Landslide hazard assessment around MCT zone in Marsyangdi River basin, west Nepal

In the hilly and mountainous terrain of Nepal, landslide is the most common natural hazard especially during prolong rainfall. Every year landslide cost lives and causes injuries. In order to address this problem, the best that can be done is to prepare the landslide hazard map of the area, apply mitigation measures and evacuate the high hazardous area, if necessary. Landslide hazard assessment is the primary tool so as to understand the nature and characteristics of the slope that are prone to failure. Logistic Regression Model is used for the preparation of landslide hazard map of the Besi Shahar-Tal area in Marsyangdi River basin in west Nepal. The causative factors such as elevation, slope, slope aspect, land use, geology, rainfall, lineament density, stream density are used. All the thematic layers of these parameters are prepared in GIS and logistic regression analysis is done by using Statistical Package for Social Science (SPSS). Five different hazard zones are separated namely very low hazard zone, low hazard zone, medium hazard zone, high hazard zone and very high hazard zone. The high hazard zone is lying along the Marsyangdi River and its tributaries.

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Landslide hazard zonation using logistic regression technique: the case of Shafe and Baso catchments, Gamo highland, Ethiopia

10.21203/rs.3.rs-191158/v1 ◽

2021 ◽

Author(s):

Leulalem Shano ◽

Tarun Kumar Raghuvanshi ◽

Matebie Meten

Keyword(s):

Land Use ◽

Logistic Regression ◽

Influencing Factors ◽

Landslide Hazard ◽

Slope Aspect ◽

Hazard Map ◽

Hazard Zonation ◽

Landslide Hazard Zonation ◽

Landslide Hazard Map ◽

Very High

Abstract Landslide hazard zonation plays an important role in safe and viable infrastructure development, urbanization, land use, and environmental planning. The Shafe and Baso catchments are found in the Gamo highland which has been highly degraded by erosion and landslides thereby affecting the lives of the local people. In recent decades, recurrent landslide incidences were frequently occurring in this Highland region of Ethiopia in almost every rainy season. This demands landslide hazard zonation in the study area in order to alleviate the problems associated with these landslides. The main objectives of this study are to identify the spatiotemporal landslide distribution of the area; evaluate the landslide influencing factors and prepare the landslide hazard map. In the present study, lithology, groundwater conditions, distance to faults, morphometric factors (slope, aspect and curvature), and land use/land cover were considered as landslide predisposing/influencing factors while precipitation was a triggering factor. All these factor maps and landslide inventory maps were integrated using ArcGIS 10.4 environment. For data analysis, the principle of logistic regression was applied in a statistical package for social sciences (SPSS). The result from this statistical analysis showed that the landslide influencing factors like distance to fault, distance to stream, groundwater zones, lithological units and aspect have revealed the highest contribution to landslide occurrence as they showed greater than a unit odds ratio. The resulting landslide hazard map was divided into five classes: very low (13.48%), low (28.67%), moderate (31.62%), high (18%), and very high (8.2%) hazard zones which was then validated using the goodness of fit techniques and receiver operating characteristic curve (ROC) with an accuracy of 85.4. The high and very high landslide hazard zones should be avoided from further infrastructure and settlement planning unless proper and cost-effective landslide mitigation measures are implemented.

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Landslide hazard assessment using UAV imagery and GIS for road planning and development in Chure area: Sindhuli-Hetauda Section.

10.21203/rs.3.rs-72224/v1 ◽

2020 ◽

Author(s):

Bishnu Prasad Pandey ◽

Kumud Raj Kafle

Keyword(s):

Hazard Assessment ◽

Landslide Hazard ◽

Slope Aspect ◽

Hazard Mapping ◽

Surface Model ◽

Hazard Map ◽

Landslide Hazard Assessment ◽

Settlement Area ◽

Road Planning ◽

Hazard Levels

Abstract Chure hills are formed with the highly fragile, weak young sedimentary rocks and are environmentally sensitive. Road construction in this region is a big challenge to conserve the Chure from landslide. Occurrence of landslide hazard along the highway is the threat to the objective of timely, efficient and qualitative construction of highway. Landslide hazard map can greatly help in fixing and shifting of the alignment to reduce the loss of life and property. This study “Landslide hazard assessment using UAV imagery and GIS for road planning and development in Chure area: Sindhuli-Hetauda section” aims at creating the hazard map, landslide inventory map and designation of hazard levels in one of the sensitive areas: Chure section. With the use of the Unmanned Aerial Vehicles (UAVs) as the primary means of carrying out the topographical surveying, the study used the Digital Surface Model (DSM) and the orthomosaic map produced from the UAV survey in acquiring the relevant results for fulfilling the study objectives. The survey area being ~100Km in length along the road alignment, four of the most crucial sites on the basis of existing landslide area and impact of those landslide in road, cultivation and settlement in the study area were selected for surveying. The study concluded that use of UAV for hazard mapping has good, accuracies and high resolution data. The Root Mean Square Error (RMSE) of the survey for the individual sites were found to be 0.001m, 0.045 m, 0.044 m and 0.804 m, respectively. A detailed topographical map of the area was created, along with the hazard map, including the factors such as slope, aspect, curvature, elevation, lithology, distance to road, distance to river and soil type. Furthermore, the hazard levels for the surveyed area were also obtained: the largest area being medium 60.68%, 57.45%, 71.21% and 71.16 %, respectively, followed by high 32.59%, 18.91%, 17.03% and 11.54% respectively and low 6.73%, 23.64%, 11.76% and 17.30%, respectively in Chattiwan, Bhawanchuli, Gurji and Hakpara. It was also concluded that the forest area is at high risk followed by the bush and the settlement area in the Chattiwan, Bhawanchuli and Hakpara site and the cultivable land followed by the bush and the settlement area were found to be in high risks in the fourth site (Gurji).

