scholarly journals Modeling Shallow Landslide Susceptibility and Assessment of the Relative Importance of Predisposing Factors, through a GIS-Based Statistical Analysis

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 333
Author(s):  
Massimo Conforti ◽  
Fabio Ietto
Keyword(s):  
Landslide Susceptibility ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Predisposing Factors ◽  
Susceptibility Map ◽  
Slope Gradient ◽  
Landslide Susceptibility Map ◽  
Jackknife Test ◽  
Susceptibility Model ◽  
Very High

Shallow landslides are destructive hazards and play an important role in landscape processes. The purpose of this paper is to evaluate the shallow landslide susceptibility and to investigate which predisposing factors control the spatial distribution of the collected instability phenomena. The GIS-based logistic regression model and jackknife test were respectively employed to achieve the scopes. The studied area falls in the Mesima basin, located in the southern Calabria (Italy). The research was based mainly on geomorphological study using both interpretation of Google Earth images and field surveys. Thus, 1511 shallow landslides were mapped and 18 predisposing factors (lithology, distance to faults, fault density, land use, soil texture, soil bulk density, soil erodibility, distance to streams, drainage density, elevation, slope gradient, slope aspect, local relief, plan curvature, profile curvature, TPI, TWI, and SPI) were recognized as influencing the shallow landslide susceptibility. The 70% of the collected shallow landslides were randomly divided into a training data set to build susceptibility model and the remaining 30% were used to validate the newly built model. The logistic regression model calculated the landslide probability of each pixel in the study area and produced the susceptibility map. Four classification methods were tested and compared between them, so the most reliable classification system was employed to the shallow landslide susceptibility map construction. In the susceptibility map, five classes were recognized as following: very low, low, moderate, high, and very high susceptibility. About 26.1% of the study area falls in high and very high susceptible classes and most of the landslides mapped (82.4%) occur in these classes. The accuracy of the predictive model was evaluated by using the ROC (receiver operating characteristics) curve approach, which showed an area under the curve (AUC) of 0.93, proving the excellent forecasting ability of the susceptibility model. The predisposing factors importance evaluation, using the jackknife test, revealed that slope gradient, TWI, soil texture and lithology were the most important factors; whereas, SPI, fault density and profile curvature have a least importance. According to these results, we conclude that the shallow landslide susceptibility map can be use as valuable tool both for land-use planning and for management and mitigation of the shallow landslide risk in the study area.

scholarly journals Landslide Susceptibility Mapping Based on the Combination of Bivariate Statistics and Modified Analytic Hierarchy Process Methods: A Case Study of Tinh Tuc Town, Nguyen Binh District, Cao Bang Province, Vietnam

2021 ◽  
Vol 16 (4) ◽  
pp. 521-528
Author(s):  
Nguyen Trung Kien ◽  
The Viet Tran ◽  
Vy Thi Hong Lien ◽  
Pham Le Hoang Linh ◽  
Nguyen Quoc Thanh ◽  
...  
Keyword(s):  
Land Use ◽  
Landslide Susceptibility ◽  
Water Storage ◽  
Landslide Inventory ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Map ◽  
Landslide Susceptibility Map ◽  
The Road ◽  
Hierarchy Process ◽  
Very High

Tinh Tuc town, Cao Bang province, Vietnam is prone to landslides due to the complexity of its climatic, geological, and geomorphological conditions. In this study, in order to produce a landslide susceptibility map, the modified analytical hierarchy process and landslide susceptibility analysis methods were used together with the layers, including: landslide inventory, slope, weathering crust, water storage, geology, land use, and distance from the road. In the study area, 98% of landslides occurred in highly or completely weathered units. Geology, land use, and water storage data layers were found to be important factors that are closely related with the occurrence of landslides. Although the weight of the “distance from the road” factor has a low value, the weight of layer “<100 m” has a high value. Therefore, the landslide susceptibility index very high is concentrated along the roads. For the validation of the predicted result, the landslide susceptibility map was compared with the landslide inventory map containing 47 landslides. The outcome shows that about 90% of these landslides fall into very high susceptibility zones.

