Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

Kinetic method for enzymatic analysis by predicting background with uricase reaction as model

Objective：To investigate the reliability for kinetic assay of substance with background predicted by the integrated method using uricase reaction as model. Methods： Absorbance before uricase action （Δ0） was estimated by extrapolation with given lag time of steady-state reaction. With Km fixed at 12.5μmol/L, background absorbance （Δb） was predicted by nonlinearly fitting integrated Michaelis-Menten equation to Candida utilis uricase reaction curve. Uric acid in reaction solution was determined by the difference （ΔA） between Δ0 and Δb. Results .Ab usually showed deviation 〈3% from direct assay with residual substrate done fifth of initial substrate for analysis. ΔA showed CV 〈5% with resistance to common interferences except xanthine, and it linearly responded to uric acid with slope consistent to the absorptivity of uric acid. The lower limit was 2.0 μmol/L and upper limit reached 30 μmol/L in reaction solution with data monitored within 8 min reaction at 0. 015 U/ml uricase. Preliminary application to serum and urine gave better precision than the direct equilibrium method without the removal of proteins before analysis. Conclusion .This kinetic method with background predicted by the integrated method was reliable for enzymatic analysis, and it showed resistance to common interferences and enhanced efficiency at much lower cost.

An improved influence function method for predicting subsidence caused by longwall mining operations in inclined coal seams

Prediction of surface subsidence caused by longwall mining operation in inclined coal seams is often very challenging. The existing empirical prediction methods are inflexible for varying geological and mining conditions. An improved influence function method has been developed to take the advantage of its fundamentally sound nature and flexibility. In developing this method, the original Knothe function has been transformed to produce a continuous and asymmetrical subsidence influence function. The empirical equations for final subsidence parameters derived from col- lected longwall subsidence data have been incorporated into the mathematical models to improve the prediction accuracy. A number of demonstration cases for longwall mining operations in coal seams with varying inclination angles, depths and panel widths have been used to verify the applicability of the new subsidence prediction model.

Preliminary Studies on Predicting the Tropical Indian Ocean Sea Surface Temperature through Combined Statistical Methods and Dynamic ENSO Prediction

The sea surface temperature （SST） in the In- dian Ocean affects the regional climate over the Asian continent mostly through a modulation of the monsoon system. It is still difficult to provide an a priori indication of the seasonal variability over the Indian Ocean. It is widely recognized that the warm and cold events of SST over the tropical Indian Ocean are strongly linked to those of the equatorial eastern Pacific. In this study, a statistical prediction model has been developed to predict the monthly SST over the tropical Indian Ocean. This model is a linear regression model based on the lag relationship between the SST over the tropical Indian Ocean and the Nino3.4 （5°S-5°N, 170°W-120°W） SST Index. The pre- dictor （i.e., Nino3.4 SST Index） has been operationally predicted by a large size ensemble E1 Nifio and the Southern Oscillation （ENSO） forecast system with cou- pled data assimilation （Leefs_CDA）, which achieves a high predictive skill of up to a 24-month lead time for the equatorial eastern Pacific SST. As a result, the prediction skill of the present statistical model over the tropical In- dian Ocean is better than that of persistence prediction for January 1982 through December 2009.

A novel method for predicting breakthrough time of horizontal wells in bottom water reservoirs

Dimensional analysis and numerical simulations were carried out to research prediction method of breakthrough time of horizontal wells in bottom water reservoir. Four dimensionless independent variables and dimensionless time were derived from 10 influencing factors of the problem by using dimensional analysis. Simulations of horizontal well in reservoir with bottom water were run to find the prediction correlation. A general and concise functional relationship for predicting breakthrough time was established based on simulation results and theoretical analysis. The breakthrough time of one conceptual model predicted by the correlation is very close to the result by Eclipse with less than 2% error. The practical breakthrough time of one well in Helder oilfield is 10 d, and the predicted results by the method is 11.2 d, which is more accurate than the analytical result. Case study indicates that the method could predict breakthrough time of horizontal well under different reservoir conditions accurately. For its university and ease of use, the method is suitable for quick prediction of breakthrough time.

A method for predicting the water-flowing fractured zone height based on an improved key stratum theory

In the process of using the original key stratum theory to predict the height of a water-flowing fractured zone(WFZ),the influence of rock strata outside the calculation range on the rock strata within the calculation range as well as the fact that the shape of the overburden deformation area will change with the excavation length are ignored.In this paper,an improved key stratum theory(IKS theory)was proposed by fixing these two shortcomings.Then,a WFZ height prediction method based on IKS theory was established and applied.First,the range of overburden involved in the analysis was determined according to the tensile stress distribution range above the goaf.Second,the key stratum in the overburden involved in the analysis was identified through IKS theory.Finally,the tendency of the WFZ to develop upward was determined by judging whether or not the identified key stratum will break.The proposed method was applied and verified in a mining case study,and the reasons for the differences in the development patterns between the WFZs in coalfields in Northwest and East China were also fully explained by this method.

