elevation model (DEM) of the study area was produced using contours on the 1/25,000 scaled topographic map. The elevation, slope, aspect, curvature, and TWI parameters were produced from the DEM using the relevant analysis routines of ArcGIS software. The raster map of each parameter was divided into 5 subclasses using the natural breaks classification method. In the reclassified raster maps, the most flood-sensitive or flood-prone subclasses were assigned a value of 5, and the least sensitive subclasses were assigned a value of 1. Then, the reclassified maps of the 7 parameters were collected using the "map algebra" function of ArcGIS 10.5 software, and the flood susceptibility index (FSI) map of the study area was obtained. The flood susceptibility map of the study area was obtained by dividing the FSI into 5 subclasses (very low, low, moderate, high, and very high) according to the natural breaks classification method. Thereafter, suitable and unsuitable areas in terms of biocomfort, which affects people's health, peace, comfort, and psychology and is significant in terms of energy efficiency, were determined. At the last stage of the study, the most suitable settlement areas that were suitable in terms of both biocomfort and low levels of landslide and flood risks were determined. The calculated proportion of such areas to the total study area was only 2.1%. Therefore, because these areas were insufficient for the establishment of new settlements, areas that had low landslide and flood risks but were unsuitable for biocomfort were secondarily determined; the ratio of these areas was calculated as 56.8%. The remaining areas were inconvenient for the establishment of settlements due to the risk of landslides and floods; the ratio of these areas was calculated as 41.1%. This study is exemplary in that the priority for the selection of settlement areas was specified, and this method can be applied for selecting new settlements for each region considering different criteria. Due to the risk of landslides or flooding in the study area, the areas unsuitable for establishing a settlement covered approximately 41.1% of the total study area. The areas that had low flood and landslide risks but were suitable for biocomfort constituted only 2.1% of the study area. In approximately 56.8% of the study area, the risk of landslides or floods was low, and these areas were unsuitable in terms of biocomfort. Therefore, these areas were secondarily preferred as settlement areas. The most suitable areas for settlements constituted only 0.19% of the total study area, and these areas will not be able to meet the increasing demand for settlement area. Therefore, it is recommended to select areas that do not have the risk of landslides and floods but are unsuitable for biocomfort. This study reveals that grading should be performed in the selection of settlement areas. When choosing a settlement area in any region, possible natural disasters in the region should be identified first, and these disasters should be ordered in terms of their threat potential. Moreover, biocomfort areas suitable for settlements should be considered. In the next stages of settlement area selection, the criteria that affect the peace and comfort of people, such as distance to pollution sources, distance to noise sources, and proximity to natural areas, should also be evaluated. Thus, a priority order should be created for the selection of settlement areas using various other criteria.

Among other natural hazards, Landslides are the most prominent and frequently occurring natural disaster in the state of Himachal Pradesh with higher socio-economical losses. About 0.42 million sq.kms of area are prone to landslide activities in our country that is excluding the snow covered areas. The current research focuses on estimating the landslide risk zones of the Shimla Tehsil, Himachal Pradesh using various statistical models. Landslide contributing factors as such Landuse Landcover, Elevation, Slope, Lithology, Soil, Geology and Geomorphology has been used to assess the Landslide risk factors. Data obtained from LANDSAT 8 OLI sensors, SRTM DEM, Soil and Land Use Survey of India and SOI Toposheets have been used as sources. Weighted Overlay, Fuzzy logic and Analytical Hierarchical Process models will be used to categorize the Vulnerability and risk Zones of the study area. The causative factors were analyzed and processed in GIS environment. These values will be then being integrated using various studied models to produce individual landslide vulnerability and risk zones. The results reveal that most of the study area falls under Very Low risk category with a total coverage of 67.34%. Low and Moderate area covers about 23% and 9.13% of the study area. Higher risk areas only account for about 0.46%. Higher percent of the study area is mostly covered by settlements. National highways, Metal roads, Slopes and Denser settlements are located along the Moderate and low risk areas. The results retrieved from the WOM model reveals a total of 55% of the area comes under very low category. Low and Moderate category covers about 31.4% and 10.6% of the study area. High and Very High category cover a total of 1.9% together.

