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The risk analysis and assessment of snow disasters are essential foundational tasks in natural disaster management and profoundly impact the scientific and precise formulation of disaster prevention, preparedness, and mitigation strategies. Employing the theory and methodology of snow disaster assessment, this research focuses on historical and potential snow disasters in the Qinghai–Tibetan Plateau (QTP) Region. Utilizing a long-time-series snow depth remote sensing dataset, we extracted six assessment indicators for historical snow disaster risk factors and potential snow disaster risk factors. We determined the weights of these six assessment indicators using the entropy weight method. Subsequently, we established a snow disaster assessment model to evaluate the grade distribution of snow disasters in the study area. This method can effectively solve the problem of the sparse data distribution of meteorological stations and reflect degrees of snow disaster risk on a large spatial scale. The findings reveal that areas with a relatively high snow disaster risk are primarily concentrated in the western part of the Ali Region, the central part of Chamdo, and near the border in Southern Xizang. Additionally, regions with a high frequency of snow disasters are predominantly located at the junction of Nagchu, Chamdo, and Nyingchi in the eastern part of Xizang. These results contribute valuable insights into the risk assessment of snow disasters and facilitate the development of effective strategies for disaster management in the region. [ABSTRACT FROM AUTHOR]

A typhoon disaster is a common meteorological disaster that seriously impacts natural ecology, social economy, and even human sustainable development. It is crucial to access the typhoon disaster information, and the corresponding disaster prevention and reduction strategies. However, traditional question and answering (Q&A) methods exhibit shortcomings like low information retrieval efficiency and poor interactivity. This makes it difficult to satisfy users' demands for obtaining accurate information. Consequently, this work proposes a typhoon disaster knowledge Q&A approach based on LLM (T5). This method integrates two technical paradigms of domain fine-tuning and retrieval-augmented generation (RAG) to optimize user interaction experience and improve the precision of disaster information retrieval. The process specifically includes the following steps. First, this study selects information about typhoon disasters from open-source databases, such as Baidu Encyclopedia and Wikipedia. Utilizing techniques such as slicing and masked language modeling, we generate a training set and 2204 Q&A pairs specifically focused on typhoon disaster knowledge. Second, we continuously pretrain the T5 model using the training set. This process involves encoding typhoon knowledge as parameters in the neural network's weights and fine-tuning the pretrained model with Q&A pairs to adapt the T5 model for downstream Q&A tasks. Third, when responding to user queries, we retrieve passages from external knowledge bases semantically similar to the queries to enhance the prompts. This action further improves the response quality of the fine-tuned model. Finally, we evaluate the constructed typhoon agent (Typhoon-T5) using different similarity-matching approaches. Furthermore, the method proposed in this work lays the foundation for the cross-integration of large language models with disaster information. It is expected to promote the further development of GeoAI. [ABSTRACT FROM AUTHOR]

Chongming Island and its surrounding areas are highly significant coastal regions in China. However, the regions undergo continuous changes owing to various factors, such as the sedimentation from the Yangtze River, human activities, and tidal movements. Chongming Island is part of the Yangtze River Delta, which is one of the most economically developed regions in China. Studying the water body changes in this area is of great importance for decision making in water resource conservation, coastal resource management, and ecological environmental protection. In this study, we propose an improved ISUNet model by enhancing the skip-connection operations in the traditional UNet architecture. We extracted and analyzed the water bodies in Chongming Island and its surrounding areas from 2013 to 2022, providing a detailed spatiotemporal analysis of the water body area over the years. The results indicate that the water body area in the study area has decreased by 267.8 km2 over the past decade, showing a gradually fluctuating downward trend with an average annual reduction of nearly 27 km2. The analysis suggests that the reduction in the water body area is mainly attributed to sedimentation near river channels and ports, the formation of sandbars owing to channel erosion, and the artificial construction of ports and coastal areas. The influencing factors include human activities and sedimentation from the Yangtze River Estuary. Specifically, human activities such as land reclamation, port construction, and aquaculture play a major role in causing changes in the area. [ABSTRACT FROM AUTHOR]

Temperature is an important part of meteorological factors, which are affected by local and surrounding meteorological factors. Aiming at the problems of significant prediction error and insufficient extraction of spatial features in current temperature prediction research, this research proposes a temperature prediction model based on the Graph Convolutional Network (GCN) and Bidirectional Long Short-Term Memory (BiLSTM) and studies the influence of temperature time-series characteristics, urban spatial location, and other meteorological factors on temperature change in the study area. In this research, multi-meteorological influencing factors and temperature time-series characteristics are used instead of single time-series temperature as influencing factors to improve the time dimension of the input data through time-sliding windows. Meanwhile, considering the influence of meteorological factors in the surrounding area on the temperature change in the study area, we use GCN to extract the urban geospatial location features. The experimental results demonstrate that our model outperforms other models and has the smallest root mean squared error (RMSE) and mean absolute error (MAE) in the following 14-day and multi-region temperature forecasts. It has higher accuracy in areas with stable temperature fluctuations and small temperature differences than in baseline models. [ABSTRACT FROM AUTHOR]

Indoor space is a core part of supporting indoor applications. Most of the existing indoor space models are expressed from three space scales: building, floor, and room, and the granularity is not fine enough, lacking the expression of each functional subspace inside the room. In this study, we first analyzed the spatio-temporal segmentation characteristics of indoor space, and proposed a multi-level indoor space model framework that takes into account multiple types of segmentation. As well, based on the IFC (Industry Foundation Classes) standard, the extension of the indoor functional subspace was realized. The experimental results showed that the indoor space model proposed in this paper can effectively support the expression of functional subspace under the multi-type segmentation based on indoor elements, especially from the aspects of semantics, geometry, relationship, and attribute. This study enriches the granularity of existing indoor models and provides support for refined indoor navigation and evacuation applications. [ABSTRACT FROM AUTHOR]