This research proposes an innovative solution to the inherent challenges faced by landslide displacement prediction models based on data-driven methods, such as the need for extensive historical datasets for training, the reliance on manual feature selection, and the difficulty in effectively utilizing landslide historical data. We have developed a dual-channel deep learning prediction model that integrates multimodal decomposition and an attention mechanism to overcome these challenges and improve prediction performance. The proposed methodology follows a three-stage framework: (1) Empirical Mode Decomposition (EMD) effectively segregates cumulative displacement and feature factors; (2) We have developed a Double Exponential Smoothing (DES) ensemble optimized through a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to enhance trend prediction; while employing a Bidirectional Long Short-Term Memory-Radial Basis Function (BiLSTM-RBF) network enhanced by a hybrid attention mechanism, which facilitates a global-local synergistic approach to hierarchical feature extraction, thereby improving the prediction of periodic displacements; (3) A bidirectional adaptive feature extraction mechanism aligns attention weights with BiLSTM propagation paths through spatial mapping, complemented by an innovative loss function incorporating Prediction Interval (PI) width optimization. In the comparative experiments of the Baishuihe landslide: the RMSE, MAE, and R² indexes of monitoring point ZG118 are improved by 19.8%, 35.2%, and 3.2% compared with the optimal baseline model (RBF-MIC); in the monitoring point ZG93, where the amount of data is less, the three indexes are even more improved by 52.1%, 32.3%, and 21.8% compared with the optimal baseline model (GRU-None). These results substantiate the model’s capacity to overcome dual constraints of data paucity and feature engineering limitations in geohazard prediction.

Brain tumors are neoplastic diseases caused by the proliferation of abnormal cells in brain tissues, and their appearance may lead to a series of complex symptoms. However, current methods struggle to capture deeper brain tumor image feature information due to the variations in brain tumor morphology, size, and complex background, resulting in low detection accuracy, high rate of misdiagnosis and underdiagnosis, and challenges in meeting clinical needs. Therefore, this paper proposes the CMS-YOLO network model for multi-category brain tumor detection, which is based on the You Only Look Once version 10 (YOLOv10s) algorithm. This model innovatively integrates the Convolutional Medical UNet extended block (CMUNeXt Block) to design a brand-new CSP Bottleneck with 2 convolutions (C2f) structure, which significantly enhances the ability to extract features of the lesion area. Meanwhile, to address the challenge of complex backgrounds in brain tumor detection, a Multi-Scale Attention Aggregation (MSAA) module is introduced. The module integrates features of lesions at different scales, enabling the model to effectively capture multi-scale contextual information and enhance detection accuracy in complex scenarios. Finally, during the model training process, the Shape-IoU loss function is employed to replace the Complete-IoU (CIoU) loss function for optimizing bounding box regression. This ensures that the predicted bounding boxes generated by the model closely match the actual tumor contours, thereby further enhancing the detection precision. The experimental results show that the improved method achieves 94.80% precision, 93.60% recall, 96.20% $mAP50$ score, and 79.60% $mAP50\text{-}95$ on the MRI for Brain Tumor with Bounding Boxes dataset. Compared to the YOLOv10s model, this represents improvements of 1.0%, 1.1%, 1.0%, and 1.1%, respectively. The method can achieve automatic detection and localization of three distinct categories of brain tumors—glioma, meningioma, and pituitary tumor, which can accurately detect and identify brain tumors, assist doctors in early diagnosis, and promote the development of early treatment.