Landslides (generally including debris flows) seriously threaten life and property. Studies on landslide risk are of great significance for regional disaster prevention and mitigation. In this study, the grid of 1 km ×1 km was selected as the assessment unit to exhibit the spatial distribution of landslide risk in China. With the support of Geographic Information System (GIS) technique, the combined model of certainty factor model (CF) and logistic regression model (LR) was applied to assess the landslide hazard, Liu’s method was used to evaluate the landslide vulnerability, and the product of hazard multiplied by vulnerability was used to represent the landslide risk. The spatial patterns of the hazard, vulnerability and risk of landslides were displayed on the national scale of China. The results indicated that the high hazard zones are commonly distributed in the south of Yangtze River, especially the very high hazard zones are concentrated in the southwest of China. The high vulnerability zones are mainly distributed in the eastern coastal cities with developed economy and dense population. The distributional pattern of landslide risk is approximately divided by Heihe-Tengchong population density line. The west of the line is mainly distributed low risk areas; whereas the east of the line contains moderate and high risk areas. The classes of very low, low, moderate, high and very high risk areas account for 40.51%, 18.34%, 33.86%, 7.28% and 0.01% of the total area respectively. The macro-scale regional assessment is needed in China, which may benefit the top design of landslide risk reduction and territorial functional zoning.

Climate change has increased the frequency and scale of heavy rainfall, increasing the risk of shallow landslides due to heavy rainfall. In recent years, ecosystem-based disaster risk reduction (Eco-DRR) has attracted attention as one way to reduce disaster risks. Vegetation is known to increase soil strength through its root system and reduce the risk of shallow landslides. To reduce the risk of shallow landslides using vegetation, it is necessary to quantitatively evaluate the effects that vegetation has on shallow landslides. In this study, we constructed a generalized linear model (GLM) and random forest (RF) model to quantitatively evaluate the impact of differences in the vegetation, such as grasslands and forests, on the occurrence of shallow landslides using statistical methods. The model that resulted in the lowest AIC in the GLM included elevation, slope angle, slope aspect, undulation, TWI, geology, and vegetation as primary factors, and the hourly rainfall as a trigger factor. The slope angle, undulation, and hourly rainfall were selected as significant explanatory variables that contribute positively to shallow landslides. On the other hand, elevation and TWI were selected as significant explanatory variables that contribute negatively to shallow landslides. Significant differences were observed among multiple categories of vegetation. The probability of shallow landslide in secondary grasslands was approximately three times that of coniferous and broadleaf forests, and approximately nine times that of broadleaf secondary forests. The landslide probability of shrubs was approximately four times that of coniferous and broadleaf forests, and approximately ten times that of broadleaf secondary forests. The results of constructing the RF model showed that the importance was highest for the hourly rainfall, followed by geology, then elevation. AUC values for the GLM and RF model were 0.91 and 0.95, respectively, indicating that highly accurate models were constructed. We quantitatively showed the impact of differences in vegetation on shallow landslides. The knowledge obtained in this study will be essential for considering appropriate vegetation management to reduce the risk of future shallow landslides.

Abstract. Inundations and landslides are widespread phenomena in Italy, where they cause severe damage and pose a threat to the population. Little is known about the public perception of landslide and flood risk. This is surprising, as an accurate perception is important for the successful implementation of many risk reduction or adaptation strategies. In an attempt to address this gap, we have conducted two national surveys to measure the perception of landslide and flood risk amongst the population of Italy. The surveys were conducted in 2012 and 2013, and consisted of approximately 3100 computer-assisted telephone interviews for each survey. The samples of the interviewees were statistically representative for a national-scale quantitative assessment. The interviewees were asked questions designed to obtain information on (i) their perception of natural, environmental, and technological risks, (ii) direct experience or general knowledge of the occurrence of landslides and floods in their municipality, (iii) perception of the possible threat posed by landslides and floods to their safety, (iv) general knowledge on the number of victims affected by landslides or floods, and on (v) the factors that the interviewees considered important for controlling landslide and flood risks in Italy. The surveys revealed that the population of Italy fears technological risks more than natural risks. Of the natural risks, earthquakes were considered more dangerous than floods, landslides, and volcanic eruptions. Examination of the temporal and geographical distributions of the responses revealed that the occurrence of recent damaging events influenced risk perception locally, and that the perception persisted longer for earthquakes and decreased more rapidly for landslides and floods. We explain the difference by the diverse consequences of the risks. The interviewees considered inappropriate land management the main cause of landslide and food risk, followed by illegal construction, abandonment of the territory, and climate change. Comparison of the risk perception with actual measures of landslide and flood risk, including the number of fatal events, the number of fatalities, and the mortality rates, revealed that in most of the Italian regions, the perception of the threat did not match the long-term risk posed to the population by landslides and floods. This outcome points to a need to foster an understanding of the public towards landslide and flood hazards and risks in Italy.

