Predicting the deformation of landslides is significant for landslide early warning. Taking the Shuping landslide in the Three Gorges Reservoir area (TGRA) as a case, the displacement is decomposed into two components by a time series model (TSM). Th...
The quality of sampling data critically influences landslide susceptibility prediction accuracy. Current studies commonly use a 1:1 ratio of landslide to non-landslide samples, failing to reflect natural geographical variability. This study develops...
Landslides could cause huge damages to properties and severe loss of lives. Landslides can be detected by analyzing the environmental data collected by wireless sensor networks (WSNs). However, environmental data are usually complex and undergo rapid...
Landslide susceptibility prediction has the disadvantages of being challenging to apply to expanding landslide samples and the low accuracy of a subjective random selection of non-landslide samples. Taking Fu’an City, Fujian Province, as an exa...
The increasing frequency and magnitude of landslides underscore the growing importance of landslide prediction in light of factors like climate change. Traditional methods, including physics-based methods and empirical methods, are beset by high cost...
9 January 2023
There are many frequent landslide areas in China, which badly affect local people. Since the 1980s, there have been more than 200 landslides in China with a death toll of 30 or more people at a time, economic losses of more than CNY 10 million or sig...
Rainfall and reservoir water level are commonly regarded as the two major influencing factors for reservoir landslides and are employed for landslide displacement prediction, yet their daily data are readily available with current monitoring technolo...
Landslide displacement prediction is of great significance for the prevention and early warning of slope hazards. In order to enhance the extraction of landslide historical monitoring signals, a landslide displacement prediction method is proposed ba...
The deformation process of landslide displacement has complex nonlinear characteristics. In view of the problems of large error, slow convergence and poor stability of the traditional neural network prediction model, in order to better realize the ac...
Climate change has recently increased the frequency of landslides in alpine areas. Susceptibility mapping is crucial for anticipating and assessing landslide risk. However, traditional methods focus on static environmental variables to emphasize the...
A landslide is a type of natural disaster that has the highest frequency, the widest distribution and the heaviest losses worldwide; landslides seriously threaten human life and property and major engineering facilities. Therefore, it is important to...
The volatility of the cumulative displacement of landslides is related to the influence of external factors. To improve the prediction of nonlinear changes in landslide displacement caused by external influences, a new combined forecasting model of l...
Early warning systems depend heavily on the accuracy of landslide displacement forecasts. This study focuses on the Bazimen landslide located in the Three Gorges Reservoir region and proposes a hybrid prediction approach combining support vector regr...
To address the issues of the insufficient accuracy and weak generalization capabilities of single models in landslide displacement prediction, this paper proposes a machine learning model fusion prediction method for landslide displacement based on s...
Landslide displacement monitoring can directly reflect the deformation process of a landslide. Predicting landslide displacements using monitored time series data through deep learning is a useful method for landslide early warning. Currently, existi...
Computational models enable accurate, timely prediction of landslides based on the monitoring data on-site as the development of artificial intelligence technology. The most existing prediction methods focus on finding a single prediction algorithm w...
Landslides have been identified as one of the costliest and deadliest natural disasters, causing tremendous damage to humans and societies. Information regarding the spatial extent of landslides is thus important to allow officials to devise successf...
Landslide prediction is one of the complicated topics recognized by the global scientific community. The research on landslide susceptibility prediction is vitally important to mitigate and prevent landslide disasters. The instability and complexity...
Landslides are among the most common geological hazards that result in considerable human and economic losses globally. Researchers have put great efforts into addressing the landslide prediction problem for decades. Previous methods either focus on...
The precise extraction of displacement time series for complex landslides poses significant challenges, and conventional landslide prediction models often overlook the deformation impacts of displacement triggers. To address this, we introduce a nove...
Landslides are affected not only by their own environmental factors, but also by the neighborhood environmental factors and the landslide clustering effect, which are represented as the neighborhood characteristics of modelling spatial datasets in la...
Landslides are a typical geological disaster, and are a great challenge to land use management. However, the traditional landslide displacement model has the defect of ignoring random displacement. In order to solve this situation, this paper propose...
The Zigui Basin, located in the Three Gorges Reservoir Area, has developed numerous landslides due to its interlayering of sandstone and mudstone, geological structure, and reservoir operations. This study identifies a fourth type of landslide failur...
Landslide susceptibility prediction (LSP) is the basis for risk management and plays an important role in social sustainability. However, the modeling process of LSP is constrained by various factors. This paper approaches the effect of landslide dat...
Climate change can exacerbate the occurrence of extreme precipitation events, thereby affecting both the frequency and intensity of rainfall-induced landslides. It is important to study the threat of rainfall-induced landslides under future climate c...