Predicting the 25th solar cycle using deep learning methods based on sunspot area data

It is a significant task to predict the solar activity for space weather and solar physics. All kinds of approaches have been used to forecast solar activities, and they have been applied to many areas such as the solar dynamo of simulation and space mission planning. In this paper, we employ the long-shortterm memory(LSTM) and neural network autoregression(NNAR) deep learning methods to predict the upcoming 25 th solar cycle using the sunspot area(SSA) data during the period of May 1874 to December2020. Our results show that the 25 th solar cycle will be 55% stronger than Solar Cycle 24 with a maximum sunspot area of 3115±401 and the cycle reaching its peak in October 2022 by using the LSTM method. It also shows that deep learning algorithms perform better than the other commonly used methods and have high application value.

Establishment of Prediction Method of Tourism Meteorological Index in Langzhong Ancient City

Based on the meteorological data of Langzhong from 2010 to 2020,the human body comfort index was calculated,and tourism climate comfort was evaluated to establish the prediction equation of tourism meteorological index.OLS was used to compare the correlation between actual tourist flow and tourism meteorological index and test the model effect.Average correlation coefficient R was 0.7017,so the correlation was strong,and P value was 0.The two were significantly correlated at 0.01 level(bilateral).It can be seen that the forecast equation of tourism meteorological index had a strong correlation with the actual number of tourists,and the predicted value was basically close to the actual situation,and the forecast effect is good.

Comparative Study of Probabilistic and Least-Squares Methods for Developing Predictive Models

This article explores the comparison between the probability method and the least squares method in the design of linear predictive models. It points out that these two approaches have distinct theoretical foundations and can lead to varied or similar results in terms of precision and performance under certain assumptions. The article underlines the importance of comparing these two approaches to choose the one best suited to the context, available data and modeling objectives.

AN EXTENSION OF THE METHOD FOR PREDICTING PERMEABILITY THROUGH POLYMER MEMBRANES FROM SIMPLE GASES TO WATER VAPOR

It is found that there is a linear relationship between log P-w, and the parameter term V-f/0.5 E(coh) [1+(delta(w) - delta(p))(2)/delta(p)(2), from the water permeability (P-w) data of 21 polymers covering 4 orders of magnitude. This correlation may be useful in choosing membrane materials for dehumidification of gases.

Predicting coastal morphological changes with empirical orthogonal function method

In order to improve the accuracy of prediction when using the empirical orthogonal function （EOF） method, this paper describes a novel approach for two-dimensional （2D） EOF analysis based on extrapolating both the spatial and temporal EOF components for long-term prediction of coastal morphological changes. The approach was investigated with data obtained from a process-based numerical model, COAST2D, which was applied to an idealized study site with a group of shore-parallel breakwaters. The progressive behavior of the spatial and temporal EOF components, related to bathymetric changes over a training period, was demonstrated, and EOF components were extrapolated with combined linear and exponential functions for long-term prediction. The extrapolated EOF components were then used to reconstruct bathymetric changes. The comparison of the reconstructed bathymetric changes with the modeled results from the COAST2D model illustrates that the presented approach can be effective for long-term prediction of coastal morphological changes, and extrapolating both the spatial and temporal EOF components yields better results than extrapolating only the temporal EOF component.

Method of Predicting Water Content in Crude Oil Based on Measuring Range Automatic Switching

Water content in output crude oil is hard to measure precisely because of wide range of dielectric coefficient of crude oil caused by injected dehydrating and demulsifying agents.The method to reduce measurement error of water content in crude oil proposed in this paper is based on switching measuring ranges of on-line water content analyzer automatically.Measuring precision on data collected from oil field and analyzed by in-field operators can be impressively improved by using back propogation （BP） neural network to predict water content in output crude oil.Application results show that the difficulty in accurately measuring water-oil content ratio can be solved effectively through this combination of on-line measuring range automatic switching and real time prediction,as this method has been tested repeatedly on-site in oil fields with satisfactory prediction results.

