Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models

Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes.In addition to PB models,deep learning(DL)models have been widely used in SM predictions recently.However,few pure DL models have notably high success rates due to lacking physical information.Thus,we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM predictions.To this end,we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale(attention model).We further built an ensemble model that combined the advantages of different hybrid schemes(ensemble model).We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory(ConvLSTM)model for 1–16 days of SM predictions.The performances of the proposed hybrid models were investigated and compared with two existing hybrid models.The results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid models.Moreover,the ensemble model performed best among all hybrid models at all forecast time scales and different soil conditions.It is highlighted that the ensemble model outperformed the pure DL model over 79.5%of in situ stations for 16-day predictions.These findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.

Large-scale model testing of high-pressure grouting reinforcement for bedding slope with rapid-setting polyurethane

Bedding slope is a typical heterogeneous slope consisting of different soil/rock layers and is likely to slide along the weakest interface.Conventional slope protection methods for bedding slopes,such as retaining walls,stabilizing piles,and anchors,are time-consuming and labor-and energy-intensive.This study proposes an innovative polymer grout method to improve the bearing capacity and reduce the displacement of bedding slopes.A series of large-scale model tests were carried out to verify the effectiveness of polymer grout in protecting bedding slopes.Specifically,load-displacement relationships and failure patterns were analyzed for different testing slopes with various dosages of polymer.Results show the great potential of polymer grout in improving bearing capacity,reducing settlement,and protecting slopes from being crushed under shearing.The polymer-treated slopes remained structurally intact,while the untreated slope exhibited considerable damage when subjected to loads surpassing the bearing capacity.It is also found that polymer-cemented soils concentrate around the injection pipe,forming a fan-shaped sheet-like structure.This study proves the improvement of polymer grouting for bedding slope treatment and will contribute to the development of a fast method to protect bedding slopes from landslides.

Resistance of full-scale beams against close-in explosions.Numerical modeling and field tests

This paper explores the performances of a finite element simulation including four concrete models applied to a full-scale reinforced concrete beam subjected to blast loading. Field test data has been used to compare model results for each case. The numerical modelling has been, carried out using the suitable code LS-DYNA. This code integrates blast load routine(CONWEP) for the explosive description and four different material models for the concrete including: Karagozian & Case Concrete, Winfrith, Continuous Surface Cap Model and Riedel-Hiermaier-Thoma models, with concrete meshing based on 10, 15, and 20 mm. Six full-scale beams were tested: four of them used for the initial calibration of the numerical model and two more tests at lower scaled distances. For calibration, field data obtained employing pressure and accelerometers transducers were compared with the results derived from the numerical simulation. Damage surfaces and the shape of rupture in the beams have been used as references for comparison. Influence of the meshing on accelerations has been put in evidence and for some models the shape and size of the damage in the beams produced maximum differences around 15%. In all cases, the variations between material and mesh models are shown and discussed.

Dynamic response of mountain tunnel,bridge,and embankment along the Sichuan-Tibet transportation corridor to active fault based on model tests

The Sichuan-Tibet transportation corridor is prone to numerous active faults and frequent strong earthquakes.While extensive studies have individually explored the effect of active faults and strong earthquakes on different engineering structures,their combined effect remains unclear.This research employed multiple physical model tests to investigate the dynamic response of various engineering structures,including tunnels,bridges,and embankments,under the simultaneous influence of cumulative earthquakes and stick-slip misalignment of an active fault.The prototype selected for this study was the Kanding No.2 tunnel,which crosses the Yunongxi fault zone within the Sichuan-Tibet transportation corridor.The results demonstrated that the tunnel,bridge,and embankment exhibited amplification in response to the input seismic wave,with the amplification effect gradually decreasing as the input peak ground acceleration(PGA)increased.The PGAs of different engineering structures were weakened by the fault rupture zone.Nevertheless,the misalignment of the active fault may decrease the overall stiffness of the engineering structure,leading to more severe damage,with a small contribution from seismic vibration.Additionally,the seismic vibration effect might be enlarged with the height of the engineering structure,and the tunnel is supposed to have a smaller PGA and lower dynamic earth pressure compared to bridges and embankments in strong earthquake zones crossing active faults.The findings contribute valuable insights for evaluating the dynamic response of various engineering structures crossing an active fault and provide an experimental reference for secure engineering design in the challenging conditions of the Sichuan-Tibet transportation corridor.

