Slope failures lead to large casualties and catastrophic societal and economic consequences, thus potentially threatening access to sustainable development. Slope stability assessment, offering potential long-term benefits for sustainable development, remains a challenge for the practitioner and researcher. In this study, for the first time, an automated machine learning (AutoML) approach was proposed for model development and slope stability assessments of circular mode failure. An updated database with 627 cases consisting of the unit weight, cohesion, and friction angle of the slope materials; slope angle and height; pore pressure ratio; and corresponding stability status has been established. The stacked ensemble of the best 1000 models was automatically selected as the top model from 8208 trained models using the H2O-AutoML platform, which requires little expert knowledge or manual tuning. The top-performing model outperformed the traditional manually tuned and metaheuristic-optimized models, with an area under the receiver operating characteristic curve (AUC) of 0.970 and accuracy (ACC) of 0.904 based on the testing dataset and achieving a maximum lift of 2.1. The results clearly indicate that AutoML can provide an effective automated solution for machine learning (ML) model development and slope stability classification of circular mode failure based on extensive combinations of algorithm selection and hyperparameter tuning (CASHs), thereby reducing human efforts in model development. The proposed AutoML approach has the potential for short-term severity mitigation of geohazard and achieving long-term sustainable development goals.

Natural hazards cause severe consequences to society, the economy, and the environment. However, it is difficult to analyze natural hazards occurrences in China because there is no complete natural hazard database in China, and it is difficult to gather all conventional natural hazard data because they are kept by many different departments. To resolve this problem, this paper proposes a social media data mining methodology. Because social media is a real-time data source, it is an effective channel for up-to-date information about the characteristics of disasters/hazards. News about natural hazards from 2008 to 2017 is mined from a news organization in China as the key data. Text mining, descriptive statistics, association rule mining, and other methods are used to extract the natural hazard events and hazard characteristics type, time, and location for the analysis. First, from an analysis of the news headlines, each hazard-focused news event is identified and the time and location information are extracted. Second, the spatial-temporal distributions of the natural hazards are analyzed using statistical analysis and network visualization, from which it is found that rainstorms, floods, wind and hail, and other meteorological hazards are the main natural hazard types in China. The high co-occurrence of meteorological hazards and geological hazards indicates that the government needs to pay more attention to geological hazards if there is also a meteorological hazard, especially in mountainous areas. Most hazards are found to have an obvious time distribution, with the high-frequency period being from April to September. Yunnan, Sichuan, and Guizhou Provinces are found to suffer the most frequently from a range of different hazards. An analysis of the associations between hazard regions finds that the southern Chinese regions are strongly related, especially Guizhou, Sichuan, Hubei, and Hunan. The results of this study offer insights into the identification of hazard risks and assists in the development of effective hazard prevention and mitigation programs.

Yang, Y., H. Guo, L. Chen, X. Liu, M. Gu, and W. Pan. 2020. Multiattribute decision making for the assessment of disaster resilience in the Three Gorges Reservoir Area. Ecology and Society 25(2):5. https://doi.org/10.5751/ES-11464-250205

Geohazard prevention and mitigation are highly complex and remain challenges for researchers and practitioners. Artificial intelligence (AI) has become an effective tool for addressing these challenges. Therefore, for decades, an increasing number of researchers have begun to conduct AI research in the field of geohazards leading to rapid growth in the number of related papers. This has made it difficult for researchers and practitioners to grasp information on cutting-edge developments in the field, thus necessitating a comprehensive review and analysis of the current state of development in the field. In this study, a comprehensive scientometric analysis appraising the state-of-the-art research for geohazard was performed based on 9226 scientometric records from the Web of Science core collection database. Multiple types of scientometric techniques, including coauthor analysis, co-citation analysis, and cluster analysis were employed to identify the most productive researchers, institutions, and hot research topics. The results show that research related to the application of AI in the field of geohazards experienced a period of rapid growth after 2000, with major developments in the field occurring in China, the United States, and Italy. The hot research topics in this field are ground motion, deep learning (DL), and landslides. The commonly used AI algorithms include DL, support vector machine (SVM), and decision tree (DT). The obtained visualization on research networks offers valuable insights and an in-depth understanding of the key researchers, institutions, fundamental articles, and salient topics through animated maps. We believe that this scientometric review offers useful reference points for early-stage researchers and provides valuable in-depth information to experienced researchers and practitioners in the field of geohazard research. This scientometric analysis and visualization are promising for reflecting the global picture of AI-based geohazard research comprehensively and possess potential for the visualization of the emerging trends in other research fields.