Prediction and optimization of flue pressure in sintering process based on SHAP

Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.

Short-Term Rolling Prediction of Tropical Cyclone Intensity Based on Multi-Task Learning with Fusion of Deviation-Angle Variance and Satellite Imagery

Tropical cyclones(TCs)are one of the most serious types of natural disasters,and accurate TC activity predictions are key to disaster prevention and mitigation.Recently,TC track predictions have made significant progress,but the ability to predict their intensity is obviously lagging behind.At present,research on TC intensity prediction takes atmospheric reanalysis data as the research object and mines the relationship between TC-related environmental factors and intensity through deep learning.However,reanalysis data are non-real-time in nature,which does not meet the requirements for operational forecasting applications.Therefore,a TC intensity prediction model named TC-Rolling is proposed,which can simultaneously extract the degree of symmetry for strong TC convective cloud and convection intensity,and fuse the deviation-angle variance with satellite images to construct the correlation between TC convection structure and intensity.For TCs'complex dynamic processes,a convolutional neural network(CNN)is used to learn their temporal and spatial features.For real-time intensity estimation,multi-task learning acts as an implicit time-series enhancement.The model is designed with a rolling strategy that aims to moderate the long-term dependent decay problem and improve accuracy for short-term intensity predictions.Since multiple tasks are correlated,the loss function of 12 h and 24 h are corrected.After testing on a sample of TCs in the Northwest Pacific,with a 4.48 kt root-mean-square error(RMSE)of 6 h intensity prediction,5.78 kt for 12 h,and 13.94 kt for 24 h,TC records from official agencies are used to assess the validity of TC-Rolling.

Dynamic intelligent prediction approach for landslide displacement based on biological growth models and CNN-LSTM

Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Regression models and Neural network models,to perform multi-characteristic coupled displacement prediction because they fail to consider landslide creep characteristics.This paper integrates the creep characteristics of landslides with non-linear intelligent algorithms and proposes a dynamic intelligent landslide displacement prediction method based on a combination of the Biological Growth model(BG),Convolutional Neural Network(CNN),and Long ShortTerm Memory Network(LSTM).This prediction approach improves three different biological growth models,thereby effectively extracting landslide creep characteristic parameters.Simultaneously,it integrates external factors(rainfall and reservoir water level)to construct an internal and external comprehensive dataset for data augmentation,which is input into the improved CNN-LSTM model.Thereafter,harnessing the robust feature extraction capabilities and spatial translation invariance of CNN,the model autonomously captures short-term local fluctuation characteristics of landslide displacement,and combines LSTM's efficient handling of long-term nonlinear temporal data to improve prediction performance.An evaluation of the Liangshuijing landslide in the Three Gorges Reservoir Area indicates that BG-CNN-LSTM exhibits high prediction accuracy,excellent generalization capabilities when dealing with various types of landslides.The research provides an innovative approach to achieving the whole-process,realtime,high-precision displacement predictions for multicharacteristic coupled landslides.

A Machine Learning-Based Observational Constraint Correction Method for Seasonal Precipitation Prediction

Seasonal precipitation has always been a key focus of climate prediction.As a dynamic-statistical combined method,the existing observational constraint correction establishes a regression relationship between the numerical model outputs and historical observations,which can partly predict seasonal precipitation.However,solving a nonlinear problem through linear regression is significantly biased.This study implements a nonlinear optimization of an existing observational constrained correction model using a Light Gradient Boosting Machine(LightGBM)machine learning algorithm based on output from the Beijing National Climate Center Climate System Model(BCC-CSM)and station observations to improve the prediction of summer precipitation in China.The model was trained using a rolling approach,and LightGBM outperformed Linear Regression(LR),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost).Using parameter tuning to optimize the machine learning model and predict future summer precipitation using eight different predictors in BCC-CSM,the mean Anomaly Correlation Coefficient(ACC)score in the 2019–22 summer precipitation predictions was 0.17,and the mean Prediction Score(PS)reached 74.The PS score was improved by 7.87%and 6.63%compared with the BCC-CSM and the linear observational constraint approach,respectively.The observational constraint correction prediction strategy with LightGBM significantly and stably improved the prediction of summer precipitation in China compared to the previous linear observational constraint solution,providing a reference for flood control and drought relief during the flood season(summer)in China.

