scholarly journals Landslide susceptibility mapping of SE Serbia using GIS

2019 ◽  
Vol 80 (2) ◽  
pp. 105-116
Author(s):  
Sonja Djokanovic
Keyword(s):  
Landslide Susceptibility ◽  
Slope Angle ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Map ◽  
Susceptibility Assessment ◽  
High Susceptibility ◽  
Landslide Susceptibility Map ◽  
Analytic Hierarchy ◽  
Hierarchy Process ◽  
Very High

Landslides represent a great problem in Serbia. According to current estimates 30-35 % of Serbia is affected by landslides. In this paper a landslide susceptibility analysis is done for SE Serbia. Study area covers 1507 km2. Relief is hilly or mountainous and characterized by high altitude differences. Analysis is done by geographic information system (GIS) and evaluation by analytic hierarchy process (AHP). For susceptibility assessment are used four factors: lithology, slope angle, distance to rivers and distance to faults. The most landslides are formed on slope steepness less than 30?. There is four classes of susceptibility in study area. Zone of very high susceptibility make 63.9 % of the study area. Zone of high susceptibility covers 15.7 % of the study area. The moderate class occupies 37.4% and zone classified as having low susceptibility accounts for 10 % of study area. Final landslide susceptibility map of SE Serbia is satisfactory.

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;

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 Nationwide Susceptibility Mapping of Landslides in Kenya Using the Fuzzy Analytic Hierarchy Process Model

Land ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 535
Author(s):  
Suhua Zhou ◽  
Shuaikang Zhou ◽  
Xin Tan
Keyword(s):  
Analytic Hierarchy Process ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Fuzzy Analytic Hierarchy Process ◽  
Susceptibility Map ◽  
Land Resource ◽  
High Susceptibility ◽  
Landslide Susceptibility Map ◽  
Analytic Hierarchy ◽  
Hierarchy Process

Landslide susceptibility mapping (LSM) is a cost-effective tool for landslide hazard mitigation. To date, no nationwide landslide susceptibility maps have been produced for the entire Kenyan territory. Hence, this work aimed to develop a landslide susceptibility map at the national level in Kenya using the fuzzy analytic hierarchy process method. First, a hierarchical evaluation index system containing 10 landslide contributing factors and their subclasses was established to produce a susceptibility map. Then, the weights of these indexes were determined through pairwise comparisons, in which triangular fuzzy numbers (TFNs) were employed to scale the relative importance based on the opinions of experts. Ultimately, these weights were merged in a hierarchical order to obtain the final landslide susceptibility map. The entire Kenyan territory was divided into five susceptibility levels. Areas with very low susceptibility covered 5.53% of the Kenyan territory, areas with low susceptibility covered 20.58%, areas with the moderate susceptibility covered 29.29%, areas with high susceptibility covered 29.16%, and areas with extremely high susceptibility covered 15.44% of Kenya. The resulting map was validated using an inventory of 425 historical landslides in Kenya. The results indicated that the TFN-AHP model showed a significantly improved performance (AUC = 0.86) compared with the conventional AHP (AUC = 0.72) in LSM for the study area. In total, 31.53% and 29.88% of known landslides occurred within the “extremely high” and “high” susceptibility zones, respectively. Only 8.24% and 1.65% of known landslides fell within the “low” and “very low” susceptibility zones, respectively. The map obtained as a result of this study is beneficial to inform planning and land resource management in Kenya.

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 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 ANALISIS TINGKAT KERAWANAN TANAH LONGSOR MENGGUNAKAN METODE FREKUENSI RASIO DI DAERAH ALIRAN SUNGAI BIALO

PERENNIAL ◽  
2019 ◽  
Vol 15 (1) ◽  
pp. 42
Author(s):  
Nurul Fadilah ◽  
Usman Arsyad ◽  
Andang Suryana Soma
Keyword(s):  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
High Accuracy ◽  
The Other ◽  
Susceptibility Map ◽  
High Susceptibility ◽  
Landslide Susceptibility Map ◽  
Causative Factors ◽  
Very High ◽  
Slope Direction

Landslide is a movement of soil with slope direction and moves it on a slide. This study aimed to predict the landslide susceptibility map by using a frequency ratio. It used seven causative factors, such as slope, curvature, land use, lithology, distance to a river, distance to lineament, and rainfall. The result showed the AUC of success rate and predicted rate produced high accuracy with 0.907 and 0.904, respectively.  According to the frequency ratio, the slope was the most influential than the other causative factors with 7.15. The landslide susceptibility divided into five classes, i.e. very low, low, moderate, high, and very high.  Landslide susceptibility with very high and high was 19%.  Moreover, classes susceptibility of very low, low, and moderate were 71%. The presentation of very high and high susceptibility is low, but it was located on an upper stream, and it will be a danger if to the downstream. 

scholarly journals Slope Stability Assessment and Landslide Susceptibility Map Production of Wadi Dhahr Area, Northwest of Sana’a, Yemen

2018 ◽  
Vol 23 (2) ◽  
pp. 120
Author(s):  
Barahim Adnan A. ◽  
Khanbari Khaled M. ◽  
Algodami Amal F. ◽  
Almadhaji Ziad A. ◽  
Adris Ahmed M.
Keyword(s):  
Slope Stability ◽  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Satellite Image ◽  
Slope Angle ◽  
Field Investigation ◽  
Susceptibility Map ◽  
Stability Assessment ◽  
Landslide Susceptibility Map ◽  
Modes Of Failure

