Natural hazard named entity recognition is a technique used to recognize natural hazard entities from a large number of texts. The method of natural hazard named entity recognition can facilitate acquisition of natural hazards information and provide reference for natural hazard mitigation. The method of named entity recognition has many challenges, such as fast change, multiple types and various forms of named entities. This can introduce difficulties in research of natural hazard named entity recognition. To address the above problem, this paper constructed a natural disaster annotated corpus for training and evaluation model, and selected and compared several deep learning methods based on word vector features. A deep learning method for natural hazard named entity recognition can automatically mine text features and reduce the dependence on manual rules. This paper compares and analyzes the deep learning models from three aspects: pretraining, feature extraction and decoding. A natural hazard named entity recognition method based on deep learning is proposed, namely XLNet-BiLSTM-CRF model. Finally, the research hotspots of natural hazards papers in the past 10 years were obtained through this model. After training, the precision of the XLNet-BilSTM-CRF model is 92.80%, the recall rate is 91.74%, and the F1-score is 92.27%. The results show that this method, which is superior to other methods, can effectively recognize natural hazard named entities.

Based on geographic information system (GIS) technology in conjunction with two methods for assessing landslide susceptibility (LS)—namely, a method using experts’ knowledge and experience, and a mathematical/statistical method—the LS of southern Anhui, China is assessed using an analytic hierarchy process (AHP) via an AHP-weighted information content method. Landslide-affecting factors are categorized into three main types and 10 subtypes. The values of spatial characteristics of the landslide-affecting factors are obtained using GIS technology. The AHP method is then employed to compare the importance and weights of landslide-affecting factors. The information content method is used to convert the measured values of the landslide-affecting factors in the study area to data reflecting regional stability. The closeness of the relationships between the classification levels of each landslide-affecting factor and landslide occurrence are calculated. The LS of the study area is assessed using the proposed method. The LS assessment shows that high LS, relatively high LS, moderate LS, relatively low LS and low LS regions account for 21.3%, 20.6%, 20.1%, 11.7% and 26.3% of the study area, respectively. Finally, the accuracy of the LS assessment results is analyzed using two methods: the assessment, including an analysis of random landslide sites for the validating models; and the area below a receiver operating characteristic (ROC) curve of area under curve (AUC) value. The results show that the proportion of landslide sites in the regions of each LS level determined using the AHP-weighted information content method increases as the LS level increases, and that the accuracies of the AHP-weighted information content method were 8.1% and 5.7% higher than those of the AHP method and information content method, respectively.

The evaluation of landslide susceptibility is of great significance in the prevention and management of geological hazards. The accuracy of the landslide susceptibility prediction model based on machine learning is significantly higher than that of traditional expert knowledge and the conventional mathematical statistics model. The correct and reasonable selection of non-landslide samples in the machine learning model greatly improves the prediction accuracy and reliability of the regional landslide susceptibility model. Focusing on the problem of selecting non-landslide samples in the machine learning model for landslide susceptibility evaluation, this paper proposes a landslide susceptibility evaluation method based on the combination of an information model and machine learning in traditional mathematical statistics. First, the influence factors for landslide susceptibility evaluation are screened by the correlation analysis method. Second, the information value model is used to delimit areas with low and relatively low landslide susceptibility, and non-landslide points are randomly selected. Third, a landslide susceptibility evaluation method combined with IV-ML, such as logistic regression (IV-LR), random forest (IV-RF), support vector machine (IV-SVM), and artificial neural network (IV-ANN), is established. Finally, the landslide susceptibility factors in the Dabie Mountain area of Anhui Province are analyzed, and the accuracy of the landslide susceptibility evaluation results using the IV-LR, IV-RF, IV-SVM, and IV-ANN and LR, RF, SVM, and ANN methods are compared. The accuracy is evaluated by examining the ACC, AUC, and kappa values of the model. The results indicate that the evaluation effect of the IV-ML models (IV-LR, IV-RF, IV-SVM, IV-ANN) on landslide susceptibility is significantly higher than that of the ML models (LR, RF, SVM, ANN).