Application of earth observation datasets and Analytic Hierarchy Process in the mapping of Landslide hazard zones of Manipur, India

2021 ◽  
Author(s):  
Digvijay Singh ◽  
Arnab Laha
Keyword(s):  
Analytic Hierarchy Process ◽  
Landslide Susceptibility ◽  
Landslide Hazard ◽  
Susceptibility Map ◽  
Hazard Map ◽  
Landslide Susceptibility Map ◽  
Analytic Hierarchy ◽  
High Hazard ◽  
Hierarchy Process ◽  
Very High

&lt;p&gt;Landslides problems are one of the major natural hazards in the mountainous region. Every year due to the increase in anthropogenic factors and changing climate, the problem of landslides is increasing, which leads to huge loss of property and life. Landslide is a common and regular phenomenon in most of the northeastern states of India. &amp;#160;However, in recent past years, Manipur has experienced several landslides including mudslides during the rainy season. Manipur is a geologically young and geodynamically active area with many streams flowing parallel to fault lines. As a first step toward hazard management, a landslide susceptibility map is the prime necessity of the region. In this study, we have prepared a landslide hazard map of the state using freely available earth observations datasets and multi-criteria decision making technique, i.e., Analytic Hierarchy Process (AHP). For this purpose, lithology, rainfall, slope, aspect, relative relief, Topographic Wetness Index, and distance from road, river and fault were used as the parameters in AHP based on the understanding of their influence towards landslide in that region. The hazard map is classified into four hazard zones: Very High, High, Moderate, and Low. About 40% of the state falls under very high and high hazard zone, and the hilly regions such as Senapati and Chandel district are more susceptible to the landslide. Among the factors, slope and rainfall have a more significant contribution towards landslide hazard. It is also observed that areas nearer to NH-39 that lies in the fault zones i.e., Mao is also susceptible to high hazard. The landslide susceptibility map gives an first-hand impression for future land use planning and hazard mitigation purpose.&lt;/p&gt;

Landslide variation with morphometric factors using the GIS techniques: The case of Shaqlawa Forest

2021 ◽  
Vol 28 (3) ◽  
pp. 117-128
Author(s):  
Sara Zaki ◽  
Jehan Suleimany
Keyword(s):  
Landslide Susceptibility ◽  
Geographical Information ◽  
Susceptibility Map ◽  
Kurdistan Region ◽  
Ahp Method ◽  
Landslide Susceptibility Map ◽  
Landslide Susceptibility Index ◽  
Map Data ◽  
Very High ◽  
Landslide Zone

This study deals with the application of geographical information system (GIS) datasets and methods to assess the landslide susceptibility in Wadi Hujran. The area has a rocky terrain and belongs to the Shaqlawa district of the Kurdistan Region of Iraq. The region is placed towards the Northeast side of Erbil city. The region covers an area of 18.56 Km2 (1856.1 ha) and consists of rough broken and stones. The watershed area is surrounded by North latitudes 36° 21' 53.514" to 36° 17' 49.7796" and East longitudes 44° 17' 5.658" to 44° 20' 9.06". Three factors, namely the morphometric, geological, and environmental, were used to prepare the landslide susceptibility index. The study made use of AHP method and prepared a landslide susceptibility map. Data related to geology, topography, hydrology, rainfall, and land use were used to prepare the map. Physical and statistical methods were used to validate the map. A heuristic approach was incorporated to produce the final susceptibility map. ArcGIS software was used to generate the landslide zones. A total of five landslide zones were generated, which varied from very low landslide zones (80.5) to very high landslide zone (84.5). The zones also included low landslide zone (1262.2), moderate landslide zone (1505.9), and high landslide zone (566.8), and the ratio of consistency in the present study was 0.06 AHP less than 1, and all the five zones in the study were compiled landslide zonation estimated.

