scholarly journals Geoinformation-based landslide susceptibility mapping in subtropical area

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaoting Zhou ◽  
Weicheng Wu ◽  
Yaozu Qin ◽  
Xiao Fu
Keyword(s):  
Local Governments ◽  
Landslide Hazard ◽  
Hybrid Modeling ◽  
Weight Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Hazard Mapping ◽  
Landslide Risk ◽  
Human Society ◽  
Subtropical Area

AbstractMapping susceptibility of landslide disaster is essential in subtropical area, where abundant rainfall may trigger landslide and mudflow, causing damages to human society. The purpose of this paper is to propose an integrated methodology to achieve such a mapping work with improved prediction results using hybrid modeling taking Chongren, Jiangxi as an example. The methodology is composed of the optimal discretization of the continuous geo-environmental factors based on entropy, weight of evidence (WoE) calculation and application of the known machine learning (ML) models, e.g., Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR). The results show the effectiveness of the proposed hybrid modeling for landslide hazard mapping in which the prediction accuracy vs the validation set reach 82.35–91.02% with an AUC [area under the receiver operating characteristic (ROC) curve] of 0.912–0.970. The RF algorithm performs best among the observed three ML algorithms and WoE-based RF modeling will be recommended for the similar landslide risk prediction elsewhere. We believe that our research can provide an operational reference for predicting the landslide hazard in the subtropical area and serve for disaster reduction and prevention action of the local governments.

The 2010 Yushu earthquake triggered landslide hazard mapping using GIS and weight of evidence modeling

2012 ◽  
Vol 66 (6) ◽  
pp. 1603-1616 ◽  
Author(s):  
Chong Xu ◽  
Xiwei Xu ◽  
Yuan Hsi Lee ◽  
Xibin Tan ◽  
Guihua Yu ◽  
...  
Keyword(s):  
Landslide Hazard ◽  
Weight Of Evidence ◽  
Hazard Mapping ◽  
Yushu Earthquake ◽  
Landslide Hazard Mapping

scholarly journals Assessment of Landslide Hazard in Jiangxi Using Geo-information Technology

2021 ◽  
Vol 9 ◽  
Author(s):  
Penghui Ou ◽  
Weicheng Wu ◽  
Yaozu Qin ◽  
Xiaoting Zhou ◽  
Wenchao Huangfu ◽  
...  
Keyword(s):  
Regional Scale ◽  
Binary Logistic Regression ◽  
Technical Support ◽  
Landslide Hazard ◽  
Information Value ◽  
Hazard Mapping ◽  
Landslide Risk ◽  
Risk Modeling ◽  
Binary Logistic Regression Model ◽  
Linear Features

Landslides constitute a severe environmental problem in Jiangxi, China. This research was aimed at conducting landslide hazard assessment to provide technical support for disaster reduction and prevention action in the province. Fourteen geo-environmental factors, e.g., slope, elevation, road, river, fault, lithology, rainfall, and land cover types, were selected for this study. A test was made in two cases: (1) only based on the main linear features, e.g., main rivers and roads, and (2) with detailed complete linear features including all levels of roads and rivers. After buffering of the linear features, an information value (IV) analysis was applied to quantify the distribution of the observed landslides for each subset of the 14 factors. The results were inputted into the binary logistic regression model (LRM) for landslide risk modeling, taking the known landslide points as a training set (70% of the total 9,525 points). The calculated probability of a landslide was further classified into five grades with an interval of 0.2 for hazard mapping: very high (3.70%), high (4.05%), moderate (18.72%), low (27.17%), and stable zones (46.36%). The accuracy was evaluated by AUC [the area under the receiver operating characteristic (ROC) curve] vs. the validation set (30%, the remaining landslides). The final results show that with increasing the completeness of the linear features, the modeling reliability also significantly increased. We hence concluded that the tested methodology is capable of achieving the landslide hazard prediction at regional scale, and the results may provide technical support for geohazard reduction and prevention in the studied province.

scholarly journals Spatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan

Water ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3312
Author(s):  
Jiaying Li ◽  
Weidong Wang ◽  
Yange Li ◽  
Zheng Han ◽  
Guangqi Chen
Keyword(s):  
Landslide Susceptibility ◽  
Heavy Rainfall ◽  
Characteristic Curve ◽  
Landslide Susceptibility Mapping ◽  
Economic Losses ◽  
Support Vector ◽  
Landslide Risk ◽  
Chi Square ◽  
Area Index ◽  
The Impact

Landslide represents an increasing menace causing huge casualties and economic losses, and rainfall is a predominant factor inducing landslides. Landslide susceptibility assessment (LSA) is a commonly used and effective method to prevent landslide risk, however, the LSA does not analyze the impact of the rainfall on landslides which is significant and non-negligible. Therefore, the spatiotemporal LSA considering the inducing effect of rainfall is proposed to improve accuracy and applicability. In this study, the influencing factors are selected using the chi-square test, out-of-bag error and multicollinearity test. The spatial LSA are thus obtained using the random forest (RF) model, deep belief networks model and support vector machine, and compared using receiver operating characteristic curve and seed cell area index to determine the optimal assessment result. According to the heavy rainfall characteristics in the study area, the rainfall period is divided into four stages, and the effective rainfall model is employed to generate the rainfall impact (RI) maps of the four stages. The spatiotemporal LSAs are obtained by coupling the optimal spatial LSA and various RI maps and verified using the landslide warning map. The results demonstrate that the optimal spatiotemporal LSA is obtained using the spatial LSA of the RF model and temporal LSA of the rainfall data in the peak stage. It can predict the area where rainfall-induced landslides are likely to occur and prevent landslide risk.

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

2020 ◽  
Vol 3 ◽  
pp. 11-21
Author(s):  
Khagendra Raj Poudel ◽  
Ramesh Hamal ◽  
Naresh Paudel
Keyword(s):  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Hazard Mapping ◽  
Landslide Risk ◽  
Entropy Model ◽  
Risk Zone ◽  
Property Loss ◽  
And Coping ◽  
Steep Slopes

 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.

scholarly journals Deep learning-based landslide susceptibility mapping

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.

scholarly journals Landslide hazard mapping in GIS

2017 ◽  
Vol 53 ◽  
pp. 63-91
Author(s):  
Ranjan Kumar Dahal
Keyword(s):  
Risk Assessment ◽  
Land Use Planning ◽  
Landslide Hazard ◽  
Hazard Mapping ◽  
Landslide Risk ◽  
Property A ◽  
Geologic Hazard ◽  
Hazard And Risk ◽  
Hazard And Risk Assessment ◽  
Elevation Model

Landslides are common geologic hazard occurring in all parts of the world predominantly in the rainy season. In recent years, landslide risk mapping has played an important role in developing land-use planning and it helps to minimize the loss of lives and damages to property. A variety of approaches have been used in landslide hazard and risk assessment and these can be classified into heuristic approach, statistical approach, deterministic approach, etc. An abrupt development of computers after 1990, geographic information systems (GIS) became essential tools for landslide hazard assessment. However, validation and replication is always difficult and there are little works on the satisfactory validation of various approaches. This paper deals with several aspects of landslide hazard and risk assessment by presenting a focalized review of GIS-based landslide hazard and risk assessment with a critical information of the state of the art in using GIS and digital elevation model (DEM) derivative for landslide hazard and risk assessment. This paper also describes some statistical and deterministic approaches and suggests detail step-by-step methodologies. It also describes in brief about integration of various database software and GIS.

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