Rainfall-triggered large landslides on 15 December 2005 in Van Canh District, Binh Dinh Province, Vietnam

Duc, Do Minh

doi:10.1007/s10346-012-0362-4

Cited by 56 publications

(12 citation statements)

References 4 publications

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“…Furthermore, total surface radiation is the sum of direct solar radiation and diffuse solar radiation, and it indirectly influences the probability of landslide occurrence by affecting the aboveground vegetation and soil moisture [54]. The population density index affects landslide evolution indirectly by calculating human engineering activity [55].…”

Section: Appl Sci 2019 9 X For Peer Review 5 Of 18mentioning

confidence: 99%

Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models

Huang

Yan

et al. 2019

Applied Sciences

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Landslides are one type of serious geological hazard which cause immense losses of local life and property. Landslide susceptibility prediction (LSP) can be used to determine the spatial probability of landslide occurrence in a certain area. It is important to implement LSP for landslide hazard prevention and reduction. This study developed a particle-swarm-optimized multilayer perceptron (PSO-MLP) model for LSP implementation to overcome the drawbacks of the conventional gradient descent algorithm and to determine the optimal structural parameters of MLP. Shicheng County in Jiangxi Province of China was used as the study area. In total, 369 landslides, randomly selected non-landslides, and 14 landslide-related predisposing factors were used to train and test the present PSO-MLP model and three other comparative models (an MLP-only model with the gradient descent algorithm, a back-propagation neural network (BPNN), and an information value (IV) model). The results showed that the PSO-MLP model had the most accurate prediction performance (area under the receiver operating characteristic curve (AUC) of 0.822 and frequency ratio (FR) accuracy of 0.856) compared with the MLP-only (0.791 and 0.829), BPNN (0.800 and 0.840), and IV (0.788 and 0.824) models. It can be concluded that the proposed PSO-MLP model addresses the drawbacks of the MLP-only model well and performs better than conventional artificial neural networks (ANNs) and statistical models. The spatial probability distribution law of landslide occurrence in Shicheng County was well revealed by the landslide susceptibility map produced using the PSO-MLP model. Furthermore, the present PSO-MLP model may have higher prediction and classification performances in some other fields compared with conventional ANNs and statistical models.

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Section: Appl Sci 2019 9 X For Peer Review 5 Of 18mentioning

confidence: 99%

Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models

Huang

Yan

et al. 2019

Applied Sciences

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“…The total surface radiation, defined as the sum of direct and diffuse solar radiation received on the horizontal surface, affects the hydrological environments and land cover types of the slope [59]. Population density, which represents the population in a certain area, mainly plays an important role in human construction activities [60].…”

Section: Land Cover Factors Acquisition From Google Earth and Landsatmentioning

confidence: 99%

Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network

Zhu

Huang

Fan

et al. 2020

Sensors

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Landslide susceptibility prediction (LSP) modeling is an important and challenging problem. Landslide features are generally uncorrelated or nonlinearly correlated, resulting in limited LSP performance when leveraging conventional machine learning models. In this study, a deep-learning-based model using the long short-term memory (LSTM) recurrent neural network and conditional random field (CRF) in cascade-parallel form was proposed for making LSPs based on remote sensing (RS) images and a geographic information system (GIS). The RS images are the main data sources of landslide-related environmental factors, and a GIS is used to analyze, store, and display spatial big data. The cascade-parallel LSTM-CRF consists of frequency ratio values of environmental factors in the input layers, cascade-parallel LSTM for feature extraction in the hidden layers, and cascade-parallel full connection for classification and CRF for landslide/non-landslide state modeling in the output layers. The cascade-parallel form of LSTM can extract features from different layers and merge them into concrete features. The CRF is used to calculate the energy relationship between two grid points, and the extracted features are further smoothed and optimized. As a case study, the cascade-parallel LSTM-CRF was applied to Shicheng County of Jiangxi Province in China. A total of 2709 landslide grid cells were recorded and 2709 non-landslide grid cells were randomly selected from the study area. The results show that, compared with existing main traditional machine learning algorithms, such as multilayer perception, logistic regression, and decision tree, the proposed cascade-parallel LSTM-CRF had a higher landslide prediction rate (positive predictive rate: 72.44%, negative predictive rate: 80%, total predictive rate: 75.67%). In conclusion, the proposed cascade-parallel LSTM-CRF is a novel data-driven deep learning model that overcomes the limitations of traditional machine learning algorithms and achieves promising results for making LSPs.