A combined procedure to assess rainfall-induced shallow landslide detachment, transit and runout susceptibility using Machine Learning and GIS techniques

2021 ◽  
Author(s):  
Mariano Di Napoli ◽  
Diego Di Martire ◽  
Domenico Calcaterra ◽  
Marco Firpo ◽  
Giacomo Pepe ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Predisposing Factors ◽  
Mitigation Measures ◽  
Economic Losses ◽  
Meaningful Improvement ◽  
Susceptibility Evaluation ◽  
Landslide Runout

&lt;p&gt;Rainfall-induced landslides are notoriously dangerous phenomena which can cause a notable death toll as well as major economic losses globally. Usually, shallow landslides are triggered by prolonged or severe rainfalls and frequently may evolve into potentially catastrophic flow-like movements. Shallow failures are typical in hilly and mountainous areas due to the combination of several predisposing factors such as slope morphology, geological and structural setting, mechanical properties of soils, hydrological and hydrogeological conditions, land-use changes and wildfires. Because of the ability of these phenomena to travel long distances, buildings and infrastructures located in areas improperly deemed safe can be affected.&lt;/p&gt;&lt;p&gt;Spatial and temporal hazard posed by flow-like movements is due to both source characteristics (e.g., location and volume) and the successive runout dynamics (e.g., travelled paths and distances). Hence, the assessment of shallow landslide susceptibility has to take into account not only the recognition of the most probable landslide source areas, but also &amp;#160;landslide runout (i.e., travel distance). In recent years, a meaningful improvement in landslide detachment susceptibility evaluation has been gained through robust scientific advances, especially by using statistical approaches. Furthermore, various techniques are available for landslide runout susceptibility assessment in quantitative terms. The combination of landslide detachment and runout dynamics has been admitted by many researchers as a suitable and complete procedure for landslide susceptibility evaluation. However, despite its significance, runout assessment is not as widespread in literature as landslide detachment assessment and still remains a challenge for researchers. Currently, only a few studies focus on the assement of both landslide detachment susceptibility (LDS) and landslide runout susceptibility (LRS).&lt;/p&gt;&lt;p&gt;In this study, the adoption of a combined approach allowed to estimate shallow landslide susceptibility to both detachment and potential runout. Such procedure is based on the integration between LDS assessment via Machine Learning techniques (applying the Ensemble approach) and LRS assessment through GIS-based tools (using the &amp;#8220;reach angle&amp;#8221; method). This methodology has been applied to the Cinque Terre National Park (Liguria, north-west Italy), where risk posed by flow-like movements is very high. Nine predisposing factors were chosen, while a database of about 300 rainfall-induced shallow landslides was used as input. In particular, the obtained map may be useful for urban and regional planning, as well as for decision-makers and stakeholders, to predict areas that may be affected by rainfall-induced shallow landslides&amp;#160; in the future and to identify areas where risk mitigation measures are needed.&lt;/p&gt;

scholarly journals Evaluating the impact of road construction on landslide susceptibility- A case study of Mandi district, Himachal Pradesh, India.

2021 ◽  
Author(s):  
Amol Sharma ◽  
Chander Prakash
Keyword(s):  
Statistical Models ◽  
Landslide Susceptibility ◽  
Road Construction ◽  
Himachal Pradesh ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Map ◽  
Landslide Susceptibility Map ◽  
The Impact ◽  
Very High

Landslide susceptibility mapping has proved to be crucial tool for effective disaster management and planning strategies in mountainous regions. The present study is perused to investigate the changes in the landslide susceptibility of the Mandi district of Himachal Pradesh due to road construction. For this purpose, an inventory of 1723 landslides was generated from various sources. Out of these, 1199 (70%) landslides were taken in the training dataset to be used for modelling and prediction purposes, while 524 (30%) landslides were taken in the testing dataset to be used for validation purposes. Eleven landslide causative factors were selected from numerous hydrological, geological and topographical factors and were analyzed for landslide susceptibility mapping using three bivariate statistical models, namely; Frequency Ratio (FR), Certainty Factor (CF) and Shanon Entropy (SE). Two sets of LSM maps i.e. landslide susceptibility map natural (LSMN) and landslide susceptibility map road (LSMR), were generated using the above mentioned bivariate models and were divided into five landslide susceptibility classes namely; very low, low, medium, high and very high. These maps were analyzed for accuracy of prediction and validation using receiver operating characteristic (ROC) curves and area under curve (AUC) technique which indicated that all three bivariate statistical models performed satisfactorily with the SE model had the highest prediction and validation accuracy of 83-86%. Further analysis LSM maps confirmed that the percentage area in high and very high classes of land-slide susceptibility increased by 2.67-4.17% due to road construction activities in the study area.

