Modeling and assessing the susceptibility of snowmelt floods is critical for flood hazard management. However, the current research on snowmelt flood susceptibility lacks a valid large-scale modeling approach. In this study, a novel high-performance deep learning model called Swin Transformer was used to assess snowmelt susceptibility in the Kunlun Mountains region, where snowmelt floods occur frequently. Support vector machine (SVM), random forest (RF), deep neural network (DNN) and convolutional neural network (CNN) were also involved in the performance comparison. Eighteen potential conditioning factors were combined with a historical flood inventory to form the database. Apart from the susceptibility assessment, sensitivity analysis was also conducted to reflect the impact of the conditioning factors on the susceptibility of different types of snowmelt floods. The results showed that Swin Transformer achieved the highest score in the model performance test (AUC = 0.99) and successfully identified the relationship between conditioning factors and snowmelt flooding. Elevation and distance to rivers are the most important factors that affect snowmelt flooding in the study region, whereas rainfall and snow water equivalent are the dominant natural factors for mixed and warming types. In addition, the north-central parts of the study area have high susceptibility to snowmelt flooding. The methods and results can provide scientific support for snowmelt flood modeling and disaster management.

Soil salinization is a global ecological and environmental problem, which is particularly serious in arid areas. The formation process of soil salinity is complex, and the interactive effects of natural causes and anthropogenic activities on soil salinization are elusive. Therefore, we propose an automated machine learning framework for predicting soil salt content (SSC), which can search for the optimal model without human intervention. At the same time, post hoc interpretation methods and graph theory knowledge are introduced to visualize the nonlinear interactions of variables related to SSC. The proposed method shows robust and adaptive performance in two typical arid regions (Central Asia and Xinjiang Province in western China) under different environmental conditions. The optimal algorithms for the Central Asia and Xinjiang regions are Extremely Randomized Trees (ET) and eXtreme Gradient Boosting (XGBoost), respectively. Moreover, precipitation and minimum air temperature are important feature variables for salt-affected soils in Central Asia and Xinjiang, and their strongest interaction effects are latitude and normalized difference water index. In both study areas, meteorological factors exhibit the greatest effect on SSC, and demonstrate strong spatiotemporal interactions. Soil salinization intensifies with long-term climate warming. Regions with severe SSC variation are mainly distributed around the irrigation water source and in low-terrain basins. From 1950 to 2100, the regional mean SSC (g/kg) varies by +20.94% and +64.76% under extreme scenarios in Central Asia and Xinjiang, respectively. In conclusion, our study provides a novel automated approach for interaction analysis of driving factors on soil salinization in drylands.

Uncertain Knowledge Graph Reasoning (UKGR) aims to infer missing facts and their confidence levels from incomplete and uncertain knowledge graphs. However, existing methods face dual challenges: traditional methods struggle to model uncertain information, while existing large language model (LLM)-based approaches fail to effectively utilize the critical uncertainty-structured information of knowledge graphs during reasoning. To address this core issue, we propose a Topology-Driven Prefix Adapter (TDPA), which activates the reasoning potential of LLMs by deeply integrating confidence topology features of uncertain knowledge graphs. In implementation, TDPA uses graph embedding to comprehend the intricate entities and relations within KGs, representing them as structural embeddings. Then TDPA communicates such structural information understanding to the LLMs through a knowledge prefix adapter which projects the structural embeddings into the textual space and obtains virtual knowledge tokens positioned as a prefix of the input prompt. Experiments on CN15K, NL27K, and PPI5K datasets demonstrate that our method outperforms traditional probabilistic graph models and existing LLM-based approaches on MSE and MAE metrics, confirming that effectively modeling uncertainty topology structures is key to enhancing the reasoning capabilities of LLMs.

Vision-language models (VLMs) pre-trained on natural image and language data, such as CLIP, have exhibited significant potential in few-shot image recognition tasks, leading to development of various efficient transfer learning methods. These methods exploit inherent pre-learned knowledge in VLMs and have achieved strong performance on standard image datasets. However, their effectiveness is often limited when confronted with cross-domain tasks where imaging domains differ from natural images. To address this limitation, we propose Consistency-guided Multi-view Collaborative Optimization (CoMuCo), a novel fine-tuning strategy for VLMs. This strategy employs two functionally complementary expert modules to extract multi-view features, while incorporating prior knowledge-based consistency constraints and information geometry-based consensus mechanisms to enhance the robustness of feature learning. Additionally, a new cross-domain few-shot benchmark is established to help comprehensively evaluate methods on imaging domains distinct from natural images. Extensive empirical evaluations on both existing and newly proposed benchmarks suggest CoMuCo consistently outperforms current methods in few-shot tasks. The code and benchmark will be released.