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Landslides are one of the most widespread disasters and threaten people’s lives and properties in many areas worldwide. Landslide susceptibility mapping (LSM) plays a crucial role in the evaluation and extenuation of risk. To date, a large number of machine learning approaches have been applied to LSM. Of late, a high-level convolutional neural network (CNN) has been applied with the intention of raising the forecast precision of LSM. The primary contribution of the research was to present a model which was based on the CNN for LSM and methodically compare its capability with the traditional machine learning approaches, namely, support vector machine (SVM), logistic regression (LR), and random forest (RF). Subsequently, we used this model in the Wenchuan region, where a catastrophic earthquake happened on 12 May 2008 in China. There were 405 valuable landslides in the landslide inventory, which were divided into a training set (283 landslides) and validation set (122 landslides). Furthermore, 11 landslide causative factors were selected as the model’s input, and each model’s output was reclassified into five intervals according to the sensitivity. We also evaluated the model’s performance by the receiver operating characteristic (ROC) curve and several statistical metrics, such as precision, recall, F1-score, and other measures. The results indicated that the CNN-based methods achieved the best performance, with the success-rate curve (SRC) and prediction-rate curve (PRC) approaches reaching 93.14% and 91.81%, respectively. The current research indicated that the approach based on the CNN for LSM had both outstanding goodness-of-fit and excellent prediction capability. Generally, the LSM in our research is capable of advancing the ability to assess landslide susceptibility.

Abstract

With the extensive application of optical parts in many high-tech fields such as high-power laser, space optics, and aerospace, the requirements for the surface quality of optical parts are also increasing, which requires not only high surface qualities but also low defects including low subsurface damage and strict wavefront errors. As an essential link in the precision and ultraprecision optical manufacturing, various surface polishing methods and techniques have always attracted researchers’ continuous study and exploration. Considering the development of optical part surface polishing technology in recent years, this study analyzes the principle and development process of typical processing methods represented by each kind of polishing technology, expounds the specific research progress of optical part surface polishing technology, including the iterative renewal of traditional technologies and the research development of new technologies, and gives examples for typical applications. Finally, the development trend of optical part surface polishing technology is prospected, which provides a reference for follow-up intensive research in optical manufacturing.