scholarly journals Developing a Landslide Susceptibility Map Using the Analytic Hierarchical Process in Ta Van and Hau Thao Communes, Sapa, Vietnam

2021 ◽  
Vol 16 (4) ◽  
pp. 529-538
Author(s):  
Thi Thanh Thuy Le ◽  
The Viet Tran ◽  
Viet Hung Hoang ◽  
Van Truong Bui ◽  
Thi Kien Trinh Bui ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
High Susceptibility ◽  
Landslide Susceptibility Map ◽  
Landslide Occurrence ◽  
Mountainous Regions ◽  
Geological Conditions ◽  
Very High

Landslides are considered one of the most serious problems in the mountainous regions of the northern part of Vietnam due to the special topographic and geological conditions associated with the occurrence of tropical storms, steep slopes on hillsides, and human activities. This study initially identified areas susceptible to landslides in Ta Van Commune, Sapa District, Lao Cai Region using Analytical Hierarchy Analysis. Ten triggering and conditioning parameters were analyzed: elevation, slope, aspect, lithology, valley depth, relief amplitude, distance to roads, distance to faults, land use, and precipitation. The consistency index (CI) was 0.0995, indicating that no inconsistency in the decision-making process was detected during computation. The consistency ratio (CR) was computed for all factors and their classes were less than 0.1. The landslide susceptibility index (LSI) was computed and reclassified into five categories: very low, low, moderate, high, and very high. Approximately 9.9% of the whole area would be prone to landslide occurrence when the LSI value indicated at very high and high landslide susceptibility. The area under curve (AUC) of 0.75 illustrated that the used model provided good results for landslide susceptibility mapping in the study area. The results revealed that the predicted susceptibility levels were in good agreement with past landslides. The output also illustrated a gradual decrease in the density of landslide from the very high to the very low susceptible regions, which showed a considerable separation in the density values. Among the five classes, the highest landslide density of 0.01274 belonged to the very high susceptibility zone, followed by 0.00272 for the high susceptibility zone. The landslide susceptibility map presented in this paper would help local authorities adequately plan their landslide management process, especially in the very high and high susceptible zones.

scholarly journals Landslide Susceptibility Mapping Using the Slope Unit for Southeastern Helong City, Jilin Province, China: A Comparison of ANN and SVM

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1047 ◽  
Author(s):  
Chenglong Yu ◽  
Jianping Chen
Keyword(s):  
Influencing Factors ◽  
Landslide Susceptibility ◽  
Jilin Province ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Susceptibility Map ◽  
Landslide Susceptibility Map ◽  
Slope Unit ◽  
Svm Model ◽  
Very High

The purpose of this study is to produce a landslide susceptibility map of Southeastern Helong City, Jilin Province, Northeastern China. According to the geological hazard survey (1:50,000) project of Helong city, a total of 83 landslides were mapped in the study area. The slope unit, which is classified based on the curvature watershed method, is selected as the mapping unit. Based on field investigations and previous studies, three groups of influencing Factors—Lithological factors, topographic factors, and geological environment factors (including ten influencing factors)—are selected as the influencing factors. Artificial neural networks (ANN’s) and support vector machines (SVM’s) are introduced to build the landslide susceptibility model. Five-fold cross-validation, the receiver operating characteristic curve, and statistical parameters are used to optimize model. The results show that the SVM model is the optimal model. The landslide susceptibility maps produced using the SVM model are classified into five grades—very high, high, moderate, low, and very low—and the areas of the five grades were 127.43, 151.60, 198.77, 491.19, and 506.91 km2, respectively. The very high and high susceptibility areas included 79.52% of the total landslides, demonstrating that the landslide susceptibility map produced in this paper is reasonable. Consequently, this study can serve as a guide for landslide prevention and for future land planning in the southeast of Helong city.

scholarly journals Rainfall-Induced Shallow Landslide Detachment, Transit and Runout Susceptibility Mapping by Integrating Machine Learning Techniques and GIS-Based Approaches

