Progress of machine learning in geosciences: Preface

正In the past two decades,artificial intelligence(AI)algorithms have proved to be promising tools for solving several tough scientific problems.As a broad subfield of AI,machine learning is concerned with algorithms and techniques that allow computers to"learn".The machine learning approach covers main domains such as data mining,difficult-to-program applications,and software applications.It is a collection of a variety of algorithms that     

Recognition of strong earthquake–prone areas with a single learning class

This article presents a new Barrier recognition algorithm with learning, designed for recognition of earthquake-prone areas. In comparison to the Crust (Kora) algorithm, used by the classical EPA approach, the Barrier algorithm proceeds with learning just on one “pure” high-seismic class. The new algorithm operates in the space of absolute values of the geological–geophysical parameters of the objects. The algorithm is used for recognition of earthquake-prone areas with М ≥ 6.0 in the Caucasus region. Comparative analysis of the Crust and Barrier algorithms justifies their productive coherence.     

Lego NXT information on test dimensionality using Kolb’s innovative learning cycle

Recently, artificial intelligence has been successfully applied to hazard prevention. Lego has released a programmable module, which many educational organizations and micro-operation robots have used. This has given rise to a new topic of study, how to use Lego NXT?in education.?In this paper, we present an application of Lego NXT in the subject of mathematics. The principle is based on Kolb??s innovative learning cycle that the user??s active learning and cooperative learning concepts complete the whole process of learning experience.?In order to compare the effectiveness of learning, we use an experimental group and a control group and give then pre- and posttests.?In addition, we proposed the technology acceptance model to investigate users?? degree of acceptance of Lego.?The results show that our approach can improve the users?? mathematical achievements and strengthen the users?? intention to use.     

How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?

Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.     

Boulder falls in Hong Kong — insights from power law relationships and supervised machine learning

Landslides - Intelligently predicting the frequency and volume of natural hazards, including boulder falls, attracts widespread attention in earth science and engineering communities. Taking Hong...     

Multi-geohazards susceptibility mapping based on machine learning—a case study in Jiuzhaigou,China

Jiuzhaigou, located in the transitional area between the Qinghai–Tibet Plateau and the Sichuan Basin, is highly prone to geological hazards (e.g., rock fall, landslide, and debris flow). High-performance-based hazard prediction models, therefore, are urgently required to prevent related hazards and manage potential emergencies. Current researches mainly focus on susceptibility of single hazard but ignore that different types of geological hazards might occur simultaneously under a complex environment. Here, we firstly built a multi-geohazard inventory from 2000 to 2015 based on a geographical information system and used satellite data in Google earth and then chose twelve conditioning factors and three machine learning methods—random forest, support vector machine, and extreme gradient boosting (XGBoost)—to generate rock fall, landslide, and debris flow susceptibility maps. The results show that debris flow models presented the best prediction capabilities [area under the receiver operating characteristic curve (AUC 0.95)], followed by rock fall (AUC 0.94) and landslide (AUC 0.85). Additionally, XGBoost outperformed the other two methods with the highest AUC of 0.93. All three methods with AUC values larger than 0.84 suggest that these models have fairly good performance to assess geological hazards susceptibility. Finally, evolution index was constructed based on a joint probability of these three hazard models to predict the evolution tendency of 35 unstable slopes in Jiuzhaigou. The results show that these unstable slopes are likely to evolve into debris flows with a probability of 46%, followed by landslides (43%) and rock falls (29%). Higher susceptibility areas for geohazards were mainly located in the southeast and middle of Jiuzhaigou, implying geohazards prevention and mitigation measures should be taken there in near future.

