AIMC Topic: Landslides

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Expedite Quantification of Landslides Using Wireless Sensors and Artificial Intelligence for Data Controlling Practices.

Computational intelligence and neuroscience
The power of wireless network sensor technologies has enabled the development of large-scale in-house monitoring systems. The sensor may play a big part in landslide forecasting where the sensor linked to the WLAN protocol can usefully map, detect, a...

A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran.

Sensors (Basel, Switzerland)
We mapped landslide susceptibility in Kamyaran city of Kurdistan Province, Iran, using a robust deep-learning (DP) model based on a combination of extreme learning machine (ELM), deep belief network (DBN), back propagation (BP), and genetic algorithm...

The use of surrounding rock loosening circle theory combined with elastic-plastic mechanics calculation method and depth learning in roadway support.

PloS one
The objective is to study the design method of roadway support and provide technical support for coal mining and other mining methods that need deep roadway excavation. Through literature review, the occurrence, development mechanism and influencing ...

Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment.

International journal of environmental research and public health
We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands, Malaysia. The models were trained with a database of 152 landslides compiled u...

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms.

International journal of environmental research and public health
Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susce...

Attribute selection using correlations and principal components for artificial neural networks employment for landslide susceptibility assessment.

Environmental monitoring and assessment
Landslide susceptibility maps can be developed with artificial neural networks (ANNs). In order to train our ANNs, a digital elevation model (DEM) and a scar map of one previous event were used. Eleven attributes are generated, possibly containing re...

Multistage fuzzy comprehensive evaluation of landslide hazards based on a cloud model.

PloS one
To accurately study the risk assessment of landslide disasters, firstly, the environmental conditions of induced landslide disasters are regarded as a fuzzy system, and the landslide risk factors in the multi-level analysis system are constructed to ...

Mine landslide susceptibility assessment using IVM, ANN and SVM models considering the contribution of affecting factors.

PloS one
The fragile ecological environment near mines provide advantageous conditions for the development of landslides. Mine landslide susceptibility mapping is of great importance for mine geo-environment control and restoration planning. In this paper, a ...

Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China.

International journal of environmental research and public health
The main goal of this study was to use the synthetic minority oversampling technique (SMOTE) to expand the quantity of landslide samples for machine learning methods (i.e., support vector machine (SVM), logistic regression (LR), artificial neural netw...

An Ensemble Approach for Cognitive Fault Detection and Isolation in Sensor Networks.

International journal of neural systems
Cognitive fault detection and diagnosis systems are systems able to provide timely information about possibly occurring faults without requiring any a priori knowledge about the process generating the data or the possible faults. This ability is cruc...