Rockburst prediction based on multi-featured drilling parameters and extreme tree algorithm for full-section excavated tunnel faces

The suddenness, uncertainty, and randomness of rockbursts directly affect the safety of tunnel construction. The prediction of rockbursts is a fundamental aspect of mitigating or even eliminating rockburst hazards. To address the shortcomings of the current rockburst prediction models, which have a limited number of samples and rely on manual test results as the majority of their input features, this paper proposes rockburst prediction models based on multi-featured drilling parameters of rock drilling jumbo. Firstly, four original drilling parameters, namely hammer pressure (Ph), feed pressure (Pf), rotation pressure (Pr), and feed speed (VP), together with the rockburst grades, were collected from 1093 rockburst cases. Then, a feature expansion investigation was performed based on the four original drilling parameters to establish a drilling parameter feature system and a rockburst prediction database containing 42 features. Furthermore, rockburst prediction models based on multi-featured drilling parameters were developed using the extreme tree (ET) algorithm and Bayesian optimization. The models take drilling parameters as input parameters and rockburst grades as output parameters. The effects of Bayesian optimization and the number of drilling parameter features on the model performance were analyzed using the accuracy, precision, recall and F1 value of the prediction set as the model performance evaluation indices. The results show that the Bayesian optimized model with 42 drilling parameter features as inputs performs best, with an accuracy of 91.89%. Finally, the reliability of the models was validated through field tests.

Data driven prediction of fragment velocity distribution under explosive loading conditions

This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key design parameters including casing dimensions and detonation positions.The paper details the finite element analysis for fragmentation,the characterizations of the dynamic hardening and fracture models,the generation of comprehensive datasets,and the training of the ANN model.The results show the influence of casing dimensions on fragment velocity distributions,with the tendencies indicating increased resultant velocity with reduced thickness,increased length and diameter.The model's predictive capability is demonstrated through the accurate predictions for both training and testing datasets,showing its potential for the real-time prediction of fragmentation performance.

Short-Term Photovoltaic Power Prediction Based onMulti-Stage Temporal Feature Learning

Harnessing solar power is essential for addressing the dual challenges of global warming and the depletion of traditional energy sources.However,the fluctuations and intermittency of photovoltaic(PV)power pose challenges for its extensive incorporation into power grids.Thus,enhancing the precision of PV power prediction is particularly important.Although existing studies have made progress in short-term prediction,issues persist,particularly in the underutilization of temporal features and the neglect of correlations between satellite cloud images and PV power data.These factors hinder improvements in PV power prediction performance.To overcome these challenges,this paper proposes a novel PV power prediction method based on multi-stage temporal feature learning.First,the improved LSTMand SA-ConvLSTMare employed to extract the temporal feature of PV power and the spatial-temporal feature of satellite cloud images,respectively.Subsequently,a novel hybrid attention mechanism is proposed to identify the interplay between the two modalities,enhancing the capacity to focus on the most relevant features.Finally,theTransformermodel is applied to further capture the short-termtemporal patterns and long-term dependencies within multi-modal feature information.The paper also compares the proposed method with various competitive methods.The experimental results demonstrate that the proposed method outperforms the competitive methods in terms of accuracy and reliability in short-term PV power prediction.

Early prediction cardiac arrest in intensive care units:the value of laboratory indicator trends

The incidence of in-hospital cardiac arrest (IHCA) has increased over the past decade,with more than half occurring in intensive care units (ICUs).^([1])ICU cardiac arrest (ICU-CA)presents unique challenges,with worse outcomes than those in monitored wards,highlighting the need for early detection and intervention.^([2])Up to 80%of patients exhibit signs of deterioration hours before IHCA.^([3])Although early warning scores based on vital signs are useful,their eff ectiveness in ICUs is limited due to abnormal physiological parameters.^([4])Laboratory markers,such as sodium,potassium,and lactate,are predictive of poor outcomes,^([5])but static measurements may not capture the patient’s trajectory.Trends in laboratory indicators,such as variability and extremes,may offer better predictive value.^([6])This study aimed to evaluate ICU-CA predictive factors,with a focus on vital signs and trends of laboratory indicators.