A slope stability assessment of Wadi Dhahr area, located northwest of Sana’a the capital of Yemen, was carried out in this study. The study area consists of sandstone and volcanic rocks that are deformed by number of faults, joints and basaltic dykes. All the important factors affecting slope stability in the area such as slope angle, slope height, discontinuities measurements, weathering, vegetation cover, rainfall and previous landslides were evaluated. The study was conducted based on the integration of field investigation and satellite image processing. A landslide susceptibility map was produced with the Landslide Possibility Index (LP1) System, and the correlation values were computed between the factors measured and Landslide Possibility Index values. The fractures counted by satellite image were categorised according to their length and zones based on their concentrations. It was found that plain sliding and rockfall are the main modes of failure in the area, while rolling and toppling are rare. Some remedial measures are proposed to protect the slopes where it is needed,  such as the removal of rock overhangs, unstable blocks and trees, and by supporting the toe of slopes and overhanging parts by retaining walls and erecting well sealed drainage conduits. The results will assist in slope management and land use planning in the area.

scholarly journals Geospatial Technology for Landslide Susceptibility Mapping along the Vathalmalai Ghat road section, South India

2021 ◽  
Vol 30 (4) ◽  
pp. 683-691
Author(s):  
G. Kavitha ◽  
S. Anbazhagan ◽  
S. Mani
Keyword(s):  
Landslide Susceptibility ◽  
Drainage Density ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Landslide Susceptibility Zonation ◽  
Road Section ◽  
Gps Measurement ◽  
Causative Factors ◽  
Very High

Landslides are among the most prevalent and harmful hazards. Assessment of landslide susceptibility zonation is an important task in reducing the losses of lifeand properties. The present study aims to demarcate the landslide prone areas along the Vathalmalai Ghat road section (VGR) using remote sensing and GIS techniques. In the first step, the landslide causative factors such as geology, geomorphology, slope, slope aspect, land use / land cover, drainage density, lineament density, road buffer and relative relief were assessed. All the factors were assigned to rank and weight based on the slope stability of the landslide susceptibility zones. Then the thematic maps were integrated using ArcGIS tool and landslide susceptibility zonation was obtained and classified into five categories ; very low, low, moderate, high and very high. The landslide susceptibility map is validated with R-index and landslide inventory data collected from the field using GPS measurement. The distribution of susceptibility zones is ; 16.5% located in very low, 28.70% in low, 24.70% in moderate, 19.90% in high and 10.20% in very high zones. The R-index indicated that about 64% landslide occurences correlated with high to very high landslide susceptiblity zones. The model validation indicated that the method adopted in this study is suitable for landslide disaster mapping and planning.

scholarly journals Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping

2020 ◽  
Vol 10 (18) ◽  
pp. 6335 ◽  
Author(s):  
Kamila Pawluszek-Filipiak ◽  
Natalia Oreńczak ◽  
Marta Pasternak
Keyword(s):  
Landslide Susceptibility ◽  
Research Question ◽  
Susceptibility Mapping ◽  
Landslide Inventory ◽  
Landslide Susceptibility Mapping ◽  
High Susceptibility ◽  
Geological Structures ◽  
Area Index ◽  
Landslide Occurrence ◽  
Very High

To mitigate the negative effects of landslide occurrence, there is a need for effective landslide susceptibility mapping (LSM). The fundamental source for LSM is landslide inventory. Unfortunately, there are still areas where landslide inventories are not generated due to financial or reachability constraints. Considering this led to the following research question: can we model landslide susceptibility in an area for which landslide inventory is not available but where such is available for surrounding areas? To answer this question, we performed cross-modeling by using various strategies for landslide susceptibility. Namely, landslide susceptibility was cross-modeled by using two adjacent regions (“Łososina” and “Gródek”) separated by the Rożnów Lake and Dunajec River. Thus, 46% and 54% of the total detected landslides were used for the LSM in “Łososina” and “Gródek” model, respectively. Various topographical, geological, hydrological and environmental landslide-conditioning factors (LCFs) were created. These LCFs were generated on the basis of the Digital Elevation Model (DEM), Sentinel-2A data, a digitized geological and soil suitability map, precipitation, the road network and the Różnów lake shapefile. For LSM, we applied the Frequency Ratio (FR) and Landslide Susceptibility Index (LSI) methods. Five zones showing various landslide susceptibilities were generated via Natural Jenks. The Seed Cell Area Index (SCAI) and Relative Landslide Density Index were used for model validation. Even when the SCAI indicated extremely high values for “very low” susceptibility classes and very small values for “very high” susceptibility classes in the training and validation areas, the accuracy of the LSM in the validation areas was significantly lower. In the “Łososina” model, 90% and 57% of the landslides fell into the “high” and “very high” susceptibility zones in the training and validation areas, respectively. In the “Gródek” model, 86% and 46% of the landslides fell into the “high” and “very high” susceptibility zones in the training and validation areas, respectively. Moreover, the comparison between these two models was performed. Discrepancies between these two models exist in the areas of critical geological structures (thrust and fault proximity), and the reliability for such susceptibility zones can be low (2–3 susceptibility zone difference). However, such areas cover only 11% of the analyzed area; thus, we can conclude that in remaining regions (89%), LSM generated by the inventory for the surrounding area can be useful. Therefore, the low reliability of such a map in areas of critical geological structures should be borne in mind.

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