Evaluation of deep learning algorithms for landslide susceptibility mapping in an alpine-gorge area:a case study in Jiuzhaigou County

With its high mountains,deep valleys,and complex geological formations,the Jiuzhaigou County has the typical characteristics of a disaster-prone mountainous region in southwestern China.On August 8,2017,a strong Ms 7.0 earthquake occurred in this region,causing some of the mountains in the area to become loose and cracked.Therefore,a survey and evaluation of landslides in this area can help to reveal hazards and take effective measures for subsequent disaster management.However,different evaluation models can yield different spatial distributions of landslide susceptibility,and thus,selecting the appropriate model and performing the optimal combination of parameters is the most effective way to improve susceptibility evaluation.In order to construct an evaluation indicator system suitable for Jiuzhaigou County,we extracted 12 factors affecting the occurrence of landslides,including slope,elevation and slope surface,and made samples.At the core of the transformer model is a self-attentive mechanism that enables any two of the features to be interlinked,after which feature extraction is performed via a forward propagation network(FFN).We exploited its coding structure to transform it into a deep learning model that is more suitable for landslide susceptibility evaluation.The results show that the transformer model has the highest accuracy(86.89%),followed by the random forest and support vector machine models(84.47%and 82.52%,respectively),and the logistic regression model achieves the lowest accuracy(79.61%).Accordingly,this deep learning model provides a new tool to achieve more accurate zonation of landslide susceptibility in Jiuzhaigou County.

Letters and Visits System Optimization and Implementation

The letters and visits system plays a vital role in government work, serving as a crucial tool for supervising law enforcement and administrative conduct, ensuring public officials’ integrity, and promoting governance by law. As Chinese citizens’ political awareness grows, the volume of letters and visits has increased steadily. This paper reviews the current state of letters and visits information construction, identifies challenges and problems in system integration, presents integration ideas for existing systems, and proposes an innovative approach to letters and visits system integration. This research aims to provide valuable insights and guidance for other units undertaking similar system integration efforts.

A real 3D scene rendering optimization method based on region of interest and viewing frustum prediction in virtual reality

As an important technology of digital construction,real 3D models can improve the immersion and realism of virtual reality(VR)scenes.The large amount of data for real 3D scenes requires more effective rendering methods,but the current rendering optimization methods have some defects and cannot render real 3D scenes in virtual reality.In this study,the location of the viewing frustum is predicted by a Kalman filter,and eye-tracking equipment is used to recognize the region of interest(ROI)in the scene.Finally,the real 3D model of interest in the predicted frustum is rendered first.The experimental results show that the method of this study can predict the frustrum location approximately 200 ms in advance,the prediction accuracy is approximately 87%,the scene rendering efficiency is improved by 8.3%,and the motion sickness is reduced by approximately 54.5%.These studies help promote the use of real 3D models in virtual reality and ROI recognition methods.In future work,we will further improve the prediction accuracy of viewing frustums in virtual reality and the application of eye tracking in virtual geographic scenes.

Integrated data-model-knowledge representation for natural resource entities

The unified management and planning of national or provincial natural resources distributed both aboveground and underground have become increasingly important.Accurate depictions of natural resource elements and their interactions are key to achieving integrated and systematic management of natural resources.However,current spatiotemporal data models are based only on data descriptions,attribute records,and other model knowledge of a more general basis,without intuitively describing relationships between these elements and natural resources.This paper,therefore,proposes an integrated data-model-knowledge representation model to explicitly describe the time,space,and interaction of natural resource entities through an integrated knowledge graph.First,this study constructs a conceptual model using the aspects of semantics,scale,and data-model-knowledge,thereby explicitly describing the relationships of natural resources.Second,a logical model of natural resource representation is proposed,that is integrated with time,space,attributes,and relationships.Finally,taking the management of water resources as an example,this paper realizes the meticulous presentation of the levels of detail and rich semantic relations of natural resource entities.The findings of this study lay the foundation for a more efficient,precise,and lucid perception of the distribution laws and complicated interactional relationships of natural resources,both aboveground and underground.