Risk factors for biometry prediction error by Barrett Universal II intraocular lens formula in Chinese patients

AIM:To investigate the influence of postoperative intraocular lens(IOL)positions on the accuracy of cataract surgery and examine the predictive factors of postoperative biometry prediction errors using the Barrett Universal II(BUII)IOL formula for calculation.METHODS:The prospective study included patients who had undergone cataract surgery performed by a single surgeon from June 2020 to April 2022.The collected data included the best-corrected visual acuity(BCVA),corneal curvature,preoperative and postoperative central anterior chamber depths(ACD),axial length(AXL),IOL power,and refractive error.BUII formula was used to calculate the IOL power.The mean absolute error(MAE)was calculated,and all the participants were divided into two groups accordingly.Independent t-tests were applied to compare the variables between groups.Logistic regression analysis was used to analyze the influence of age,AXL,corneal curvature,and preoperative and postoperative ACD on MAE.RESULTS:A total of 261 patients were enrolled.The 243(93.1%)and 18(6.9%)had postoperative MAE<1 and>1 D,respectively.The number of females was higher in patients with MAE>1 D(χ^(2)=3.833,P=0.039).The postoperative BCVA(logMAR)of patients with MAE>1 D was significantly worse(t=-2.448;P=0.025).After adjusting for gender in the logistic model,the risk of postoperative refractive errors was higher in patients with a shallow postoperative anterior chamber[odds ratio=0.346;95% confidence interval(CI):0.164,0.730,P=0.005].CONCLUSION:Risk factors for biometry prediction error after cataract surgery include the patient’s sex and postoperative ACD.Patients with a shallow postoperative anterior chamber are prone to have refractive errors.

Enhancing rectal cancer liver metastasis prediction:Magnetic resonance imaging-based radiomics,bias mitigation,and regulatory considerations

In this article,we comment on the article by Long et al published in the recent issue of the World Journal of Gastrointestinal Oncology.Rectal cancer patients are at risk for developing metachronous liver metastasis(MLM),yet early prediction remains challenging due to variations in tumor heterogeneity and the limitations of traditional diagnostic methods.Therefore,there is an urgent need for noninvasive techniques to improve patient outcomes.Long et al’s study introduces an innovative magnetic resonance imaging(MRI)-based radiomics model that integrates high-throughput imaging data with clinical variables to predict MLM.The study employed a 7:3 split to generate training and validation datasets.The MLM prediction model was constructed using the training set and subsequently validated on the validation set using area under the curve(AUC)and dollar-cost averaging metrics to assess performance,robustness,and generalizability.By employing advanced algorithms,the model provides a non-invasive solution to assess tumor heterogeneity for better metastasis prediction,enabling early intervention and personalized treatment planning.However,variations in MRI parameters,such as differences in scanning resolutions and protocols across facilities,patient heterogeneity(e.g.,age,comorbidities),and external factors like carcinoembryonic antigen levels introduce biases.Additionally,confounding factors such as diagnostic staging methods and patient comorbidities require further validation and adjustment to ensure accuracy and generalizability.With evolving Food and Drug Administration regulations on machine learning models in healthcare,compliance and careful consideration of these regulatory requirements are essential to ensuring safe and effective implementation of this approach in clinical practice.In the future,clinicians may be able to utilize datadriven,patient-centric artificial intelligence(AI)-enhanced imaging tools integrated with clinical data,which would help improve early detection of MLM and optimize personalized treatment strategies.Combining radiomics,genomics,histological data,and demographic information can significantly enhance the accuracy and precision of predictive models.

