Rapid prediction models for 3D geometry of landslide dam considering the damming process

The geometry of a landslide dam plays a critical role in its stability and failure mode,and is influenced by the damming process.However,there is a lack of understanding of the factors that affect the 3D geometry of a landslide dam.To address this gap,we conducted a study using the smoothed particle hydrodynamics numerical method to investigate the evolution of landslide dams.Our study included 17 numerical simulations to examine the effects of several factors on the geometry of landslide dams,including valley inclination,sliding angle,landslide velocity,and landslide mass repose angle.Based on this,three rapid prediction models were established for calculating the maximum height,the minimum height,and the maximum width of a landslide dam.The results show that the downstream width of a landslide dam remarkably increases with the valley inclination.The position of the maximum dam height along the valley direction is independent of external factors and is always located in the middle of the landslide width area.In contrast,that position of the maximum dam height across the valley direction is significantly influenced by the sliding angle and landslide velocity.To validate our models,we applied them to three typical landslide dams and found that the calculated values of the landslide dam geometry were in good agreement with the actual values.The findings of the current study provide a better understanding of the evolution and geometry of landslide dams,giving crucial guidance for the prediction and early warning of landslide dam disasters.

A Rapid Prediction Model of Urban Flood Inundation in a High-Risk Area Coupling Machine Learning and Numerical Simulation Approaches

Climate change has led to increasing frequency of sudden extreme heavy rainfall events in cities,resulting in great disaster losses.Therefore,in emergency management,we need to be timely in predicting urban floods.Although the existing machine learning models can quickly predict the depth of stagnant water,these models only target single points and require large amounts of measured data,which are currently lacking.Although numerical models can accurately simulate and predict such events,it takes a long time to perform the associated calculations,especially two-dimensional large-scale calculations,which cannot meet the needs of emergency management.Therefore,this article proposes a method of coupling neural networks and numerical models that can simulate and identify areas at high risk from urban floods and quickly predict the depth of water accumulation in these areas.Taking a drainage area in Tianjin Municipality,China,as an example,the results show that the simulation accuracy of this method is high,the Nash coefficient is 0.876,and the calculation time is 20 seconds.This method can quickly and accurately simulate the depth of water accumulation in high-risk areas in cities and provide technical support for urban flood emergency management.

Comparison and rapid prediction of lignocellulose and organic elements of a wide variety of rice straw based on near infrared spectroscopy

Rice straw is a major kind of biomass that can be utilized as lignocellulosic materials and renewable energy.Rapid prediction of the lignocellulose(cellulose,hemicellulose,and lignin)and organic elements(carbon,hydrogen,nitrogen,and sulfur)of rice straw would help to decipher its growth mechanisms and thereby improve its sustainable usages.In this study,364 rice straw samples featuring different rice subspecies(japonica and indica),growing seasons(early-,middle-,and late-season),and growing environments(irrigated and rainfed)were collected,the differences among which were examined by multivariate analysis of variance.Statistic results showed that the cellulose content exhibited significant differences among different growing seasons at a significant level(p<0.01),and the contents of cellulose and nitrogen had significant differences between different growing environments(p<0.01).Near infrared reflectance spectroscopy(NIRS)models for predicting the lignocellulosic and organic elements were developed based on two algorithms including partial least squares(PLS)and competitive adaptive reweighted sampling-partial least squares(CARS-PLS).Modeling results showed that most CARS-PLS models are of higher accuracy than the PLS models,possibly because the CARS-PLS models selected optimal combinations of wavenumbers,which might have enhanced the signal of chemical bonds and thereby improved the predictive efficiency.As a major contributor to the applications of rice straw,the nitrogen content was predicted precisely by the CARS-PLS model.Generally,the CARS-PLS models efficiently quantified the lignocellulose and organic elements of a wide variety of rice straw.The acceptable accuracy of the models allowed their practical applications.

Mining hyperspectral data for non-destructive and rapid prediction of nitrite content in ham sausages

Accurate and rapid determination of nitrite contents is an important step for guaranteeing sausage quality.This study attempted to mine hyperspectral data in the range of 900-1700 nm for non-destructive and rapid prediction of nitrite contents in sausages.The average spectra of 156 samples were collected to relate to the measured nitrite values by partial least squares(PLS)regression.Optimal wavelengths were respectively selected by successive projections algorithm(SPA)and regression coefficients(RC)to simplify the PLS model.The results indicated that PLS model established with 15 optimal wavelengths(900.5 nm,907.1 nm,908.8 nm,912.1 nm,915.4 nm,920.3 nm,922.0 nm,941.7 nm,979.6 nm,1083.2 nm,1213.2 nm,1353.0 nm,1460.2 nm,1595.6 nm and 1699.9 nm)selected by SPA had better performance with r C,r CV,r P of 0.92,0.89,0.89 and RMSEC,RMSECV,RMSEP of 0.41 mg/kg,0.89 mg/kg,0.49 mg/kg,respectively,for calibration set,cross-validation and prediction set.It was concluded that hyperspectral data could be mined by PLS&SPA for realizing the rapid evaluation of nitrite content in ham sausages.

The rapid uncertainty prediction of the ocean-acoustic coupled model

Focusing on the rapid prediction of acoustic field uncertainty in environment with temporal and spatial sound speed perturbation, evolvement of sound speed structure over time is predicted based on the ocean-acoustic coupled model to obtain the uncertainty distribution of the vertical structure of sound speed. Further, a method combining the arbitrary polynomial chaos expansion with the empirical orthogonal function is proposed to reduce the dimensionality of uncertain parameters and to obtain the uncertainty distribution of the acoustic field. Simulations have shown that the computational complexity can be reduced by 2 orders of magnitude compared to the conventional polynomial chaos expansion while ensures the same precision.Moreover, the computational complexity is not influenced by the complexity of the sound speed profile. The acoustic field and uncertainty predicted in uncertain environment by proposed method also have been tested with the experimental data.

Choroidal blood perfusion as a potential"rapid predictive index"for myopia development and progression

Myopia is the leading cause of visual impairment worldwide.The lack of a"rapid predictive index"for myopia development and progression hinders the clinic management and prevention of myopia.This article reviews the studies describing changes that occur in the choroid during myopia development and proposes that it is possible to detect myopia development at an earlier stage than is currently possible in a clinical setting using choroidal blood perfusion as a"rapid predictive index"of myopia.

Choroidal blood perfusion as a potential“rapid predictive index”for myopia development and progression

Myopia is the leading cause of visual impairment worldwide.The lack of a“rapid predictive index”for myopia development and progression hinders the clinic management and prevention of myopia.This article reviews the studies describing changes that occur in the choroid during myopia development and proposes that it is possible to detect myopia development at an earlier stage than is currently possible in a clinical setting using choroidal blood perfusion as a“rapid predictive index”of myopia.