scholarly journals Landslide Susceptibility Mapping and Their Rainfall Thresholds Model in Tinalah Watershed, Kulon Progo District, Yogyakarta Special Region, Indonesia

2021 ◽  
Vol 6 (2) ◽  
pp. 112
Author(s):  
Thema Arrisaldi ◽  
Wahyu Wilopo ◽  
Teuku Faisal Fathani

Landslide often occurred in Tinalah watershed, Kulon Progo District, every year. The frequency of landslide events is increasing after high rainfall intensity. Some factors control landslides such as slope gradient, land use, geological structure, slope hydrology, and geological condition. This research has an objective to develop the susceptibility map of Tinalah watershed and to identify the rainfall threshold to trigger a landslide. The development of the susceptibility map using frequency ratio method with four parameters including slope, type of rock, land use, and lineament density. The landslide data were collected during the field survey and from regional disaster management authority (BPBD) Kulon Progo. Rainfall data were collected from BMKG and GSMap. Soil analysis also was conducted to develop a numerical model to verify the rainfall threshold value. The result shows a high susceptibility of the landslide area is dominated in Tinalah watershed. The rainfall threshold for the low susceptibility of the landslide zone is I=490.14 D-1.404with 5-7 days antecedent rain. The rainfall threshold for medium susceptibility map is I=164.32D-0,689 3-7 days antecedent rain. Moreover, the rainfall threshold for the high susceptibility of the landslide zone is 111.62 D-0.779, with 2-7 days antecedent rain.

2020 ◽  
Vol 42 (1) ◽  
pp. 55-66 ◽  
Author(s):  
Dang Quang Thanh ◽  
Duy Huu Nguyen ◽  
Indra Prakash ◽  
Abolfazl Jaafari ◽  
Viet -Tien Nguyen ◽  
...  

Landslide susceptibility mapping of the city of Da Lat, which is located in the landslide prone area of Lam Dong province of Central Vietnam region, was carried out using GIS based frequency ratio (FR) method. There are number of methods available but FR method is simple and widely used method for landslide susceptibility mapping. In the present study, eight topographical and geo-environmental landslide-conditioning factors were used including slope, elevation, land use, weathering crust, soil, lithology, distance to geology features, and stream density in conjunction with 70 past landslide locations. The results show that 6.27% of the area is in the very low susceptibility area, 21.03% in the low susceptibility area, 27.09% in the moderate susceptibility area and 27.41% of the area is in the high susceptibility zone and 18.21% in the very high susceptibility zone. The landslide susceptibility map produced in this study helps to assist decision makers in proper land use management and planning.


2021 ◽  
Vol 13 (11) ◽  
pp. 2166
Author(s):  
Xin Yang ◽  
Rui Liu ◽  
Mei Yang ◽  
Jingjue Chen ◽  
Tianqiang Liu ◽  
...  

This study proposed a new hybrid model based on the convolutional neural network (CNN) for making effective use of historical datasets and producing a reliable landslide susceptibility map. The proposed model consists of two parts; one is the extraction of landslide spatial information using two-dimensional CNN and pixel windows, and the other is to capture the correlated features among the conditioning factors using one-dimensional convolutional operations. To evaluate the validity of the proposed model, two pure CNN models and the previously used methods of random forest and a support vector machine were selected as the benchmark models. A total of 621 earthquake-triggered landslides in Ludian County, China and 14 conditioning factors derived from the topography, geological, hydrological, geophysical, land use and land cover data were used to generate a geospatial dataset. The conditioning factors were then selected and analyzed by a multicollinearity analysis and the frequency ratio method. Finally, the trained model calculated the landslide probability of each pixel in the study area and produced the resultant susceptibility map. The results indicated that the hybrid model benefitted from the features extraction capability of the CNN and achieved high-performance results in terms of the area under the receiver operating characteristic curve (AUC) and statistical indices. Moreover, the proposed model had 6.2% and 3.7% more improvement than the two pure CNN models in terms of the AUC, respectively. Therefore, the proposed model is capable of accurately mapping landslide susceptibility and providing a promising method for hazard mitigation and land use planning. Additionally, it is recommended to be applied to other areas of the world.


2021 ◽  
Vol 13 (2) ◽  
pp. 238
Author(s):  
Zhice Fang ◽  
Yi Wang ◽  
Gonghao Duan ◽  
Ling Peng

This study presents a new ensemble framework to predict landslide susceptibility by integrating decision trees (DTs) with the rotation forest (RF) ensemble technique. The proposed framework mainly includes four steps. First, training and validation sets are randomly selected according to historical landslide locations. Then, landslide conditioning factors are selected and screened by the gain ratio method. Next, several training subsets are produced from the training set and a series of trained DTs are obtained by using a DT as a base classifier couple with different training subsets. Finally, the resultant landslide susceptibility map is produced by combining all the DT classification results using the RF ensemble technique. Experimental results demonstrate that the performance of all the DTs can be effectively improved by integrating them with the RF ensemble technique. Specifically, the proposed ensemble methods achieved the predictive values of 0.012–0.121 higher than the DTs in terms of area under the curve (AUC). Furthermore, the proposed ensemble methods are better than the most popular ensemble methods with the predictive values of 0.005–0.083 in terms of AUC. Therefore, the proposed ensemble framework is effective to further improve the spatial prediction of landslides.


Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1402 ◽  
Author(s):  
Nohani ◽  
Moharrami ◽  
Sharafi ◽  
Khosravi ◽  
Pradhan ◽  
...  