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Landslide hazard and risk zonation of Thankot – Chalnakhel area,

Journal of Nepal Geological Society ◽

10.3126/jngs.v31i0.259 ◽

1970 ◽

Vol 31 ◽

pp. 43-50 ◽

Cited By ~ 6

Author(s):

Pradeep Paudyal ◽

Megh Raj Dhital

Keyword(s):

Landslide Hazard ◽

Geographical Information ◽

Hazard Map ◽

Risk Map ◽

Risk Zonation ◽

Hazard Zone ◽

Risk Zones ◽

Vulnerability Map ◽

Landslide Hazard Map ◽

Moderate Hazard

The rocks in the Thankot–Chalnakhel area constitute the Chandragiri Range bordering the Kathmandu valley. The Phulchauki Group of rocks comprise its steep and rugged south slope, whereas the gentle north slope is covered by fluvio-lacustrine deposits of the Kathmandu basin with some recent alluvial fans. During the field study, 94 landslides (covering about 0.24 sq km) were mapped. Most of them were triggered by intense rainfall within the last two years. Landslides are generally found on steep colluvial slope (25°–35°) and dry cultivated land. Based on a computer-based geographical information system, a landslide hazard map, a vulnerability map, and a risk map were prepared. The landslide hazard map shows 20% of the area under high hazard zone, 41% under moderate hazard zone, and 39% under low hazard zone. The risk map generated by combining the hazard map and vulnerability map shows 19% of the area under high and very high risk zones, 33% under moderate risk zone, and 48% under low and very low risk zones.

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Applying logistic regression method to determine combinatorial optimization of landslide-related factors and construct landslide hazard map in Khanh Vinh district, Khanh Hoa Province

Science and Technology Development Journal ◽

10.32508/stdj.v20ik4.1121 ◽

2017 ◽

Vol 20 (K4) ◽

pp. 76-83

Author(s):

Danh Thanh Nguyen ◽

Ngo Van Dau ◽

Dung Quoc Ta

Keyword(s):

Logistic Regression ◽

Combinatorial Optimization ◽

Regression Method ◽

Landslide Hazard ◽

Optimization Models ◽

Hazard Map ◽

Related Factors ◽

Maximum Precipitation ◽

Logistic Regression Method ◽

Landslide Hazard Map

The purpose of this study is to produce landslide hazard map in Khanh Vinh district, Khanh Hoa province using logistic regression method integrated with GIS analytical tools. The spatial relationship between landslide-related factors such as topography; lithology; vegetation; maximum precipitation in year; distance from roads; distance from drainages; distance from faults and the distribution of landslides were used in the landslide hazard analyses. Using success rate and prediction rate curve assess the fit and accuracy of logistic regression method. The results show that this method have the goodness of fit and the high accuracy (Areas Under Curves - AUC = 0.8 ~ 0.9). Bayesian Model Average (BMA) of the R statistical software was applied to identify the most influential factors and the combinatorial optimization models of landslide-related factors. There are four the most important landslide-related factors and five combinatorial optimization models of landslide-related factors. Model 3 (slope angle, slope aspect, altitude, distance from roads and maximum precipitation in year) is the best optimization.

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COMBINING NEURAL NETWORKS AND GEOSTATISTICS FOR LANDSLIDE HAZARD ASSESSMENT OF SAN SALVADOR METROPOLITAN AREA, EL SALVADOR

Revista de Matemática Teoría y Aplicaciones ◽

10.15517/rmta.v23i1.22439 ◽

2017 ◽

Vol 23 (1) ◽

pp. 155-172

Author(s):

Ricardo Ríos ◽

Alexandre Ribó ◽

Roberto Mejía ◽

Giovanni Molina

Keyword(s):