Faults represent significant geological structures. Conventional fault identification methods pri-marily rely on the linear features of faults, achieved through the interpretation of remote sensing imagery (RSI). To more accurately enhance the morphological features of faults and achieve their rapid, precise, and intelligent identification, this paper employs a multi-source information fusion method. By analyzing and processing RSI, digital elevation model, and geological map data, the spectral, topographic, geomorphic, and structural features of faults are extracted. By training samples and applying fusion algorithms, the spectral, topographic, geomorphic, and structural features are integrated to enhance the morphological features information of faults. Ultimately, intelligent fault identification is realized through deep learning-based image recognition technology. First, 16 influencing factors are selected from the perspectives of spectral, topographic, geomorphic, and structural features. Second, the importance of each influencing factor is predicted using 4 machine learning methods. Finally, fault identification is carried out on the fault identification map, which is fused with multi-source feature information, using the Convolutional Neural Network Model. The study applies the method to the southern part of Jinzhai County, Lu’an City. The results indicate that among the machine learning methods, the classification and regression Trees model achieved an accuracy of 0.993, true positive rate of 0.988, F1-score of 0.994. Topographic position index(TPI), Valley line (VL), Surface cutting depth (SCD), and RSI all show high importance across the four machine learning models, indicating their crucial role in fault identification. For the Convolutional Neural Network model-based method, the Validation Accuracy(Val_Accuracy) was 0.990, F1-score was 0.736, and Validation Loss(Val_Loss) was 0.025, suggesting that this method can accurately identify faults in the study area.

Taking the mausoleums in the main urban area of Nanjing city as the study object, with the support of GIS technology, this paper extracts eight influencing factors (surface roughness, elevation, surface cutting depth, surface peaks, outfall, river system, slope, and aspect) corresponding to geomantic elements (“Long”, “Sha”, “Shui”, and “Xue”) in the geographical environments of mausoleums. The AHP is applied in the correlation weight method to calculate the weights of the eight influencing factors, while the information content method is applied in the grading method to calculate the numerical index of each factor. The feng shui (geomantic) suitability of the mausoleums in the main urban area of Nanjing is evaluated using the AHP-weighted information content method and by combining expert knowledge and experience with mathematical statistical techniques. According to the feng shui (geomantic) suitability evaluation results obtained for the mausoleums in the study area, the relatively low-suitability area accounts for 14.55% of the entire study area, the low-suitability area accounts for 25.40%, the suitable area accounts for 29.13%, the relatively high-suitability area accounts for 22.00%, and the high-suitability area accounts for 8.92%. Finally, through a sensitivity analysis of the model, a verification analysis applied using random mausoleums and a feng shui (geomantic) analysis of the Ming Xiaoling Mausoleum, the feng shui (geomantic) suitability evaluation results obtained for the mausoleums analyzed herein are found to be consistent with the actual distribution of the mausoleum sites. The research shows that the AHP-weighted information method based on GIS is suitable for evaluating the feng shui (geomantic) suitability of mausoleums.

Innovation is a key factor for a country’s overall national strength and core competitiveness. The spatial pattern of innovation reflects the regional differences of innovation development, which can provide guidance for the regional allocation of innovation resources. Most studies on the spatial pattern of innovation are at urban and above spatial scale, but studies at urban internal scale are insufficient. The precision and index of the spatial pattern of innovation in the city needs to be improved. This study proposes to divide spatial units based on geographic coordinates of patents, designs the innovation capability and innovation structure index of a spatial unit and their calculation methods, and then reveals the spatial patterns of innovation and their evolutionary characteristics in Shenzhen during 2000–2018. The results show that: (1) The pattern of innovation capacity of secondary industry exhibited a pronounced spatial spillover effect with a positive spatial correlation. The innovation capacity and innovation structure index of the secondary industry evolved in a similar manner; i.e., they gradually extended from the southwest area to the north over time, forming a tree-like distribution pattern with the central part of the southwest area as the “root” and the northwest and northeast areas as the “canopy”. (2) The pattern of innovation capacity of tertiary industry also had a significant spatial spillover effect with a positive spatial correlation. There were differences between the evolutions of innovation capacity and innovation structure index of tertiary industry. Specifically, its innovation capacity presented a triangular spatial distribution pattern with three groups in the central and eastern parts of the southwest area and the south-eastern part of the northwest area as the vertices, while its innovative structure showed a radial spatial distribution pattern with the southwestern part of the southwest area as the source and a gradually sparse distribution toward the northeast. (3) There were differences between the evolution modes of secondary and tertiary industries. Areas with high innovation capacity in the secondary industry tended to be more balanced, while areas with high innovation capacity in the tertiary industry did not necessarily have a balanced innovation structure. Through the method designed in this paper, the spatial pattern of urban innovation can be more precise and comprehensive revealed, and provide useful references for the development of urban innovation.