Abstract. Only a few studies have investigated the geographical and temporal variations in the frequency and distribution of rainfall-induced landslides, and the consequences of the variations on landslide risk. Lack of information limits the possibility to evaluate the impact of environmental and climate changes on landslide frequency and risk. Here, we exploit detailed historical information on landslides and rainfall in Calabria, southern Italy, between 1921 and 2010 to study the temporal and the geographical variation in the occurrence of rainfall-induced landslides and in their impact on the population. We exploit a catalogue with information on historical landslides from June 1920 to December 2010, and daily rainfall records obtained by a network of 318 rain gauges in the same period, to reconstruct 448 493 rainfall events (RE). Combining the rainfall and the landslide information, we obtain a catalogue of 1466 rainfall events with landslides (REL), where an REL is the occurrence of one or more landslide during or immediately after a rainfall event. We find that (i) the geographical and the temporal distributions of the rainfall-induced landslides have changed in the observation period, (ii) the monthly distribution of the REL has changed in the observation period, and (iii) the average and maximum cumulated event rainfall that have resulted in landslides in the recent 30-year period 1981–2010 are lower than the rainfall necessary to trigger landslides in previous periods, whereas the duration of the RE that triggered landslides has remained the same. We attribute the changes to variations in the rainfall conditions and to an increased vulnerability of the territory. To investigate the variations in the impact of REL on the population, we compared the number of REL in each of the 409 municipalities in Calabria with the size of the population in the municipalities measured by national Censuses conducted in 1951, 1981, and 2011. We adopted two strategies; the first strategy considered impact as IREL = #REL / P, and the second strategy measured impact as RREL = #REL × P, where #REL is the total number of REL in a period, and P is the size of the population in the same period and geographical area. The analysis has revealed a complex pattern of changes in the impact of rainfall-induced landslides in Calabria in the recent past, with areas where IREL and RREL have increased, and other areas where they have decreased. Municipalities where IREL has increased are mainly in the mountains, and municipalities where RREL has increased are mainly along the coasts. The complexity of the changes in the frequency and impact of rainfall-induced landslides observed in Calabria suggests that it remains difficult and uncertain to predict the possible variations in the frequency and impact of landslide in response to future climatic and environmental changes.

Extended Abstract Introduction As a type of mass movement involving slow or rapid movement of soil, rock material or both on the lower hillsides, landslide is under the effect of gravity.Landslide is recognized as one of the most common geological disasters causing worldwide damages and casualties.Landslide susceptibility maps provide important and valuable information,including time scale of possible future landslides, which are usedfor predicting landslide hazards. Since predicting the time of landslide occurrence is beyond the capability of science and knowledge, identifying areas susceptible to landslide and ranking them can extensively restrict the damages caused by landslide. Therefore, it is essential to zone landslide risk and identify factors affecting it. Analytic Network Process(ANP) is aGIS-based Multi-Criteria Decision Analysis(GIS MCDA) method successfully applied to many decision-making systems. The present study seeks to evaluate landslide risk and achieve a zoning map for the sub-basin under study using ANP and Weighted Overlaymethods. Materials and Methods Based on the literature and using different experts’ viewpoint, criteria affecting landslide risk were identified and five major criteria including topography, land use and land cover, geology, hydrometry and infrastructure were selected. The selected criteria include the following sub-criteria: slope, slope direction, curvature, elevation, lithology, soil type, land use, vegetation density, distance from roads, distance from habitat, river and drainage density and precipitation. The effective factor layers were standardized and a specific scale was defined for their units.Then, each layer was assigned a weight based on its role and importanceusing Analytic Network Process.Proposed to modify Analytic Hierarchical Process(AHP), this method (ANP) relies on the analyses of the human brain for complex and fuzzy problems.Network Analysis Process generally includes the following steps: determining indicators, criteria and options;classifying identified criteria into clusters and elements; determining the relationship between clusters, elements and options; performing pairwise comparisons between clusters, elements and options, and finally calculating the final weight of elements and options. UsingWeightedOverlaymethod, these elements were then integrated with their related coefficients and the final landslide risk map was obtained. Results and discussion Each criteria and sub-criteria were weighted using Analytic Network Processmethod.Topographic and land cover criteria had the most and hydrographic criteria had the least impact on the landslide occurrence. According to the final map, most landslides have occurred in eastern and southern slopes at an altitude of 500 to 2,200 meters. Moreover, 17/31% of the study area was located in the very high-risk class and 33% in the high risk class (about half of the area has high potential of landslide). Previous landslide data were used to assess the landslide zoning map results. Results indicate that most landslides have occurred in the high risk class (about 35% of landslides) and only about 4% of landslides have occurred in the very low risk class. Conclusion Landslide is one of the natural hazards causing serious harms and problems for human life. Identifying the factors affecting landslide and zoning its hazard is especially important for the identification of risky and susceptible areas.So, landslides were selected as one of the main topics of the study with the aim of controlling and managing its hazards.The ANP network analysis method was used to model and predict landslide risk in this research.Each criteria and sub-criteria were weighted and overlapped to producethe map of relative landsliderisk.The lowest risk was observed in the northern parts of the region, and the highest landslide risk was observed in the northern hillsides with higher humidity.WeightedOverlaymethod and network analysis model were effective in predicting landslide susceptibility and producing landslide zoning map.

