Landslide Susceptibility Assessment: Identification and Hazard Mapping of Gandaki Province, Nepal

Landslides considered as a common hazard, affecting constantly the administrative territory of Gandaki province, located in the central part of Nepal. Impact of landslides is significant due to its specific geological, anthropic, vegetation and other circumstances. The main aim of this study was to identify the factors determining landslides and forming a landslide susceptibility mapping of study area. The fieldwork was conducted, where 128 GPS locations was recorded throughout the study area. This study also used the maximum entropy model using MaxEnt software, taking into account of various landslide-causing factors, resulting major variables of landslides risk and formed susceptibility mapping of landslide. It is identified that slope and land use land cover are most important variables to increase the landslide risk. Findings highlight that lands around the riversides and steep slopes are more risky area in terms of landslides. Moreover, it is found that the area of 3371.32 km2 measured as landslide risk zone in this province, where Gorkha district categorized as most vulnerable place for landslide, comprising of largest area of landslide risk zone while Parbat district has low amount of risk land. Since the human casualties and property loss are the major consequences of the disaster, it is essential to identify and analyse the factors determining for landslide and developing the landslide susceptibility mapping of Gandaki province, which could be taken into account while developing mitigation and coping strategies.

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Parameters Derived from and/or Used with Digital Elevation Models (DEMs) for Landslide Susceptibility Mapping and Landslide Risk Assessment: A Review

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8120545 ◽

2019 ◽

Vol 8 (12) ◽

pp. 545 ◽

Cited By ~ 8

Author(s):

Nayyer Saleem ◽

Md. Enamul Huq ◽

Nana Yaw Danquah Twumasi ◽

Akib Javed ◽

Asif Sajjad

Keyword(s):

Risk Assessment ◽

Landslide Susceptibility ◽

3D Visualization ◽

Digital Elevation Models ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Landslide Risk ◽

Conditioning Factors ◽

Digital Elevation ◽

The Impact

Digital elevation models (DEMs) are considered an imperative tool for many 3D visualization applications; however, for applications related to topography, they are exploited mostly as a basic source of information. In the study of landslide susceptibility mapping, parameters or landslide conditioning factors are deduced from the information related to DEMs, especially elevation. In this paper conditioning factors related with topography are analyzed and the impact of resolution and accuracy of DEMs on these factors is discussed. Previously conducted research on landslide susceptibility mapping using these factors or parameters through exploiting different methods or models in the last two decades is reviewed, and modern trends in this field are presented in a tabulated form. Two factors or parameters are proposed for inclusion in landslide inventory list as a conditioning factor and a risk assessment parameter for future studies.

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GIS-Based Landslide Susceptibility Mapping for Land Use Planning and Risk Assessment

Land ◽

10.3390/land10020162 ◽

2021 ◽

Vol 10 (2) ◽

pp. 162

Author(s):

Anna Roccati ◽

Guido Paliaga ◽

Fabio Luino ◽

Francesco Faccini ◽

Laura Turconi

Keyword(s):

Risk Assessment ◽

Land Use ◽

Land Use Planning ◽

Landslide Susceptibility ◽

Risk Mitigation ◽

Current Model ◽

Susceptibility Mapping ◽

Mitigation Strategies ◽

Landslide Susceptibility Mapping ◽

Landslide Risk

Landslide susceptibility mapping is essential for a suitable land use managing and risk assessment. In this work a GIS-based approach has been proposed to map landslide susceptibility in the Portofino promontory, a Mediterranean area that is periodically hit by intense rain events that induce often shallow landslides. Based on over 110 years landslides inventory and experts’ judgements, a semi-quantitative analytical hierarchy process (AHP) method has been applied to assess the role of nine landslide conditioning factors, which include both natural and anthropogenic elements. A separated subset of landslide data has been used to validate the map. Our findings reveal that areas where possible future landslides may occur are larger than those identified in the actual official map adopted in land use and risk management. The way the new map has been compiled seems more oriented towards the possible future landslide scenario, rather than weighting with higher importance the existing landslides as in the current model. The paper provides a useful decision support tool to implement risk mitigation strategies and to better apply land use planning. Allowing to modify factors in order to local features, the proposed methodology may be adopted in different conditions or geographical context featured by rainfall induced landslide risk.

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Analyzing the Effects of Spatial Resolution for Small Landslide Susceptibility and Hazard Mapping

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-1-293-2014 ◽

2014 ◽

Vol XL-1 ◽

pp. 293-300 ◽

Cited By ~ 5

Author(s):

O. E. Mora ◽

M. G. Lenzano ◽

C. K. Toth ◽

D. A. Grejner-Brzezinska

Keyword(s):

Remote Sensing ◽

Spatial Resolution ◽

Landslide Susceptibility ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Hazard Mapping ◽

