Early warning model for slope debris flow initiation

Early warning model of debris flow is important for providing local residents with reliable and accurate warning information to escape from debris flow hazards. This research studied the debris flow initiation in the Yindongzi gully in Dujiangyan City, Sichuan province, China with scaled-down model experiments. We set rainfall intensity and slope angle as dominating parameters and carried out 20 scaled-down model tests under artificial rainfall conditions. The experiments set four slope angles(32°, 34°, 37°, 42°) and five rainfall intensities(60 mm/h, 90 mm/h, 120 mm/h, 150 mm/h, and 180 mm/h) treatments. The characteristic variables in the experiments, such as, rainfall duration, pore water pressure, moisture content, surface inclination, and volume were monitored. The experimental results revealed the failure mode of loose slope material and the process of slope debris flow initiation, as well as the relationship between the surface deformation and the physical parameters of experimental model. A traditional rainfall intensity-duration early warning model(I-D model) was firstly established by using a mathematical regression analysis, and it was then improved into ISD model and ISM model(Here, I is rainfall Intensity, S is Slope angle, D is rainfall Duration, and M is Moisture content). The warning model can provide reliable early warning of slope debris flow initiation.

A Novel Early Warning Model for Hand, Foot and Mouth Disease Prediction Based on a Graph Convolutional Network

Objectives Hand,foot and mouth disease(HFMD)is a widespread infectious disease that causes a significant disease burden on society.To achieve early intervention and to prevent outbreaks of disease,we propose a novel warning model that can accurately predict the incidence of HFMD.Methods We propose a spatial-temporal graph convolutional network(STGCN)that combines spatial factors for surrounding cities with historical incidence over a certain time period to predict the future occurrence of HFMD in Guangdong and Shandong between 2011 and 2019.The 2011-2018 data served as the training and verification set,while data from 2019 served as the prediction set.Six important parameters were selected and verified in this model and the deviation was displayed by the root mean square error and the mean absolute error.Results As the first application using a STGCN for disease forecasting,we succeeded in accurately predicting the incidence of HFMD over a 12-week period at the prefecture level,especially for cities of significant concern.Conclusions This model provides a novel approach for infectious disease prediction and may help health administrative departments implement effective control measures up to 3 months in advance,which may significantly reduce the morbidity associated with HFMD in the future.

Remote Monitoring and Early Warning Model of Frozen Soil in Dam Areas

In view of the cumbersome and often untimely process of manual collection and observation of frozen soil data parameters,and the damage caused to dams by frost heaving of frozen soil,a remote monitoring and an early warning model for frozen soil in dam areas was presented.The Pt100 temperature sensors and JM seam gauges were used as measurement tools in the system.The sensor layout was designed,based on the actual situation in the monitoring area.A 4G network was used for wireless transmission to monitor frozen soil data in real time.BP neural network was used to predict the parameters of frozen soil.After analysis,four factors including the average temperature of frozen soil,the type of frozen soil,the artificial upper limit of frozen soil and the building construction time were selected to establish an early warning model using fuzzy reasoning.The experimental results showed that the early warning model could reflect the influence on dam buildings of frost heaving and sinking of frozen soil,and provided technical support for predicting the hazard level.

Influenza Early Warning Model Based on Yunqi Theory

Objective：To establish an early warning model to simulate the outbreak of influenza based on weather conditions and Yunqi theory,an ancient calendar theory of Chinese medicine（CM）.Methods：Tianjin, a northeastern city in China,was chosen as the region of research and applied the influenza-like illness attack rate（ILI）%as the baseline and warning line to determine the severity of influenza epidemic.Then,an influenza early warning model was constructed based on the theory of rough set and support vector machines（RS-SVM）, and the relationship between influenza and meteorology was explored through analyzing the monitoring data. Results：The predictive performance of the model was good,which had achieved 81.8%accuracy when grouping the obtained data into three levels that represent no danger,danger of a light epidemic,and danger of a severe epidemic.The test results showed that if the host qi and guest qi were not balanced,this kind of situation was more likely to cause influenza outbreaks.Conclusions：The outbreak of influenza closely relates to temperature, humidity,visibility,and wind speed and is consistent with some part of CM doctrine.The result also indicates that there is some reasonable evidence in the Yunqi theory.

Centrifuge model test of an irrigation-induced loess landslide in the Heifangtai loess platform, Northwest China

The Heifangtai platform in Northwest China is famous for irrigation-induced loess landslides.This study conducted a centrifuge model test with reference to an irrigation-induced loess landslide that occurred in Heifangtai in 2011.The loess slope model was constructed by whittling a cubic loess block obtaining from the landslide site.The irrigation water was simulated by applying continuous infiltration from back of the slope.The deformation,earth pressure,and pore pressure were investigated during test by a series of transducers.For this particular study,the results showed that the failure processes were characterized by retrogressive landslides and cracks.The time dependent reductions of cohesion and internal friction angle at basal layer with increasing pore-water pressure were responsible for these failures.The foot part of slope is very important for slope instability and hazard prevention in the study area,where concentration of earth pressure and generation of high pore-water pressures would form before failures.The measurements of earth pressure and pore-water pressure might be effective for early warning in the study area.