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“…Rainfall: In almost all the inventoried landslides long duration of rainfall with varying intensities prior to the event was perceptually reported to trigger and is also cited as the main trigger for landslides in Tanzania, East Africa and the world at large [18,46,47]. Generally, the central, western, southeastern and southwestern parts of the country, constituting 75% of the country's land area, receive unimodal rainfall pattern while the northwestern and northeastern parts receive bi-modal pattern [48].…”

Section: Triggering Factors For Landslide Occurrencementioning

confidence: 99%

“…Unusual sounds and flashlights associated with landslide events reported in this inventory have also been reported in landslide events occurring in other parts of the world. For example, Duc [47] reported on loud booming noises, signifying forthcoming severe earthquake or the "angry angel of mountain associated with large landslides in Van Canh District, Binh Dinh Province, Vietnam. Ludwin et al [59] reported on the "A'yahos", a supernatural spirit power in form of a two-headed serpent, which is associated with earth shaking and rushes of turbid water, locally associated with five landslide locales along the trace of the Seattle Fault in USA.…”

Section: Community Perception Of Landslides and Disaster Risk Managementmentioning

confidence: 99%

“…Resettling victims to safer areas reported in this inventory has proved to be the most appropriate solution, and has been applied in many parts of the world, including the Mount Elgon ecosystem in Uganda [46], the Qingping area, southwest China [60] and Van Canh district, Binh province Vietnam [47]. However, availability and use of early warning system can avert the need for resettlement and thus, reduce cost to potential victims of future landslides.…”

Section: Community Perception Of Landslides and Disaster Risk Managementmentioning

confidence: 99%

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Review of Spatial and Temporal Distribution of Landslides in Tanzania

Ja¹,

Kervyn²

2017

J Ecosyst Ecogr

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This paper intended to inventorize the occurrence of landslides in Tanzania in temporal and spatial terms. Specifically, the inventory focused on identifying areas in Tanzania which are prone to landslides, identifying the frequency of landslide occurrence in the country, identifying the underlying and triggering factors for landslides, the socio-economic impacts of landslides and assessing the people's perception of landslides in Tanzania. Identification and mapping of landslide was achieved through the use primary and secondary sources of information. Primary data was acquired through exploratory field visit to the 5 landslide scars in the north and south Pare Mountains in the northern highlands of Tanzania. Short discussion with local people in the areas was made to gain insights into their perception of landslides. Secondary data was acquired through the review of achieved sources. This review was made in respect of the landslides that were not covered by the aforementioned exploratory field visit. The EM-DAT was also used to provide information on the number of landslide events, fatalities and socio-economic impacts. Google Earth™ search and image interpretation were used to located and describe landslides in other regions of the country that were not covered by the aforementioned exploratory field study. A 90 m resolution Digital Elevation Model (DEM) from USGS for Tanzania was used to map the landslide scars identified through Google Earth search. Google Earth search and image interpretation were supported by secondary information from archived sources. The inventory noted that landslides in Tanzania have received relatively limited attention both in the literature and in government disaster management plans. With exception of studies conducted in Uluguru Mountain in 1970s, no formal landslide research has been made in recent years. Despite extensive web searches, no landslide inventory has been made in Tanzania. This may presumably be due to existing assumption that landslides in Tanzania have low frequency of occurrence and are localized compared to other disasters such as floods, epidemics and drought, whose occurrence affect many more people, drawing more community, government and media attention. This has thus, affected the way landslide events are reported and managed in the country. This assertion however, does not preclude the fact that one/few landslide events, depending on their magnitude, where they occur and nature of elements at risk, can affect significant number of people and vice versa. This inventory has also documented cultural perspectives on landslides. Care must be taken to incorporate appropriate indigenous knowledge and people's perception pertaining to landslides and other disasters as these are critical in designing and implementing culturallyinformed and low cost disaster management strategies. This may, in turn, obviate the need or reduce cost for early warning systems. Landslide information provided by some reviewed sources is limited in time and space. Most of the visi...

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Rainfall-triggered large landslides on 15 December 2005 in Van Canh District, Binh Dinh Province, Vietnam

Cited by 56 publications

References 4 publications

Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models

Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models

Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network

Review of Spatial and Temporal Distribution of Landslides in Tanzania

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