The presented work was performed within the scope of the IPL project no. 197, entitled ‘Low frequency, highly damaging potential landslide events in ‘low-risk’ regions – challenges for hazard and risk management’. The Czech Republic is an example of a landslide ‘low-risk’ country with all the related challenges for long-term and sustainable landslide risk management. We argue that the main challenge is to raise and maintain a corresponding level of public attention to landslide hazards and risks. Since hazard and risk recognition by the potentially affected people is the main precondition of any effective risk mitigation, we performed several tasks to provide as yet unavailable information about specific aspects of the occurrence of landslides in the Czech Republic which may attract the attention of the public, including the responsible authorities, to the landslide risk. These aspects include new ways of updating a landslide inventory and compilation of a database of the cost of landslide mitigation works paid by the government. Landslide inventories derived from web sources, the unified system of traffic information of the national road authority and information collected by the Czech Geological Survey were compared. The landslide inventory compiled by the Czech Geological Survey is the most complete, but in some cases, the other two inventories could be used to complete it with landslide events not yet registered. Landslide-related expenses of the state budget are not negligible and their uneven spatial distribution cannot be explained by landslide occurrences only, which calls for in-depth risk assessment.

Landslide risk assessment in mining areas using hybrid machine learning methods under fuzzy environment

Qinghai is rich in mineral resources, but frequent and large-scale mineral mining has caused secondary damage to the fragile primary surface and produced a large number of landslide disasters. In complex geological environments such as glacier ablation and frequent tectonic movements, a complete quantitative evaluation method for landslide risk in high-cold mining areas has not yet been formed. In view of this, this article uses the field survey and remote sensing data of the Datong mining area in Qinghai Province in 2012 as the basic data. We comprehensively considered five first-level factors (13 s-level factors) including topography, lithological structure, mining engineering activities, land use, and dynamic deformation as evaluation indicators for landslide susceptibility in mining areas, and used the Topographic Wetness Index (TWI) and the Human Engineering Activity Intensity (HEAI) to quantitatively estimate the hazard of landslide according to the landslide trigger mechanism. The weight-of-evidence approach was used for landslide hazard and risk mapping under different landslide--inducing conditions. The results indicate that the extremely high-hazard areas induced by human engineering activities account for 14% of the total area, and the extremely high-risk areas account for 13% of the total area in the Datong mining area, and the area of the extremely high-risk area is large; the landslide risk assessment mapping model constructed in this study can effectively evaluate the probability of slope instability caused by rainfall and human engineering activities. The effective value of the receiver operating characteristic (ROC) curve of the sensitivity assessment model reaches 0.863, and the evaluation results are consistent with reality; using the weight-of-evidence model for landslide risk assessment is more in line with the actual situation in alpine mining areas, and is more suitable for guiding landslide risk management and disaster prevention and mitigation in mining areas.

Human activity influences both the occurrence and impact of landslides in mountainous environments. Population pressure and the associated land-use changes are assumed to exacerbate landslide risk, yet there is a lack of statistical evidence to support this claim, especially in the Global South where historical records are scarce. In this work, we explore the interactions between population, deforestation and landslide risk in the Kivu Rift in Africa. To do so, we develop a holistic landslide risk model that evaluates 58 years of population and forest-cover trends. We show that the current landslide risk in the eastern Democratic Republic of the Congo (DRC) is twice as high as in neighbouring Rwanda and Burundi. Congolese households, on average, populate more hazardous terrain, probably as a result of conflicts and economic pull factors such as mining. Moreover, the recent large-scale deforestation of primary rainforest in the DRC has considerably exacerbated the landslide risk. Our analysis demonstrates how the legacy of deforestation, conflicts and population dynamics is reflected in the landslide risk in the Kivu Rift. A mapping study covering over 50 years finds that landslide risk is much higher in the Democratic Republic of the Congo, due to more recent deforestation and more people living in more susceptible areas, than in similar landscapes in neighbouring countries.