Landslides are the most frequent phenomenon in the northern part of Iran, which cause considerable financial and life damages every year. One of the most widely used approaches to reduce these damages is preparing a landslide susceptibility map (LSM) using suitable methods and selecting the proper conditioning factors. The current study is aimed at comparing four bivariate models, namely the frequency ratio (FR), Shannon entropy (SE), weights of evidence (WoE), and evidential belief function (EBF), for a LSM of Klijanrestagh Watershed, Iran. Firstly, 109 locations of landslides were obtained from field surveys and interpretation of aerial photographs. Then, the locations were categorized into two groups of 70% (74 locations) and 30% (35 locations), randomly, for modeling and validation processes, respectively. Then, 10 conditioning factors of slope aspect, curvature, elevation, distance from fault, lithology, normalized difference vegetation index (NDVI), distance from the river, distance from the road, the slope angle, and land use were determined to construct the spatial database. From the results of multicollinearity, it was concluded that no collinearity existed between the 10 considered conditioning factors in the occurrence of landslides. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used for validation of the four achieved LSMs. The AUC results introduced the success rates of 0.8, 0.86, 0.84, and 0.85 for EBF, WoE, SE, and FR, respectively. Also, they indicated that the rates of prediction were 0.84, 0.83, 0.82, and 0.79 for WoE, FR, SE, and EBF, respectively. Therefore, the WoE model, having the highest AUC, was the most accurate method among the four implemented methods in identifying the regions at risk of future landslides in the study area. The outcomes of this research are useful and essential for the government, planners, decision makers, researchers, and general land-use planners in the study area.


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 ◽  
...  

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.


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 ◽  
...  

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.


2016 ◽  
Vol 50 (1) ◽  
pp. 83-93
Author(s):  
Khagendra Poudel ◽  
Amar Deep Regmi

 The Tulsipur-Kapurkot road is the main highway connecting the northern part of Rapti zone to the rest of Nepal. It suffers from numerous mass movements obstructing the traffic every monsoon. This paper describes the development of landslide susceptibility map of the road section and its surrounding regions based on bivariate (frequency ratio) statistical model. Geologically, the road section passes through the rocks of Lesser Himalaya, Siwaliks and Quaternary deposits. Several large and small scale thrusts present within the area making it unstable. For the susceptibility evaluation of the region, first a landslide inventory map consisting more than 187 landslides was prepared. These landslide locations were then randomly partitioned into a ratio of 80/20 for training and validating the models. Second, nine landslide causative factors were prepared. They include slope, aspect, elevation, curvature, geology, land use, distance from fault, distance from river and distance from major road sections. Finally, a landslide susceptibility map of the region was obtained and it was validated using area under curve (AUC). From the analysis, the success rate of the model is found to be 85.18% and predictive accuracy is 78.76%. The resultant susceptibility map shows that the highway in between Ranagaun to Khamari and Ramri to Kapurkot falls within very high to high susceptible zone. Besides, it is observed that the Kapurkot Bazar is also under high landslide susceptible zone. Furthermore, the northern part of the watershed lies in high landslide susceptible zone. The result of this study is useful for land use planning and decision making in landslide management activities.


Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 267 ◽  
Author(s):  
Abhirup Dikshit ◽  
Raju Sarkar ◽  
Biswajeet Pradhan ◽  
Ratiranjan Jena ◽  
Dowchu Drukpa ◽  
...  

Landslides are one of the major natural disasters that Bhutan faces every year. The monsoon season in Bhutan is usually marked by heavy rainfall, which leads to multiple landslides, especially across the highways, and affects the entire transportation network of the nation. The determinations of rainfall thresholds are often used to predict the possible occurrence of landslides. A rainfall threshold was defined along Samdrup Jongkhar–Trashigang highway in eastern Bhutan using cumulated event rainfall and antecedent rainfall conditions. Threshold values were determined using the available daily rainfall and landslide data from 2014 to 2017, and validated using the 2018 dataset. The threshold determined was used to estimate temporal probability using a Poisson probability model. Finally, a landslide susceptibility map using the analytic hierarchy process was developed for the highway to identify the sections of the highway that are more susceptible to landslides. The accuracy of the model was validated using the area under the receiver operating characteristic curves. The results presented here may be regarded as a first step towards understanding of landslide hazards and development of an early warning system for a region where such studies have not previously been conducted.


2018 ◽  
Vol 149 ◽  
pp. 02084 ◽  
Author(s):  
L Ait Brahim ◽  
M Bousta ◽  
I A Jemmah ◽  
I El Hamdouni ◽  
A ElMahsani ◽  
...  

The peninsula of Tangier (Northern Morocco) is submitted to a significant number of landslides each year due to its lithological, structural and morphological complexity; which cause a lot of damage to the road network and other related infrastructure. The main objective of this study is to create a landslide indexed susceptibility map of Tangier peninsula, by using AHP (Analytical Hierarchical Processes) model to calculate each factor’s weight. The work is made via GIS by using an ArcGIS AHP extension. In the current research, First of all, the four main types of landslides were identified and mapped from existing documents, works and new data which came from either remote sensing or fieldwork. Lithology, land use, slope, hypsometry, exposure, fault density and drainage network density were used as main parameters controlling the occurrence of the selected landslides. Then, afterward, each parameter is classified into a number of significant classes based on their relative influence on gravitational movement genesis. The validity of the susceptibility zoning map which is obtained through linear summation of indexed maps was tested and cross-checked by inventoried and studied landslides. The obtained landslide susceptibility map constitutes a powerful decision-making tool in land-use planning, i.e. New highways, secondary highways, railways, etc. within the national development program in the Northern provinces. It is a necessary step for the landslides hazard assessment in the Tangier peninsula in northern Morocco.


2019 ◽  
Vol 80 (2) ◽  
pp. 105-116
Author(s):  
Sonja Djokanovic

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.


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