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 488 ◽  
Author(s):  
Mariano Di Napoli ◽  
Diego Di Martire ◽  
Giuseppe Bausilio ◽  
Domenico Calcaterra ◽  
Pierluigi Confuorto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Geographical Information ◽  
Machine Learning Techniques ◽  
Mitigation Measures ◽  
Landslide Risk ◽  
Susceptibility Map ◽  
Learning Techniques

Rainfall-induced shallow landslides represent a serious threat in hilly and mountain areas around the world. The mountainous landscape of the Cinque Terre (eastern Liguria, Italy) is increasingly popular for both Italian and foreign tourists, most of which visit this outstanding terraced coastal landscape to enjoy a beach holiday and to practice hiking. However, this area is characterized by a high level of landslide hazard due to intense rainfalls that periodically affect its rugged and steep territory. One of the most severe events occurred on 25 October 2011, causing several fatalities and damage for millions of euros. To adequately address the issues related to shallow landslide risk, it is essential to develop landslide susceptibility models as reliable as possible. Regrettably, most of the current land-use and urban planning approaches only consider the susceptibility to landslide detachment, neglecting transit and runout processes. In this study, the adoption of a combined approach allowed to estimate shallow landslide susceptibility to both detachment and potential runout. At first, landslide triggering susceptibility was assessed using Machine Learning techniques and applying the Ensemble approach. Nine predisposing factors were chosen, while a database of about 300 rainfall-induced shallow landslides was used as input. Then, a Geographical Information System (GIS)-based procedure was applied to estimate the potential landslide runout using the “reach angle” method. Information from such analyses was combined to obtain a susceptibility map describing detachment, transit, and runout. The obtained susceptibility map will be helpful for land planning, as well as for decision makers and stakeholders, to predict areas where rainfall-induced shallow landslides are likely to occur in the future and to identify areas where hazard mitigation measures are needed.

scholarly journals Landslide Susceptibility Mapping using Statistical Methods in Uatzau Catchment Area, Northwestern Ethiopia

2020 ◽  
Author(s):  
Azemeraw Wubalem
Keyword(s):  
Landslide Susceptibility ◽  
Prediction Accuracy ◽  
Susceptibility Mapping ◽  
Information Value ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Map ◽  
Ratio Model ◽  
Certainty Factor ◽  
Landslide Susceptibility Map ◽  
Susceptibility Model

Abstract Landslide susceptibility mapping is important to hazard management and to have planning development activities in the mountainous country like Ethiopia. In the present study, the landslide susceptibility mapping of the Uatzau basin is made using certainty factor, information value and logistic regression methods. Preparation of landslide inventory map from detailed fieldwork and Google Earth image interpretation was the first activity. Thus, 514 landslides were mapped and out of which 490 (70%) of landslides were randomly selected keeping their spatial distribution to build landslide susceptibility models while the remaining 155 (30%) of the landslides were used to models validation. It is clear that the effectiveness of the landslide susceptibility model using GIS and statistical methods is depending on the selection of the causative factors, which have a prevailing effect on landslide occurrence. In this study, six factors including lithology, land use/cover, distance to stream, slope gradient, slope aspect, and slope curvature were the landslide factors that were evaluated. After preparation of these factor maps, the effects of them on slope instability was determined by comparing with landslide inventory raster map using GIS. Finally, the landslide susceptibility model for the Uatzau area was developed and validated using the receiver operating characteristics curve (ROC). The results of ROC showed that for landslide susceptibility map using frequency ratio model (FRM) with an AUC value of 0.8883 has the highest prediction accuracy of 88.83%. The landslide susceptibility map, which is produced using Certainty factor and information value methods also showed that 87.03% and 84.83% of prediction accuracy respectively. Besides the prediction accuracy of the model, the success rate curve for all models was applied and the result showed that more than 80% accuracy (i.e, 80.83% for the information value model, 87.19% for the certainty factor model and 83.27% for frequency ratio model). The present research finds out that all methods/ models, which have employed in this study showed that reasonably very good accuracy in predicting landslide susceptibility of the Uatzau area. Therefore, these landslide susceptibility maps can be used for regional land use planning and landslide hazard mitigation purposes.

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