A comprehensive review of machine learning‐based methods in landslide susceptibility mapping

Abstract

Landslide susceptibility mapping (LSM) has been widely used as an important reference for development and construction planning to mitigate the potential social‐eco impact caused by landslides. Originally, most of those maps were generated by the judgements of experts, which is time‐consuming and laborious, and whose accuracy is difficult to be quantified because of the subjective effects. With the development of machine learning algorithms and the methods of data collection, big data and artificial intelligence have now been popularized in this field, significantly improving mapping accuracy and efficiency. Various machine learning‐based methods, mainly including conventional machine learning, deep learning, and transfer learning have been applied and compared in LSM in different areas by previous researchers. Nevertheless, none of them can be effective in all cases. Although deep learning‐based methods were proven more accurate than conventional machine learning‐based methods in most data‐rich situations, the latter is sometimes more popularly used in LSM, as there is not that much data in this field to train a deep learning network perfectly. In a more rigorous situation when there is very limited data, transfer learning‐based approaches are applied by several researchers, which have contributed to improve the workability and the accuracy of LSM in data‐limited areas. Such technical explosion has promoted the application of landslide susceptibility maps, thus contributing to mitigating the social‐eco impact associated with landslides. This paper comprehensively reviews the whole process of generating landslide susceptibility maps based on machine learning methods, introduces and compares the commonly used machine learning methods, and discusses the topics for future research.

Development and application of a more refined process for extracting rock crack width information based on artificial intelligence

“…Although the Transformer-based SegFormer network shows superior performance over the CNNbased network in several datasets [20,21,22], the CNN-based network still shows excellent performance for the crack image data in this study. In addition, the SS network can achieve more accurate segmentation in the dataset reconstructed based on RCAN, and the overall structure and detail aspects of the detected cracks best match the labeled data.…”

“…With the rapid development of CV technology, some novel algorithms such as FCN, Deeplab V3+ and FraSegNet are gradually being applied to the recognition and parsing of various research objects and have shown better performance in feature extraction [14,15,16,17,18,19]. The feature extracted by the latest Transformer-based algorithms such as SETR, TransUNet and SegFormer have richer global contextual information, which overcomes the drawback that convolutional neural network-based (CNN) algorithms cannot directly extract long-range information and eventually show excellent performance in semantic segmentation tasks [20,21,22]. However, in the case of regional rock crack images, the selection of hyperparameters in the feature extraction process is highly dependent on the image resolution and specific scene features.…”

Reliability evaluation of reservoir bank slopes with weak interlayers considering spatial variability

“…The stability of coastal banks is affected by the changing water levels, rainfall, and earthquakes (Sassa and Takagawa, 2019;Liu et al, 2023). The reliability evaluation method of reservoir banks under various disaster-causing factors in this paper can be applied to predict the long-term safety of the coastal banks (Cavanaugh et al, 2019).…”

Stability Evaluation of Sliding-Type Perilous Rock in Huangzangsi Hydrojunction Project Based on Natural Vibration Frequency

“…Du et al and Jia et al further conducted experimental research on dumping perilous rock and found that the natural vibration frequency is closely related to the stability [30][31][32][33]. However, the above research has just started and is in the experimental stage, and the theory has not yet been perfected [34,35]. And the above test found that the dynamic characteristic parameters of the perilous rock are closely related to the stability [36].…”