Neural Networks ◽

Logistic Regression ◽

El Salvador ◽

Hazard Assessment ◽

Slope Angle ◽

Landslide Hazard ◽

Assessment Model ◽

Landslide Hazard Assessment ◽

San Salvador ◽

Geostatistical Approach

This contribution describes the creation of a landslide hazard assessment model for San Salvador, a department in El Salvador. The analysis started with an aerial photointerpretation from Ministry of Environment and Natural Resources of El Salvador (MARN Spanish acronym), where 4792 landslides were identified and georeferenced along with 7 conditioning factors including: geomorphology, geology, rainfall intensity, peak ground acceleration, slope angle, distance to road, and distance to geological fault. Artificial Neural Networks (ANN) were utilized to assess the susceptibility to landslides, achieving results where more than 80% of landslide were properly classified using in-sample and out of sample criteria. Logistic regression was used as base of comparison. Logistic regression obtained a lower performance. To complete the analysis we have performed interpolation of the points using the kriging method from geostatistical approach. Finally, the results show that is possible to derive a landslide hazard map, making use of a combination of ANNs and geostatistical techniques, thus the present study can help landslide mitigation in El Salvador.

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GIS-based spatial analysis and modeling for landslide hazard assessment: A case study in upper Minjiang River basin

Wuhan University Journal of Natural Sciences ◽

10.1007/bf02830175 ◽

2006 ◽

Vol 11 (4) ◽

pp. 847-852

Author(s):

Feng Wenlan ◽

Zhou Qigang ◽

Zhang Baolei ◽

Zhou Wancun ◽

Li Ainong ◽

...

Keyword(s):

Spatial Analysis ◽

River Basin ◽

Hazard Assessment ◽

Landslide Hazard ◽

Landslide Hazard Assessment ◽

Minjiang River

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Landslide Hazard Assessment Based on Bayesian Optimization-Support Vector Machine in Nanping City, China

10.21203/rs.3.rs-182891/v1 ◽

2021 ◽

Author(s):

Wei Xie ◽

Wen Nie ◽

Pooya Saffari ◽

Luis F. Robledo ◽

Pierre-Yves Descote ◽

...

Keyword(s):

Support Vector Machine ◽

Hazard Assessment ◽

Roc Curve ◽

Confusion Matrix ◽

Landslide Hazard ◽

Assessment Model ◽

Bayesian Optimization ◽

Slope Aspect ◽

Support Vector ◽

Landslide Hazard Assessment

Abstract Landslide hazard assessment is critical for preventing and mitigating landslide disasters. The tuning of model hyperparameters is of great importance to the accuracy and precision of one landslide hazard assessment model. In this study, Bayesian Optimization (BO) method was used to tune the hyperparameters of Support Vector Machine (SVM) model to obtain a high accuracy landslide hazard zoning map. 1711 historical landslide disaster points were obtained as landslide inventory in a case of Nanping City landslide hazard assessment. A total of 12 factors including elevation, slope, aspect, curvature, lithology, soil type, soil erosion, rainfall, river, land use, highway, and railway were selected as landslide conditional factors. The multicollinearity diagnosis was performed on factors using the Spearman correlation coefficient. 1711 landslides and 1711 non-landslides were collected as the dataset and divided into the same number of training dataset and testing dataset. The confusion matrix and receiver operating characteristic (ROC) curve were used to verify the models. The results of confusion matrix accuracy and the area under ROC curve (AUC) showed that BO-SVM (89.53%, 97%) performed better than only SVM (84.91%, 0.93), which indicated the superiority of the proposed method during this study.

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Validation of landslide hazard assessment by means of GPS monitoring technique – a case study in the Dolomites (Eastern Alps, Italy)

Natural Hazards and Earth System Science ◽

10.5194/nhess-7-185-2007 ◽

2007 ◽

Vol 7 (1) ◽

pp. 185-193 ◽

Cited By ~ 18

Author(s):

F. Tagliavini ◽

M. Mantovani ◽

G. Marcato ◽

A. Pasuto ◽

S. Silvano

Keyword(s):

River Basin ◽

Hazard Assessment ◽

Test Site ◽

Eastern Alps ◽

Landslide Hazard ◽

Deterministic Approach ◽

Landslide Hazard Assessment ◽

Historical Archive ◽

Swiss Confederation

Abstract. In the last years a research project aimed at the assessment of the landslide hazard and susceptibility in the high Cordevole river basin (Eastern Dolomites, Italy) have been carried out. The hazard map was made adopting the Swiss Confederation semi-deterministic approach that takes into account parameters such as velocity, geometry and frequency of landslides. Usually these parameters are collected by means of geological and morphological surveys, historical archive researches, aerophotogrammetric analysis etc. In this framework however the dynamics of an instable slope can be difficult to determine. This work aims at illustrating some progress in landslide hazard assessment using a modified version of the Swiss Confederation semi-deterministic approach in which the values of some parameters have been refined in order to accomplish more reliable results in hazard assessment. A validation of the accuracy of these new values, using GPS and inclinometric measurements, has been carried out on a test site located inside the high Cordevole river basin.

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