In landslide disaster warning, a variety of monitoring and warning methods are commonly adopted. However, most monitoring and warning methods cannot provide information in advance, and serious losses are often caused when landslides occur. To advance...
This contribution exposes the relative uncertainties associated with prediction patterns of landslide susceptibility. The patterns are based on relationships between direct and indirect spatial evidence of landslide occurrences. In a spatial database...
In landslide displacement prediction, random factors that would affect the performance of prediction are usually ignored by using a time series analysis method. In order to solve this problem, in this paper, a landslide displacement prediction model,...
12 February 2022
In recent years, machine learning models facilitated notable performance improvement in landslide displacement prediction. However, most existing prediction models which ignore landslide data at each time can provide a different value and meaning. To...
In order to promptly evacuate personnel and property near the foot of the landslide and take emergency treatment measures in case of sudden danger, it is very necessary to select suitable forecasting methods for conduct short-term displacement predic...
Rainfall-triggered landslides generally pose a high risk due to their sudden initiation, massive impact force, and energy. It is, therefore, necessary to perform accurate and timely hazard prediction for these landslides. Most studies have focused on...
Considering the great time and labor consumption involved in conventional hazard assessment methods in compiling landslide inventory, the construction of a transferable landslide susceptibility prediction model is crucial. This study employs UAV imag...
Spatial susceptible landslide prediction is the one of the most challenging research areas which essentially concerns the safety of inhabitants. The novel geographic information web (GIW) application is proposed for dynamically predicting landslide r...
The evolution of landslides is influenced by the complex interplay of internal geological factors and external triggering factors, resulting in nonlinear dynamic changes. Although deep learning methods have demonstrated advantages in predicting multi...
Landslide disasters pose severe threats to mountainous regions, where accurate monitoring and scientific prediction are crucial for early warning and risk mitigation. This study addresses this challenge by focusing on the Outang Landslide, a represen...
Landslide displacement prediction is an important part of monitoring and early warning systems. Effective displacement prediction is instrumental in reducing the risk of landslide disasters. This paper proposes a displacement prediction model based o...
Soil erosion (SE) provides slide mass sources for landslide formation, and reflects long-term rainfall erosion destruction of landslides. Therefore, it is possible to obtain more reliable landslide susceptibility prediction results by introducing SE...
3 July 2020
Data-driven models have been extensively employed in landslide displacement prediction. However, predictive uncertainty, which consists of input uncertainty, parameter uncertainty, and model uncertainty, is usually disregarded in deterministic data-d...
Accurate prediction of reservoir landslide displacements is crucial for early warning and hazard prevention. Current machine learning (ML) paradigms for predicting landslide displacement demonstrate superior performance, while often relying on variou...
Accurate prediction of landslide susceptibility is a key component of disaster risk reduction and early warning systems. Traditional landslide susceptibility prediction methods often face challenges in capturing complex nonlinear and spatio-temporal...
The prediction of landslide displacement is a challenging and essential task. It is thus very important to choose a suitable displacement prediction model. This paper develops a novel Attention Mechanism with Long Short Time Memory Neural Network (AM...
Landslide susceptibility evaluation (LSE) refers to the probability of landslide occurrence in a region under a specific geological environment and trigger conditions, which is crucial to preventing and controlling landslide risk. The mainstream of t...
Landslides are serious and complex geological and natural disasters that threaten the safety of people’s health and wealth worldwide. To face this challenge, a landslide displacement prediction model based on time series analysis and modified l...
Displacement predictions are essential to landslide early warning systems establishment. Most existing prediction methods are focused on finding an individual model that provides a better result. However, the limitation of generalization that is inhe...
In order to establish an effective early warning system for landslide disasters, accurate landslide displacement prediction is the core. In this paper, a typical step-wise-characterized landslide (Caojiatuo landslide) in the Three Gorges Reservoir (T...
Influenced by autochthonous geological conditions and external environmental changes, the evolution of landslides is mostly nonlinear. This article proposes a combined neural network prediction model that combines a temporal convolutional neural netw...
10 June 2020
Landslides fall under natural, unpredictable and most distractive disasters. Hence, early warning systems of such disasters can alert people and save lives. Some of the recent early warning models make use of Internet of Things to monitor the environ...
This paper systematically reviews remote sensing technology and learning algorithms in exploring landslides. The work is categorized into four key components: (1) literature search characteristics, (2) geographical distribution and research publicati...
The monitoring and prediction of the landslide groundwater level is a crucial part of landslide early warning systems. In this study, Tangjiao landslide in the Three Gorges Reservoir area (TGRA) in China was taken as a case study. Three groundwater l...
Landslides affecting soil layers up to 1–2 m deep pose a significant hazard in mountainous and hilly regions, particularly in the Mediterranean, where intense precipitation is increasing. Identifying landslide-prone areas is crucial for risk as...
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