Application of a GIS-aided method for the assessment of volcaniclastic soil sliding susceptibility to sample areas of Campania (Southern Italy)

A significant part of Campania is extensively covered by volcaniclastic soils, deriving from the alteration of airfall-sedimented formations of layered ashes and pumices that were ejected by Campi Flegrei and Mt. Somma–Vesuvius during explosive eruptions. Where such soils cover steep slopes cut in carbonate bedrock, landforms depend essentially on the morpho-evolution of such slopes prior to the deposition of the volcaniclastic soils, because these are generally present only as thin veneers, up to a few meters of total thickness. Historical records and local literature testify that, in this part of Campania, landslides that originate on carbonate slopes covered by such soils and terminate at their foot or at gully outlets are frequent, following critical rainfall events. Such landslides can be classified as complex, occurring initially as debris slides, but rapidly evolving into debris avalanches and/or debris flows. The localization of the initial sliding areas (i.e. “sources”) on the slopes depends on both the spatial distribution of characters of the soil cover and the spatial distribution of the triggering rainfall events. It therefore appears reasonable to separate the two aspects of the problem and focus on the former one, in order to attempt an assessment of soil sliding susceptibility in the event of landslide-triggering rainfall. In this paper, some results of the application of a method aimed at such an assessment are presented. The method, called SLIDE (from SLiding Initiation areas DEtection), is based on the concept that, for a spatially homogeneous soil cover and a spatially homogeneous landslide-triggering rainfall sequence, different values of threshold slope gradient for limit equilibrium conditions exist, depending on morphological characters of the soil cover, such as its continuity and planform curvature. The method is based on the assessment of (1) soil cover presence, (2) discontinuities within soil cover, (3) slope gradients and curvature, by means of good resolution DEMs. It has been applied to sample carbonate slopes of Campania, where landslides originated either repeatedly or recently. Results are encouraging, and a soil sliding susceptibility map of a large area, based on a simplified version of method, is also presented.     

Volume estimate of flow-type landslides along carbonatic and volcanic slopes in Campania (Southern Italy)

Recent studies on flow-type landslides in pyroclastic deposits have been performed to identify potential source areas and the main depositional mechanisms. Interesting methods for mapping landslide susceptibility have also been proposed. Since the potential volume of flow-type landslides is a measure of event magnitude, hence of considerable use in hazard assessment, we propose a method to estimate the potential volume for the morphometric analysis of 213 flow-like landslides occurred in Campania in recent centuries. First, our data show that the height, H, of the detachment and erosion-transport zones (i.e. the difference in height between the top of source area and a point, the first break at the foot of the slope, where the deposition stars to take place and the landslide loses velocity) and the area, A _f, of the same zones are linked by a mathematical function. Secondly, only part of the entire thickness of the pyroclastic material on the slope is involved. To define the potential volumes of the flow-type landslides, we analysed slopes, both in volcanic and carbonatic contexts, considering both channelled and unchannelled flow-type landslides. The most susceptible areas are identified by using a landslide-triggering susceptibility map, and then in each case the height H was estimated. This height is the difference in level between the point on the slope with highest susceptibility and the first break at the foot of the slope. Using the statistical correlation between H and A _f, both calculated for historical landslides, we evaluate the area of a potential landslide on a slope. Finally, potential volumes are calculated by using A _f and a constant thickness of the pyroclastic cover for the whole slope. This method could represent a useful tool to detect the main areas where risk mitigation works are required.     

