To distinguish debris flow infrasound from other infrasound sources, previous works have used one-dimensional infrasound shapes and parameters. In this study, we converted infrasound signals into two-dimensional signal time–frequency graphs and created a time–frequency graph dataset containing five common kinds of infrasound. We used deep learning to distinguish debris flow infrasound from other infrasound and improved the deep learning model to enhance the accuracy of debris flow infrasound identification. By improving the LeNet-5 network, we obtained an infrasound signal recognition method for debris flows based on deep learning. After signal preprocessing and model training, this method was able to differentiate target infrasound from environmental infrasound, and a debris flow infrasound recognition accuracy of 84.1% was achieved. The method described in this paper can effectively recognize debris flow infrasound and distinguish it from other environmental infrasound. By such means, more accurate and more timely debris flow disaster warnings may be obtained.

Accurate landslide detection plays an important role in land planning, disaster prediction and disaster relief. At present, field investigation and exploration based on professional personnel is the most widely used landslide mapping and detection technology, but this method consumes a lot of manpower and material resources and is inefficient. With the development of artificial intelligence, landslide identification and target detection based on deep learning have attracted more and more attention due to their remarkable advantages over traditional technologies. It is a technical problem to identify landslides from satellite remote sensing images. Although there are some methods at present, there is still room for improvement in the target detection algorithm of landslides against the background of the diversity and complexity of landslides. In this paper, target detection algorithm models such as Faster R-CNN apply to landslide recognition and detection tasks, and various commonly used recognition and detection algorithm network structures are used as the basic models for landslide recognition. Efficient residual channel soft thresholding attention mechanism algorithm (ERCA) is proposed, which intends to reduce the background noise of images in complex environments by means of deep learning adaptive soft thresholding to improve the feature learning capability of deep learning target detection algorithms. ERCA is added to the backbone network of the target detection algorithm for basic feature extraction to enhance the feature extraction and expression capability of the network. During the experiment ERCA combined with ResNet50, ResNet101 and other backbone networks, the objective indicators of detection results such as AP50 (Average Precision at IOU=0.50), AP75 (Average Precision at IOU=0.75) and AP (Average Precision) were improved, and the AP values were all improved to about 4%, and the final detection results using ResNet101 combined with ERCA as the backbone network reached 76.4% AP value. ERCA and other advanced channel attention networks such as ECA (Efficient Channel Attention for Deep Convolutional Neural Networks) and SENet (Squeeze-and-Excitation Networks) are fused into the backbone network of the target detection algorithm and experimented on the landslide identification detection task, and the detection results are that the objective detection indexes AP50, AP75, AP, etc. are higher for ERCA compared with other channel attention, and the subjective detection image detection effect and feature map visualization display are also better. We released our code at: https://github.com/fluoritess/Efficient-residual-channel-attention-mechanism-network-and-Faster-R-CNN.

Urban expansion caused numerous geological hazards, particularly in the mountainous areas. In this article, the unsaturated soil mechanics combined with a numerical method is applied to build the rainfall thresholds for the Xiangmi Lake slope in early warning. The drainage mechanism in soil slope under different rainfall conditions has been studied. Then, the actual rainfall data collected in the rainy season are taken as the rainfall condition to simulate the actual rainfall situation. The corresponding safety factor of the slope under the rainfall has been calculated and compared with the improved deformation tangential angle criterion for the slope safety prediction. The tangent angle can be used as the judgment basis to classify the early warning levels of a rainstorm, which helps to establish the early warning model of slope rainfall and guide the landslide early warning.

As frequent and highly destructive geologic hazards, debris flows necessitate effective monitoring alongside rapid and accurate detection to support disaster prevention and mitigate losses of life and property. Current detection technologies, however, are often limited by high false alarm rates, insufficient accuracy, considerable model complexity that complicates deployment, and the elevated costs of contact-based detection. To overcome these limitations, this paper introduces a lightweight real-time debris flow detection model based on the Roboflow Detection Transformer (RF-DETR). First, we constructed a dataset of realistic debris flow scenarios including debris flow disaster events worldwide. Based on this, the lightweight vision transformer model EfficientFormerV2 is adopted as the backbone. By employing a dimension-consistent architecture, the model avoids the frequent switching between 4D and 3D features found in traditional Vision Transformers (ViTs), thereby reducing a significant number of inefficient operations. Additionally, we optimized the multiscale projection layer by removing downsampling and feature aggregation operations, which reduces redundant computations and improves feature extraction efficiency. Furthermore, the introduction of the Efficient Intersection over Union (EIoU) loss function achieves faster convergence and improved detection accuracy for debris flows. Ablation studies performed on our debris flow dataset demonstrate that the improved model reduces parameters by 54.2% and computational load by 36.4% while ensuring acceptable losses in detection accuracy and latency. This significant reduction in model size and complexity achieves effective lightweighting, fulfilling the requirements for deployment on edge devices and enabling real-time debris flow detection.