An attention-based teacher-student model for multivariate short-term landslide displacement prediction incorporating weather forecast data

Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and property.However,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same time.In order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MIC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement prediction.Through MIC,a suitable input sequence length is selected for the LSTM model.To investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different lengths.The teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration(CMA)and Reliable Prognosis(https://rp5.ru)to improve the model’s expression capability,and the student model learns sequence information from other time series.An attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention mode.Finally,the predicted displacement is obtained through a linear layer.The proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention models.It achieves a mean absolute error(MAE)of 0.072 mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PCCS)of 0.85.Additionally,it exhibits enhanced prediction stability and interpretability,rendering it an indispensable tool for landslide disaster prevention and mitigation.

An Integrated Analysis of Yield Prediction Models:A Comprehensive Review of Advancements and Challenges

The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research.Deep learning(DL)and machine learning(ML)models effectively deal with such challenges.This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024.In addition,it analyses the effectiveness of various input parameters considered in crop yield prediction models.We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield.The total number of articles reviewed for crop yield prediction using ML,meta-modeling(Crop models coupled with ML/DL),and DL-based prediction models and input parameter selection is 125.We conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research papers.Each study is assessed based on the crop type,input parameters employed for prediction,the modeling techniques adopted,and the evaluation metrics used for estimatingmodel performance.We also discuss the ethical and social impacts of AI on agriculture.However,various approaches presented in the scientific literature have delivered impressive predictions,they are complicateddue to intricate,multifactorial influences oncropgrowthand theneed for accuratedata-driven models.Therefore,thorough research is required to deal with challenges in predicting agricultural output.

A deep multimodal fusion and multitasking trajectory prediction model for typhoon trajectory prediction to reduce flight scheduling cancellation

Natural events have had a significant impact on overall flight activity,and the aviation industry plays a vital role in helping society cope with the impact of these events.As one of the most impactful weather typhoon seasons appears and continues,airlines operating in threatened areas and passengers having travel plans during this time period will pay close attention to the development of tropical storms.This paper proposes a deep multimodal fusion and multitasking trajectory prediction model that can improve the reliability of typhoon trajectory prediction and reduce the quantity of flight scheduling cancellation.The deep multimodal fusion module is formed by deep fusion of the feature output by multiple submodal fusion modules,and the multitask generation module uses longitude and latitude as two related tasks for simultaneous prediction.With more dependable data accuracy,problems can be analysed rapidly and more efficiently,enabling better decision-making with a proactive versus reactive posture.When multiple modalities coexist,features can be extracted from them simultaneously to supplement each other’s information.An actual case study,the typhoon Lichma that swept China in 2019,has demonstrated that the algorithm can effectively reduce the number of unnecessary flight cancellations compared to existing flight scheduling and assist the new generation of flight scheduling systems under extreme weather.

A HEVC Video Steganalysis Method Using the Optimality of Motion Vector Prediction

Among steganalysis techniques,detection against MV(motion vector)domain-based video steganography in the HEVC(High Efficiency Video Coding)standard remains a challenging issue.For the purpose of improving the detection performance,this paper proposes a steganalysis method that can perfectly detectMV-based steganography in HEVC.Firstly,we define the local optimality of MVP(Motion Vector Prediction)based on the technology of AMVP(Advanced Motion Vector Prediction).Secondly,we analyze that in HEVC video,message embedding either usingMVP index orMVD(Motion Vector Difference)may destroy the above optimality of MVP.And then,we define the optimal rate of MVP as a steganalysis feature.Finally,we conduct steganalysis detection experiments on two general datasets for three popular steganographymethods and compare the performance with four state-ofthe-art steganalysis methods.The experimental results demonstrate the effectiveness of the proposed feature set.Furthermore,our method stands out for its practical applicability,requiring no model training and exhibiting low computational complexity,making it a viable solution for real-world scenarios.

A VSFPWM Strategy for High Frequency Torque Ripple Control of PMSM based on Stator Flux Ripple Prediction

Vector-controlled AC motor drives utilize pulse width modulation(PWM)to synthesize the desired output voltage of the voltage source inverter(VSI).In space vector PWM(SVPWM)techniques,the average realization of the space vector applying the volt-sec balance principle results in an instantaneous error voltage that generates high frequency torque ripple.It may lead to an increase in motor vibration and acoustic noise.This article presents a high frequency torque ripple prediction model based on stator flux ripple and proposes a targeted designed variable switching frequency PWM(VSFPWM)strategy to diminish high frequency torque ripple.The switching frequency is dynamically adjusted according to the peak value of the predicted stator flux ripple to mitigate high frequency torque ripple.In contrast to existing strategies,the strategy outlined in this article directly suppresses high frequency torque ripple,thus remaining unaffected by inaccurate motor parameters.Additionally,due to the introduction of the power factor angle,the proposed strategy can better adapt to the full speed range operating conditions of the motor.Detailed simulations and experiments are provided to validate the effectiveness of the proposed strategy.