Surface Features ◽

Remote Sensing Technology ◽

Sensing Technology ◽

Landslide Morphology

Spatial resolution plays an important role in remote sensing technology as it defines the smallest scale at which surface features may be extracted, identified, and mapped. Remote sensing technology has become a vital component in recent developments for landslide susceptibility mapping. The spatial resolution is essential, especially when landslides are small and the dimensions of slope failures vary. If the spatial resolution is relevant to the surface features found in the landslide morphology, it will help improve the extraction, identification and mapping of landslide surface features. Although, the spatial resolution is a well-known issue, few studies have demonstrated the potential effects it may have on small landslide susceptibility mapping. For these reasons, an evaluation to assess the impact of spatial resolution was performed using data acquired along a transportation corridor in Zanesville, Ohio. Using a landslide susceptibility mapping algorithm, landslide surface features were extracted and identified on a cell-by-cell basis from Digital Elevation Models (DEM) generated at 50, 100, 200 and 400 cm spatial resolution. The performance of the landslide surface feature extraction algorithm was then evaluated using an inventory map and a confusion matrix to assess the effects of spatial resolution. In addition to assessing the performance of the algorithm, we statistically analyzed the surface features and their relevant patterns. The results from this evaluation reveal patterns caused by the varying spatial resolution. From this study we can conclude that the spatial resolution has an effect on the accuracy and surface features extracted for small landslide susceptibility mapping, as the performance is dependent on the scale of the landslide morphology.

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Risk Factor Detection and Landslide Susceptibility Mapping Using Geo-Detector and Random Forest Models: The 2018 Hokkaido Eastern Iburi Earthquake

Remote Sensing ◽

10.3390/rs13061157 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1157

Author(s):

Yimo Liu ◽

Wanchang Zhang ◽

Zhijie Zhang ◽

Qiang Xu ◽

Weile Li

Keyword(s):

Random Forest ◽

Landslide Susceptibility ◽

Prediction Accuracy ◽

Integrated Model ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Landslide Risk ◽

Conditioning Factors ◽

Q Statistic ◽

Selection Of

Landslide susceptibility mapping is an effective approach for landslide risk prevention and assessments. The occurrence of slope instability is highly correlated with intrinsic variables that contribute to the occurrence of landslides, such as geology, geomorphology, climate, hydrology, etc. However, feature selection of those conditioning factors to constitute datasets with optimal predictive capability effectively and accurately is still an open question. The present study aims to examine further the integration of the selected landslide conditioning factors with Q-statistic in Geo-detector for determining stratification and selection of landslide conditioning factors in landslide risk analysis as to ultimately optimize landslide susceptibility model prediction. The location chosen for the study was Atsuma Town, which suffered from landslides following the Eastern Iburi Earthquake in 2018 in Hokkaido, Japan. A total of 13 conditioning factors were obtained from different sources belonging to six categories: geology, geomorphology, seismology, hydrology, land cover/use and human activity; these were selected to generate the datasets for landslide susceptibility mapping. The original datasets of landslide conditioning factors were analyzed with Q-statistic in Geo-detector to examine their explanatory powers regarding the occurrence of landslides. A Random Forest (RF) model was adopted for landslide susceptibility mapping. Subsequently, four subsets, including the Manually delineated landslide Points with 9 features Dataset (MPD9), the Randomly delineated landslide Points with 9 features Dataset (RPD9), the Manually delineated landslide Points with 13 features Dataset (MPD13), and the Randomly delineated landslide Points with 13 features Dataset (RPD13), were selected by an analysis of Q-statistic for training and validating the Geo-detector-RF- integrated model. Overall, using dataset MPD9, the Geo-detector-RF-integrated model yielded the highest prediction accuracy (89.90%), followed by using dataset MPD13 (89.53%), dataset RPD13 (88.63%) and dataset RPD9 (87.07%), which implied that optimized conditioning factors can effectively improve the prediction accuracy of landslide susceptibility mapping.

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Deep learning-based landslide susceptibility mapping

Scientific Reports ◽

10.1038/s41598-021-03585-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Mohammad Azarafza ◽

Mehdi Azarafza ◽

Haluk Akgün ◽

Peter M. Atkinson ◽

Reza Derakhshani

Keyword(s):

Landslide Susceptibility ◽

Naive Bayes ◽

Confusion Matrix ◽

Naïve Bayes ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Support Vector ◽

Landslide Risk ◽

Isfahan Province ◽

Wide Range

AbstractLandslides are considered as one of the most devastating natural hazards in Iran, causing extensive damage and loss of life. Landslide susceptibility maps for landslide prone areas can be used to plan for and mitigate the consequences of catastrophic landsliding events. Here, we developed a deep convolutional neural network (CNN–DNN) for mapping landslide susceptibility, and evaluated it on the Isfahan province, Iran, which has not previously been assessed on such a scale. The proposed model was trained and validated using training (80%) and testing (20%) datasets, each containing relevant data on historical landslides, field records and remote sensing images, and a range of geomorphological, geological, environmental and human activity factors as covariates. The CNN–DNN model prediction accuracy was tested using a wide range of statistics from the confusion matrix and error indices from the receiver operating characteristic (ROC) curve. The CNN–DNN model was evaluated comprehensively by comparing it to several state-of-the-art benchmark machine learning techniques including the support vector machine (SVM), logistic regression (LR), Gaussian naïve Bayes (GNB), multilayer perceptron (MLP), Bernoulli Naïve Bayes (BNB) and decision tree (DT) classifiers. The CNN–DNN model for landslide susceptibility mapping was found to predict more accurately than the benchmark algorithms, with an AUC = 90.9%, IRs = 84.8%, MSE = 0.17, RMSE = 0.40, and MAPE = 0.42. The map provided by the CNN–DNN clearly revealed a high-susceptibility area in the west and southwest, related to the main Zagros trend in the province. These findings can be of great utility for landslide risk management and land use planning in the Isfahan province.

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