     

Sensitivity analysis of parameters influencing the ice–seabed interaction in sand by using extreme learning machine

Ice gouging problem is a significant challenge threatening the integrity of subsea pipelines in the Arctic (e.g., Beaufort Sea) and even non-Arctic (e.g., Caspian Sea) offshore regions. Determining the seabed response to ice scour through the subgouge soil deformations and the keel reaction forces are important aspects for a safe and cost-effective design. In this study, the subgouge soil deformations and the keel reaction forces were simulated by the extreme learning machine (ELM) for the first time. Nine ELM models (ELM 1–ELM 9) were developed using the key parameters governing the ice–seabed interaction. The number of neurons in the hidden layer was optimized and the best activation function for the ELM network was identified. The premium ELM model, resulting in the lowest level of inaccuracy and complexity and the highest level of correlation with experimental values was identified by performing a sensitivity analysis. The gouge depth ratio and the shear strength of the seabed soil were found to be the most influential input parameters affecting the subgouge soil deformations and the keel reaction forces. A set of the ELM-based equations were proposed to approximate the ice gouging parameters. The uncertainty analysis showed that the premium ELM model slightly underestimated the subgouge soil deformation.

     

Portability of semantic and spatial–temporal machine learning methods to analyse social media for near-real-time disaster monitoring

Natural Hazards - Up-to-date information about an emergency is crucial for effective disaster management. However, severe restrictions impede the creation of spatiotemporal information by current...     

How to manipulate interactive E-book on learning natural catastrophe—An example of structural mechanics using power machine

Economic losses caused by natural disasters have increased rapidly in recent years. Therefore, learning about natural catastrophe will be helpful for saving life and reducing cost. This study explores the effect of using the interactive electronic book available from Lego Simple Power Machine sets for learning. The applications for Power Machine are wide, such as in robots manufacturing, artificial intelligence, and intelligent devices. Based on the theory of Human–Computer Interaction, user-centered design is a modern and widely practiced design philosophy rooted in the idea that users must take center-stage in the design of any computer system. An interactive electronic book is developed to test learning achievement with the Lego Simple Power Machine set. The results reveal that the attention of users can be improved and the image can also be improved during the interaction and operation process. Finally, this study proves that the interactive electronic book makes learning easier and faster than the traditional book.     

Snow avalanche susceptibility evaluation in the central Shaluli Mountains of Tibetan Plateau based on machine learning method北大核心CSCD

Snow avalanches,which are widely and frequently developed at high elevations,seriously threatens the built traffic corridors in the Tibetan Plateau. Susceptibility evaluation of snow avalanche via machine learning model with a high forecast accuracy can be appled to quickly and effectively assess the regional avalanche risk. This paper took the central Shaluli Mountain region as the study area,in which the snow avalanche inventory was established through remote sensing interpretation and field investigation verification. We quantitatively extracted 17 evaluation factors via GIS-based analysis,and these factors were selected through the variance expansion factor（VIF）. Four machine learning models containing SVM,DT,MLP and KNN were used to compile the susceptibility index map of snow avalanches,and kappa coefficient and ROC curve were used to verify the accuracy. The results suggested that the susceptibility indexes obtained from SVM,DT,MLP and KNN were in the range of［0,0. 964］,［0,815］,［0,0. 995］and［0,1］,respectively. The accuracy test results show that these four models all have good prediction accuracy. Among them,the SVM model is the best. The results also indicated that the areas with the high snow avalanche susceptibility mainly distributed in Genie Mountain and Rigong Mountain,most of which were above the planation surface of the Tibetan Plateau. The average altitude of the extremely high snow-avalanche-prone areas is 4 939 m,while the average altitude of the high snow avalanche-prone areas is 4 859 m. The snow avalanche has low perniciousness on the Sichuan-Tibet Highway and the Sichuan-Tibet Railway in the study area. This study can provide theoretical basis and method reference for disaster prevention and mitigation of snow avalanche along Sichuan-Tibet Railway and other major projects across Shaluli Mountains region. © 2022 Science Press (China).     

A comparative study of different machine learning algorithms in predicting EPB shield behaviour: a case study at the Xi’an metro,China

Acta Geotechnica - Complex geological conditions and/or inappropriate shield tunnel boring machine (TBM) operation can significantly degrade both the excavation and safety of tunnel construction....     

“Events and failures are our only means for making policy changes”: learning in disaster and emergency management policies in Manitoba,Canada

Natural Hazards - The Canadian Province of Manitoba has experienced many severe floods and other natural disasters, and in response municipal, provincial, and federal governments have developed...