Application of Homotopy-Newton Hybrid Method for Predicting Azeotropes

A homotopy-Newton hybrid method is proposed for predicting azeotropes, in which the set of homotopy equations with larger convergent domain is solved firstly to generate the better initial guess, then Newton algorithm is used to solve the azeotrope equations. It has been proved through some illustrative examples that the hybrid method has concurrently the advantages of the wide range of convergence, flexible demand for initial guess and good smoothness of iterative process as the homotopy algorithm, and the rapid convergent speed and high accuracy as Newton′s method. So the hybrid method is a prospective approach to predicting the azeotropes of nonideal mixtures.

Comparison between methods for predicting maximum solid solubility of transition metals in solvent metal

It is important to know the maximum solid solubility( C max ) of various transition metals in a metal when one designs multi component alloys. There have been several semi empirical approaches to qualitatively predict the C max , such as Darken Gurry(D G) theorem, Miedema Chelikowsky(M C) theorem, electron concentration rule and the bond parameter rule. However, they are not particularly valid for the prediction of C max . It was developed on the basis of energetics of alloys as a new method to predict C max of different transition metals in metal Ti, which can be described as a semi empirical equation using the atomic parameters, i e, electronegativity difference, atomic diameter and electron concentration. It shows that the present method can be used to explain and deduce D G theorem, M C theorem and electron concentration rule.

Prediction method of highway pavement rutting based on the grey theory

In order to make a scientific pavement maintenance decision, a grey-theory-based prediction methodological framework is proposed to predict pavement performance. Based on the field pavement rutting data,analysis of variance （ANOVA）was first used to study the influence of different factors on pavement rutting. Cluster analysis was then employed to investigate the rutting development trend.Based on the clustering results,the grey theory was applied to build pavement rutting models for each cluster, which can effectively reduce the complexity of the predictive model.The results show that axial load and asphalt binder type play important roles in rutting development.The prediction model is capable of capturing the uncertainty in the pavement performance prediction process and can meet the requirements of highway pavement maintenance,and,therefore,has a wide application prospects.

Projectile impact point prediction method based on GRNN

In order to forecast projectile impact points quickly and accurately,aprojectile impact point prediction method based on generalized regression neural network（GRNN）is presented.Firstly,the model of GRNN forecasting impact point is established;secondly,the particle swarm algorithm（PSD）is used to optimize the smooth factor in the prediction model and then the optimal GRNN impact point prediction model is obtained.Finally,the numerical simulation of this prediction model is carried out.Simulation results show that the maximum range error is no more than 40 m,and the lateral deviation error is less than0.2m.The average time of impact point prediction is 6.645 ms,which is 1 300.623 ms less than that of numerical integration method.Therefore,it is feasible and effective for the proposed method to forecast projectile impact points,and thus it can provide a theoretical reference for practical engineering applications.

Effects of Mapping Methods on Accuracy of Protein Coding Regions Prediction

[Objective] To discuss the effects of major mapping methods for DNA sequence on the accuracy of protein coding regions prediction,and to find out the effective mapping methods.[Method] By taking Approximate Correlation（AC） as the full measure of the prediction accuracy at nucleotide level,the windowed narrow pass-band filter（WNPBF） based prediction algorithm was applied to study the effects of different mapping methods on prediction accuracy.[Result] In DNA data sets ALLSEQ and HMR195,the Voss and Z-Curve methods are proved to be more effective mapping methods than paired numeric（PN）,Electron-ion Interaction Potential（EIIP） and complex number methods.[Conclusion] This study lays the foundation to verify the effectiveness of new mapping methods by using the predicted AC value,and it is meaningful to reveal DNA structure by using bioinformatics methods.

Predicting geological hazards during tunnel construction

The complicated geological conditions and geological hazards are challenging problems during tunnel construction,which will cause great losses of life and property.Therefore,reliable prediction of geological defective features,such as faults,karst caves and groundwater,has important practical significances and theoretical values.In this paper,we presented the criteria for detecting typical geological anomalies using the tunnel seismic prediction（TSP） method.The ground penetrating radar（GPR） signal response to water-bearing structures was used for theoretical derivations.And the 3D tomography of the transient electromagnetic method（TEM） was used to develop an equivalent conductance method.Based on the improvement of a single prediction technique,we developed a technical system for reliable prediction of geological defective features by analyzing the advantages and disadvantages of all prediction methods.The procedure of the application of this system was introduced in detail.For prediction,the selection of prediction methods is an important and challenging work.The analytic hierarchy process（AHP） was developed for prediction optimization.We applied the newly developed prediction system to several important projects in China,including Hurongxi highway,Jinping II hydropower station,and Kiaochow Bay subsea tunnel.The case studies show that the geological defective features can be successfully detected with good precision and efficiency,and the prediction system is proved to be an effective means to minimize the risks of geological hazards during tunnel construction.