Slope units-based flow susceptibility model: using validation tests to select controlling factors

A susceptibility map for an area, which is representative in terms of both geologic setting and slope instability phenomena of large sectors of the Sicilian Apennines, was produced using slope units and a multiparametric univariate model. The study area, extending for approximately 90 km², was partitioned into 774 slope units, whose expected landslide occurrence was estimated by averaging seven susceptibility values, determined for the selected controlling factors: lithology, mean slope gradient, stream power index at the foot, mean topographic wetness index and profile curvature, slope unit length, and altitude range. Each of the recognized 490 landslides was represented by its centroid point. On the basis of conditional analysis, the susceptibility function here adopted is the density of landslides, computed for each class. Univariate susceptibility models were prepared for each of the controlling factors, and their predictive performance was estimated by prediction rate curves and effectiveness ratio applied to the susceptibility classes. This procedure allowed us to discriminate between effective and non-effective factors, so that only the former was subsequently combined in a multiparametric model, which was used to produce the final susceptibility map. The validation of this map latter enabled us to verify the reliability and predictive performance of the model. Slope unit altitude range and length, lithology and, subordinately, stream power index at the foot of the slope unit demonstrated to be the main controlling factors of landslides, while mean slope gradient, profile curvature, and topographic wetness index gave unsatisfactory results.     

Clay and Marl Formation Susceptibility in Mila Province,Algeria

In Mila Province, Algeria (a.k.a Mila Wilaya), sensitive clays presenting shrinkage-swelling phenomena are widespread, covering large areas. Due to this, constructions and infrastructures sustainability built on such soils is questionable. Such clays sensitivity is amplified by seasonal rainfall deficits which reaches extreme magnitudes. In order to develop disasters’ prevention measures, we present in this work, areas mapping susceptible to shrinkage-swelling phenomenon by adopting a French method developed by B R G M. This method takes into account three measurable characteristics: i.e. lithological, mineralogical and geotechnical, of clayey formations identified in the studied sector. Based on these characteristics, three scores are attributed for each formation. Then, the susceptibility index is the obtained score average for each clayey formation. The susceptibility map is established on this index basis with three classes: low, medium, high.     

A comparison of the predictive potential of various vegetation indices for leaf chlorophyll content

Leaf chlorophyll is an important indicator of nutritional stress, photosynthetic capacity, and growth status of plants. Therefore, it is important to accurately estimate leaf chlorophyll content over a range of spatial and temporal scales. However, traditional methods for chlorophyll measurement mainly rely on chemical analysis, which is not universally applicable to dynamic monitoring of plants in large areas because it is time consuming and requires destructive measurements. Hyperspectral remote sensing has enormous potential for accurate retrieval of plant biochemical parameters. Therefore, it has become the most popular means to retrieve chlorophyll content, by establishing empirical relationships between different vegetation indices and chlorophyll content. In recent years, many vegetation indices have been developed for the inversion of chlorophyll content. Only the vegetation indices that are less affected by the external environment are used, in order to establish a strong and robust model. Therefore, it is very important to assess the anti-disturbance ability of different vegetation indices. In this paper, a new method is proposed to quantitatively assess the anti-disturbance ability of vegetation indices. The anti-disturbance ability of several vegetation indices that are commonly used for chlorophyll estimation was evaluated using this method. We concluded that the anti-disturbance ability of VogD, TCARI/OSAVI, and Datt99 is stronger than the other vegetation indices tested. However, if a vegetation index is not sensitive to chlorophyll content, predicting chlorophyll content holds no value even though it has a strong anti-disturbance ability. Therefore, we used the slope of the best fitting function between the vegetation index and chlorophyll content (defined as d(VI)/d(Chlorophyll content)) to measure the sensitivities of different indices to chlorophyll content. Finally, we found that TCARI/OSAVI was one of the best vegetation indices to estimate chlorophyll content at the leaf level. However, we have only considered three factors to evaluate the anti-disturbance ability of different vegetation indices, which is still far from enough because the canopy reflectance is affected by many factors. Therefore, we should account for more factors in the future.     

A liquefaction severity index suggested for engineering practice

One of the major tasks in earthquake geotechnical engineering is the preparation of liquefaction susceptibility maps. For this purpose, some indices associated with liquefaction susceptibility are used. However, the currently used indices have some limitations. In this study, a liquefaction severity index, which can be used in preparation of susceptibility maps, is suggested. The suggested severity index is applied to two sites selected from Taiwan (Yuanlin) and Turkey (Inegol) to examine performance. The application indicated that the suggested index shows a good performance for the selected sites, and can be considered as a tool for further studies.     