Kurdistan dam watershed with an area of 120.15 km2is located in the northeast of Saqez city.The purpose of this study is to analyze the tectonic activity of the basin and the relationship between faults and landslide risk using the information value model. First, the region is divided into 15 sub-basins using ArcHydro software and morphotectonic indexes including Stream Length Gradient Index (SL), Asymmetric Factor (AF), Hypsometric Integra (Hi), Transverse Topographic Symmetry Factor (T) and basin shape ratio (Bs) are calculated and the results are calculated. Their evaluation was evaluated with active tectonics index (IAT). In order to evaluate the relationship between faults and landslide risk, first a map of faults and maps of factors affecting landslides were prepared by processing satellite images of Landsat 8 sensor and ENVI software and field observations. Then, using the information value model, landslide hazard map of the basin was prepared and combined with the fault map and the relationship between faults and landslide risk in ArcGIS. 10.3 environment was reviewed. The results of calculating the relative active tectonics indices show that 77.77% of the basin is tectonically active in the active class and in the information value model 55.55% of the occurrence of landslides is in the range of high instability risk. Distance coefficients from faults in both positive models and most landslides occurred at a distance of less than 1000 meters and due to active tectonics there is a significant relationship between faults and areas with high landslide risk in the basin. The results showed that the information value model has better performance for landslide risk zoning. In general, diversity of lithology, topography, abundant national and local faults and climatic conditions can be mentioned as the most important factors in the occurrence and distribution of landslides.

<em>Located on a hilly topography with a steep slope, highlighted the importance of settlement arrangement based on a landslide risk assessment in Girirejo village, Imogiri, Bantul, Yogyakarta. This study aims to map landslides risk, identify houses in the landslide risk zone, and provide recommendations for settlement arrangements. The research begins with observation, interviews, and focus group discussion. Disaster risk mapping and analysis were carried out through weighting method based on Perka BNPB No.2 of 2012 concerning General Guidelines for Disaster Risk Assessment and a formula with parameters of hazard, vulnerability, and capacity. Results showed the medium to a high-level of landslide risk was dominated by northern and eastern parts of Girirejo (21 families in red-zone, 23 families in yellow-zone), while western and southern regions had a low landslide risk level. This research also provided a formulation of settlements concept for medium and high-risk areas by considering landslides risk analysis study.</em>

The Kingdom of Thailand has been facing with natural disasters every year: landslide, drought, wind storm, landslide etc. especially, the last decade the natural disaster was most frequency and devastated vast areas. Furthermore, landslide occurrences have become more and more recurrence and human impacts have been increasing on seriously natural disasters problem during the past couple of decades. The study has been designed to analyze the risk landslide areas for landslide management in Phetchabun province, Thailand. This study aim to apply the geo-informatics technology, create landslide risk map, and develop landslide monitoring and warning systems used for formulating preparedness and recovery plans. This analyzed the concerned physical and environmental factors though statistical techniques and spatial analysis. The analyzed factors included with river, elevation, street, land use, sub-basin area, slope, drainage and rainfall. Potential Surface Analysis (PSA) technique has been used for analysis included with overlaying and Weighting-Rating Model for landslide risk area. The validation model compared with historical data. The result could show risk areas of landslide in Phetchabun province that high risk areas are covering north-eastern and central of province. In addition, we divided risk area as three levels; high risky, moderate and less. Furthermore, the consequences can be protect or relieved by using appropriate measures; including both publicizing risk information and be prepared for the happening of such disasters. However, some of spatial data have to up to date and improve to high accuracy.

ABSTRACTA landslide quantitative risk analysis is applied the municipality of Santa Marta de Penaguião (N of Portugal) to evaluate the risk to which the buildings are exposed, using a vector data model in GIS.Two landslide subgroups were considered: landslide subgroup 1 (event inventory of landslides occurred on January 200)1; and landslide subgroup 2 (inventoried landslides occurred after the 2001 event until 2010). Seven landslide predisposing factors were weighted and integrated using the Information Value Method. The landslide susceptibility model was independently validated and the model performance was expressed by ROC curves.The probability of landslide size was estimated using a probability density function and the landslide hazard scenario was defined using the same landslide rainfall-triggering event.A vulnerability curve was constructed for each type of building considering its structural properties and the proxy of landslide magnitude. The economic value assigned for each building represents an approximated cadastral value.The landslide risk was computed for each building in vector format based on a rainfall triggering scenario and two landslide magnitudes.The probability of occurrence of small landslides is two orders of magnitude higher than the probability of occurrence for large landslides, which explains the higher risk generated by small landslides, despite of registering.