Photovoltaic Power Generation Power Prediction under Major Extreme Weather Based on VMD-KELM

The output of photovoltaic power stations is significantly affected by environmental factors,leading to intermittent and fluctuating power generation.With the increasing frequency of extreme weather events due to global warming,photovoltaic power stations may experience drastic reductions in power generation or even complete shutdowns during such conditions.The integration of these stations on a large scale into the power grid could potentially pose challenges to systemstability.To address this issue,in this study,we propose a network architecture based on VMDKELMfor predicting the power output of photovoltaic power plants during severe weather events.Initially,a grey relational analysis is conducted to identify key environmental factors influencing photovoltaic power generation.Subsequently,GMM clustering is utilized to classify meteorological data points based on their probabilities within different Gaussian distributions,enabling comprehensive meteorological clustering and extraction of significant extreme weather data.The data are decomposed using VMD to Fourier transform,followed by smoothing processing and signal reconstruction using KELM to forecast photovoltaic power output under major extreme weather conditions.The proposed prediction scheme is validated by establishing three prediction models,and the predicted photovoltaic output under four major extreme weather conditions is analyzed to assess the impact of severe weather on photovoltaic power station output.The experimental results show that the photovoltaic power output under conditions of dust storms,thunderstorms,solid hail precipitation,and snowstorms is reduced by 68.84%,42.70%,61.86%,and 49.92%,respectively,compared to that under clear day conditions.The photovoltaic power prediction accuracies,in descending order,are dust storms,solid hail precipitation,thunderstorms,and snowstorms.

Mechanism prediction of Astragalus mongholicus Bunge and Angelica sinensis Diels in treating interstitial lung disease based on network pharmacology and molecular docking

Objective:To investigate the mechanism by which Astragalus mongholicus Bunge(AM),and Angelica sinensis Diels(AS)act in interstitial lung disease(ILD)based on computational prediction.Methods:We screened the ingredients of AM and AS in PubMed,the Web of Science,China National Knowledge Infrastructure(CNKI)Databases,etc.Then obtained the potential effective components.By sharing the same molecular with ILD,we got the possible target genes for ILD treatment and constructed components–targets–disease network with Cytoscape software.The CTD(Comparative Toxicogenomics Database)database was used for GO and KEGG enrichment analysis of these target genes.Results:59 active ingredients that can be druggable were chosen from AM,67 active ingredients were chosen from AS.77 overlapping target genes for AM and ILD and 36 overlapping target genes for AS and ILD were acquired.The hub targets of AM were PTGS2,PTGS1,CDK2,MAOA,ESR1,TOP2A,GSK3B,ESR2,PPARG,NOS2,The hub targets of AS were PTGS2,GABRA1,PTGS1,CHRM1,SLC6A2,ADRA1B,ADRAIA,ADRB2,CHRM3,GABRA2,CHRM2.Quercetin,kaempferol,daidzein,pavilion,7-Hydroxycoumarin,and 5-Hydroxycoumarin were the main active ingredients which have more effective targets.Prediction of the protein-protein interaction network showed PTGS2,GSK3B,PPARG,etc.,were the important predicted targets.The enriched KEGG pathways,including the Immune System,Metabolism of lipids and lipoproteins,Cytokine Signaling in the Immune system,Generic Transcription Pathway,The interleukin pathway,Metabolism of proteins,PI3K-Akt signaling pathway,Metabolic pathways,Innate Immune System,Neuroactive ligand-receptor interaction,Metabolism,GPCR downstream signaling,Amine ligand-binding receptors,Class A/1,Calcium signaling pathway.Molecular docking showed that quercetin,kaempferol,daidzein,pavilion,7-Hydroxycoumarin,5-Hydroxycoumarin had good binding activities with PTGS2 and GSK3B,which mainly mediated PI3K/Akt and other important signaling pathways in the pathogenesis of ILD.Conclusion:The components in AS and AM share some common targets,such as PTGS2.AM and AS may ameliorate ILD through the PI3K-Akt signaling pathway which is mediated by GSK3B.PTGS2,PPARG may also be vital target genes in the treatment of ILD with AM and AS.