Probabilistic landslide hazard assessment using homogeneous susceptible units (HSU) along a national highway corridor in the northern Himalayas, India

The increased socio-economic significance of landslides has resulted in the application of statistical methods to assess their hazard, particularly at medium scales. These models evaluate where, when and what size landslides are expected. The method presented in this study evaluates the landslide hazard on the basis of homogenous susceptible units (HSU). HSU are derived from a landslide susceptibility map that is a combination of landslide occurrences and geo-environmental factors, using an automated segmentation procedure. To divide the landslide susceptibility map into HSU, we apply a region-growing segmentation algorithm that results in segments with statistically independent spatial probability values. Independence is tested using Moran’s I and a weighted variance method. For each HSU, we obtain the landslide frequency from the multi-temporal data. Temporal and size probabilities are calculated using a Poisson model and an inverse-gamma model, respectively. The methodology is tested in a landslide-prone national highway corridor in the northern Himalayas, India. Our study demonstrates that HSU can replace the commonly used terrain mapping units for combining three probabilities for landslide hazard assessment. A quantitative estimate of landslide hazard is obtained as a joint probability of landslide size, of landslide temporal occurrence for each HSU for different time periods and for different sizes.     

Landslide occurrence and its relation with terrain factors in the Siwalik Hills,Nepal: case study of susceptibility assessment in three basins

Landslides in the three studied basins of the Siwalik Hills are not random in distribution; they tend to cluster in certain areas implying the control of certain in situ factors or their combination. Landslide controlling in situ factors were reviewed and analyzed from maps, aerial photos and imageries using GIS. Chi square analysis was carried out to test the significance of landslide distribution vis-à-vis in situ factors. Slope gradient and relative relief were consistently significant in landslide distribution. Geology, dip-topography relation, land use and land cover, and vegetation conditions appeared important in landslide occurrence in all three basins either in terms of area or count in any two basins. Slope aspect and altitude tested significant for landslide occurrence in at least two basins. However, upslope flow contributing area, drainage density and distance to lineament were found insignificant in all three basins. In situ factors that tested significant in any two basins were used for landslide susceptibility analysis using a bivariate statistical approach. The distribution of landslides strongly correlates with susceptibility indices. With in situ factors, landslide susceptibility had good correlation with slope gradient and relative relief. Incorporating calculated factor weight values from one basin to the other two basins, proxy susceptibility index maps were also prepared. A moderate to good positive correlation appeared between them implying certain range of confidence for replicating the result for whole of the Siwalik Hills. Slope gradient and relative relief can be used as proxy indicators of landslide susceptible areas in the Siwalik Hills.     

Mapping landslide susceptibility with frequency ratio,statistical index,and weights of evidence models: a case study in northern Iran

The main objective of this study is to investigate potential application of frequency ratio (FR), weights of evidence (WoE), and statistical index (SI) models for landslide susceptibility mapping in a part of Mazandaran Province, Iran. First, a landslide inventory map was constructed from various sources. The landslide inventory map was then randomly divided in a ratio of 70/30 for training and validation of the models, respectively. Second, 13 landslide conditioning factors including slope degree, slope aspect, altitude, plan curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, lithology, distance from streams, faults, roads, and land use type were prepared, and the relationships between these factors and the landslide inventory map were extracted by using the mentioned models. Subsequently, the multi-class weighted factors were used to generate landslide susceptibility maps. Finally, the susceptibility maps were verified and compared using several methods including receiver operating characteristic curve with the areas under the curve (AUC), landslide density, and spatially agreed area analyses. The success rate curve showed that the AUC for FR, WoE, and SI models was 81.51, 79.43, and 81.27, respectively. The prediction rate curve demonstrated that the AUC achieved by the three models was 80.44, 77.94, and 79.55, respectively. Although the sensitivity analysis using the FR model revealed that the modeling process was sensitive to input factors, the accuracy results suggest that the three models used in this study can be effective approaches for landslide susceptibility mapping in Mazandaran Province, and the resultant susceptibility maps are trustworthy for hazard mitigation strategies.     

Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (Northern Calabria, Italy)

This work summarizes the results of a geomorphological and bivariate statistical approach to gully erosion susceptibility mapping in the Turbolo stream catchment (northern Calabria, Italy). An inventory map of gully erosion landforms of the area has been obtained by detailed field survey and air photograph interpretation. Lithology, land use, slope, aspect, plan curvature, stream power index, topographical wetness index and length-slope factor were assumed as gully erosion predisposing factors. In order to estimate and validate gully erosion susceptibility, the mapped gully areas were divided in two groups using a random partitions strategy. One group (training set) was used to prepare the susceptibility map, using a bivariate statistical analysis (Information Value method) in GIS environment, while the second group (validation set) to validate the susceptibility map, using the success and prediction rate curves. The validation results showed satisfactory agreement between the susceptibility map and the existing data on gully areas locations; therefore, over 88% of the gullies of the validation set are correctly classified falling in high and very high susceptibility areas. The susceptibility map, produced using a methodology that is easy to apply and to update, represents a useful tool for sustainable planning, conservation and protection of land from gully processes. Therefore, this methodology can be used to assess gully erosion susceptibility in other areas of Calabria, as well as in other regions, especially in the Mediterranean area, that have similar morphoclimatic features and sensitivity to concentrated erosion.     

黏质土隧道围岩分级指标的选取研究

以长序列黏质土围岩物理力学指标及埋深数据为基础,运用数理统计学原理和灰色关联度理论从定量的角度分析了黏质土围岩分级的指标选取。研究结果表明：选用力学指标的影响因素作为分级的基本指标是可行的;黏质土围岩分级预选物性指标可分为3组,根据综合分组分析和关联度分析,确定了各组内指标的综合相关系数,综合相关系数大小表明液性指数、天然密度及塑性指数的变化对黏聚力和内摩擦角参数有很大的影响,据此确定黏质土隧道围岩的分级指标为液性指数、天然密度和塑性指数。     

Combining landslide susceptibility maps obtained from frequency ratio,logistic regression,and artificial neural network models using ASTER images and GIS

Landslide-related factors were extracted from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, and integrated techniques were developed, applied, and verified for the analysis of landslide susceptibility in Boun, Korea, using a geographic information system (GIS). Digital elevation model (DEM), lineament, normalized difference vegetation index (NDVI), and land-cover factors were extracted from the ASTER images for analysis. Slope, aspect, and curvature were calculated from a DEM topographic database. Using the constructed spatial database, the relationships between the detected landslide locations and six related factors were identified and quantified using frequency ratio (FR), logistic regression (LR), and artificial neural network (ANN) models. These relationships were used as factor ratings in an overlay analysis to create landslide susceptibility indices and maps. Three landslide susceptibility maps were then combined and applied as new input factors in the FR, LR, and ANN models to make improved susceptibility maps. All of the susceptibility maps were verified by comparison with known landslide locations not used for training the models. The combined landslide susceptibility maps created using three landslide-related input factors showed improved accuracy (87.00% in FR, 88.21% in LR, and 86.51% in ANN models) compared to the individual landslide susceptibility maps (84.34% in FR, 85.40% in LR, and 74.29% in ANN models) generated using the six factors from the ASTER images.     

Local- and global-scale seismic analyses of historical masonry compounds in San Pio delle Camere (L’Aquila,Italy)

Masonry building aggregates are large parts of the Italian building heritage often designed without respecting seismic criteria. The current seismic Italian code does not foresee a clear calculation method to predict their static nonlinear behaviour. For this reason, in this paper firstly, a simple methodology to forecast the seismic response of masonry aggregates in San Pio delle Camere (L’Aquila, Italy) has been set up starting from the provisions of the Italian Guidelines on Cultural Heritage. The implemented procedure has been calibrated on the results of two FEM structural analysis programs used to investigate three masonry building compounds. As a result, a design chart used to correctly predict the base shear of aggregate masonry units starting from code provisions has been set up. Later on, the large-scale seismic vulnerability and damage appraisal of the inspected historical centre has been done on the basis of a quick methodology, already implemented and experienced by the author in some historical centres of the Campania region. The analysis result was a numerical correlation between vulnerability index and mean damage grade of examined building compounds. In particular, a damage forecast under numerical way has been firstly estimated and then compared with the real one. The post-earthquake scenario has represented an ideal term of comparison for effectively testing the reliability of the employed technique, which should be further extended to other Italian historical centres.

     

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

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

Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling�CNarayanghat road section in Nepal Himalaya

Landslide susceptibility maps are vital for disaster management and for planning development activities in the mountainous country like Nepal. In the present study, landslide susceptibility assessment of Mugling?CNarayanghat road and its surrounding area is made using bivariate (certainty factor and index of entropy) and multivariate (logistic regression) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field survey. As a result, 321 landslides were mapped and out of which 241 (75?%) were randomly selected for building landslide susceptibility models, while the remaining 80 (25?%) were used for validating the models. The effectiveness of landslide susceptibility assessment using GIS and statistics is based on appropriate selection of the factors which play a dominant role in slope stability. In this case study, the following landslide conditioning factors were evaluated: slope gradient; slope aspect; altitude; plan curvature; lithology; land use; distance from faults, rivers and roads; topographic wetness index; stream power index; and sediment transport index. These factors were prepared from topographic map, drainage map, road map, and the geological map. Finally, the validation of landslide susceptibility map was carried out using receiver operating characteristic (ROC) curves. The ROC plot estimation results showed that the susceptibility map using index of entropy model with AUC value of 0.9016 has highest prediction accuracy of 90.16?%. Similarly, the susceptibility maps produced using logistic regression model and certainty factor model showed 86.29 and 83.57?% of prediction accuracy, respectively. Furthermore, the ROC plot showed that the success rate of all the three models performed more than 80?% accuracy (i.e. 89.15?% for IOE model, 89.10?% for LR model and 87.21?% for CF model). Hence, it is concluded that all the models employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of Mugling?CNarayanghat road section. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.     

基于突变理论的滑坡危险性评价

滑坡危险性评价与预测是滑坡灾害防治中的首要任务,科学合理地评价滑坡危险性十分重要。以岩桑树水电站库区发育的潜在滑坡为例,据其特有的地质环境条件,选取坡体风化程度、斜坡坡度等9个影响因素作为滑坡危险性评价的指标,并建立分级标准将滑坡危险性分为轻度危险、中度危险、重度危险和极度危险4个等级。将突变理论运用到滑坡危险性评价中,从而建立了新的稳定性评判模型。基于突变级数法的滑坡危险性评价方法,综合考虑了各评价指标间的相关性,真实地描绘了滑坡系统的内在机制。实例分析结果表明,该方法评判结果准确率高,可为滑坡的防治提供依据。     

Individual and societal risk owing to landslides in the Campania region (southern Italy)

This paper deals with the estimation of both individual and societal risks owing to landslides in the Campania region (southern Italy) thanks to the availability of an extensive catalogue of historical incident data spanning from the 5^th century up to now. Individual risk is estimated by computing the landslide mortality rate. Societal risk is measured by plotting the annual frequency F of events causing N or more fatalities against the number N of fatalities (i.e. an F–N curve). The results obtained show that in Campania both individual and societal risks owing to landslides are very high when compared to similar risks of the Italian territory. Moreover, the analysis of the incident data clearly highlights the most prone areas to catastrophic events, essentially related to the occurrence of flow-like fast-moving phenomena, where the societal risk is proved to be one of the highest in Europe.     