Classifying rockburst with confidence:A novel conformal prediction approach

The scientific community recognizes the seriousness of rockbursts and the need for effective mitigation measures.The literature reports various successful applications of machine learning(ML)models for rockburst assessment;however,a significant question remains unanswered:How reliable are these models,and at what confidence level are classifications made?Typically,ML models output single rockburst grade even in the face of intricate and out-of-distribution samples,without any associated confidence value.Given the susceptibility of ML models to errors,it becomes imperative to quantify their uncertainty to prevent consequential failures.To address this issue,we propose a conformal prediction(CP)framework built on traditional ML models(extreme gradient boosting and random forest)to generate valid classifications of rockburst while producing a measure of confidence for its output.The proposed framework guarantees marginal coverage and,in most cases,conditional coverage on the test dataset.The CP was evaluated on a rockburst case in the Sanshandao Gold Mine in China,where it achieved high coverage and efficiency at applicable confidence levels.Significantly,the CP identified several“confident”classifications from the traditional ML model as unreliable,necessitating expert verification for informed decision-making.The proposed framework improves the reliability and accuracy of rockburst assessments,with the potential to bolster user confidence.

Prediction model for corrosion rate of low-alloy steels under atmospheric conditions using machine learning algorithms

This work constructed a machine learning(ML)model to predict the atmospheric corrosion rate of low-alloy steels(LAS).The material properties of LAS,environmental factors,and exposure time were used as the input,while the corrosion rate as the output.6 dif-ferent ML algorithms were used to construct the proposed model.Through optimization and filtering,the eXtreme gradient boosting(XG-Boost)model exhibited good corrosion rate prediction accuracy.The features of material properties were then transformed into atomic and physical features using the proposed property transformation approach,and the dominant descriptors that affected the corrosion rate were filtered using the recursive feature elimination(RFE)as well as XGBoost methods.The established ML models exhibited better predic-tion performance and generalization ability via property transformation descriptors.In addition,the SHapley additive exPlanations(SHAP)method was applied to analyze the relationship between the descriptors and corrosion rate.The results showed that the property transformation model could effectively help with analyzing the corrosion behavior,thereby significantly improving the generalization ability of corrosion rate prediction models.

Uncertainties in landslide susceptibility prediction:Influence rule of different levels of errors in landslide spatial position

The accuracy of landslide susceptibility prediction(LSP)mainly depends on the precision of the landslide spatial position.However,the spatial position error of landslide survey is inevitable,resulting in considerable uncertainties in LSP modeling.To overcome this drawback,this study explores the influence of positional errors of landslide spatial position on LSP uncertainties,and then innovatively proposes a semi-supervised machine learning model to reduce the landslide spatial position error.This paper collected 16 environmental factors and 337 landslides with accurate spatial positions taking Shangyou County of China as an example.The 30e110 m error-based multilayer perceptron(MLP)and random forest(RF)models for LSP are established by randomly offsetting the original landslide by 30,50,70,90 and 110 m.The LSP uncertainties are analyzed by the LSP accuracy and distribution characteristics.Finally,a semi-supervised model is proposed to relieve the LSP uncertainties.Results show that:(1)The LSP accuracies of error-based RF/MLP models decrease with the increase of landslide position errors,and are lower than those of original data-based models;(2)70 m error-based models can still reflect the overall distribution characteristics of landslide susceptibility indices,thus original landslides with certain position errors are acceptable for LSP;(3)Semi-supervised machine learning model can efficiently reduce the landslide position errors and thus improve the LSP accuracies.