Determination of partial factors for the verification of the bearing capacity of shallow foundations under open channels

The limit state design method has been introduced into the design criteria for geotechnical structures. The current paper attempts to apply the reliability-based design method, at Level II, to the bearing capacity of the foundations of open channels from the viewpoint of the limit state design. To examine the applicability of the proposed procedure for practical structures, the reliability index is computed for evaluating the stability of the foundations of existing open channels designed by the conventional method. The conventional design procedure makes excessively safe side design. We applied the FORM to the existing open channels designed by the conventional design procedure, and consequently, large values of reliability index, 3 and 5 were obtained for clayey and sandy soils, respectively. Finally, the partial factors for the soil parameters have been determined, corresponding to the target reliability indices β _t =1, 3 and 4.     

Identification of the best spectral indices to remotely trace the diurnal course of water use efficiency of <Emphasis Type="Italic">Tamarix ramosissima</Emphasis> in the Gurbantunggut Desert,China

Water availability is one of the most important factors limiting photosynthetic assimilation of carbon dioxide and growth of individual plants in terrestrial ecosystems. Water use efficiency (WUE) of plants has been widely assessed using ecological methods in field measurements; however, approaches for remotely sensing WUE are still lacking, particularly in arid ecosystems. In this study, a comprehensive analysis of diurnal WUE via spectral indices in arid ecosystems was assessed. Analyses were conducted on a native dominant desert shrub, Tamarix ramosissima, in its original habitat on the southern periphery of the Gurbantunggut Desert, China. Based on diurnal measurements of spectral reflectance, photosynthesis, and micrometeorological variables, simple and useful spectral indices for estimating diurnal WUE at the assimilative organ scale were explored. From six types of spectral indices, ranging from simple to sophisticated, the best wavelength domains for a given type of index were determined by screening all combinations using correlation analysis. The coefficient of determination (R ²), ranging from 0.19 to 0.60, for WUE was calculated for all indices derived from spectra taken from the assimilative organs. With only two wavelengths and a significant correlation coefficient (R ² = 0.60, P < 0.001), the simple ratio (SR) type index was the most sensitive to WUE among all of the indices. Furthermore, SR is a useful indicator to determine the dynamic and diurnal processes of photosynthesis and transpiration of T. ramosissima. Although it has a few weaknesses, SR serves as a simple and robust indicator of WUE in arid ecosystems.     

Applying different scenarios for landslide spatial modeling using computational intelligence methods

Landslides every year impose extensive damages to human beings in various parts of the world; therefore, identifying prone areas to landslides for preventive measures is essential. The main purpose of this research is applying different scenarios for landslide susceptibility mapping by means of combination of bivariate statistical (frequency ratio) and computational intelligence methods (random forest and support vector machine) in landslide polygon and point formats. For this purpose, in the first step, a total of 294 landslide locations were determined from various sources such as aerial photographs, satellite images, and field surveys. Landslide inventory was randomly split into a testing dataset 70% (206 landslide locations) for training the different scenarios, and the remaining 30% (88 landslides locations) was used for validation purposes. To providing landslide susceptibility maps, 13 conditioning factors including altitude, slope angle, plan curvature, slope aspect, topographic wetness index, lithology, land use/land cover, distance from rivers, drainage density, distance from fault, distance from roads, convergence index, and annual rainfall are used. Tolerance and the variance inflation factor indices were used for considering multi-collinearity of conditioning factors. Results indicated that the smallest tolerance and highest variance inflation factor were 0.31 and 3.20, respectively. Subsequently, spatial relationship between classes of each landslide conditioning factor and landslides was obtained by frequency ratio (FR) model. Also, importance of the mentioned factors was obtained by random forest (RF) as a machine learning technique. The results showed that according to mean decrease accuracy, factors of altitude, aspect, drainage density, and distance from rivers had the greatest effect on the occurrence of landslide in the study area. Finally, the landslide susceptibility maps were produced by ten scenarios according to different ensembles. The receiver operating characteristics, including the area under the curve (AUC), were used to assess the accuracy of the models. Results of validation of scenarios showed that AUC was varying from 0.668 to 0.749. Also, FR and seed cell area index indicators show a high correlation between the susceptibility classes with the landslide pixels and field observations in all scenarios except scenarios 10_RF and 10_SVM. The results of this study can be used for landslides management and mitigation and development activities such as construction of settlements and infrastructure in the future.