Explosion resistance performance of reinforced concrete box girder coated with polyurea:Model test and numerical simulation

To study the anti-explosion protection effect of polyurea coating on reinforced concrete box girder,two segmental girder specimens were made at a scale of 1:3,numbered as G(without polyurea coating)and PCG(with polyurea coating).The failure characteristics and dynamic responses of the specimens were compared through conducting explosion tests.The reliability of the numerical simulation using LS-DYNA software was verified by the test results.The effects of different scaled distances,reinforcement ratios,concrete strengths,coating thicknesses and ranges of polyurea were studied.The results show that the polyurea coating can effectively enhance the anti-explosion performance of the girder.The top plate of middle chamber in specimen G forms an elliptical penetrating hole,while that in specimen PCG only shows a very slight local dent.The peak vertical displacement and residual displacement of PCG decrease by 74.8% and 73.7%,respectively,compared with those of specimen G.For the TNT explosion with small equivalent,the polyurea coating has a more significant protective effect on reducing the size of fracture.With the increase of TNT equivalent,the protective effect of polyurea on reducing girder displacement becomes more significant.The optimal reinforcement ratio,concrete strength,thickness and range of polyurea coating were also drawn.

Rainfall-triggered waste dump instability analysis based on surface 3D deformation in physical model test

Landslide is the second largest natural disaster after earthquake. It is of significance to study the evolution laws and failure mechanism of landslides based on its surface 3D deformation information. Based on the rainfall-triggered waste dump instability model test, we studied the failure mechanisms of the waste dump by integrating surface deformation and internal slope stress and proposed novel parameters for identifying landslide stability. We developed a noncontact measurement device, which can obtain millimeter-level 3D deformation data for surface scene in physical model test;Then we developed the similar materials and established a test model for a waste dump. Based on the failure characteristics of slope surface, internal stress of slope body and displacement contours during the whole process, we divided the slope instability process in model test into four stages: rainfall infiltration and surface erosion, shallow sliding, deep sliding, and overall instability. Based on the obtained surface deformation data, we calculated the volume change during slope instability process and compared it with the point displacement on slope surface. The results showed that the volume change can not only reflect the slow-ultra acceleration process of slope failure, but also fully reflect the above four stages and reduce the fluctuations caused by random factors. Finally, this paper proposed two stability identification parameters: the volume change rate above the slip surface and the relative velocity of volume change rate. According to the calculation of these two parameters in model test, they can be used for study the deformation and failure mechanism of slope stability.

Comparison among the UECM Model, and the Composite Model in Forecasting Malaysian Imports

For more than a century, forecasting models have been crucial in a variety of fields. Models can offer the most accurate forecasting outcomes if error terms are normally distributed. Finding a good statistical model for time series predicting imports in Malaysia is the main target of this study. The decision made during this study mostly addresses the unrestricted error correction model (UECM), and composite model (Combined regression—ARIMA). The imports of Malaysia from the first quarter of 1991 to the third quarter of 2022 are employed in this study’s quarterly time series data. The forecasting outcomes of the current study demonstrated that the composite model offered more probabilistic data, which improved forecasting the volume of Malaysia’s imports. The composite model, and the UECM model in this study are linear models based on responses to Malaysia’s imports. Future studies might compare the performance of linear and nonlinear models in forecasting.

Determination of Material Parameters of EVA Foam under Uniaxial Compressive Testing Using Hyperelastic Models

The objective of this research was to determine the mechanical parameter from EVA foam and also investigate its behavior by using Blatz-Ko,Neo-Hookean,Mooney model and experimental test.The physical characteristic of EVA foam was also evaluated by scanning electron microscopy(SEM).The results show that Blatz-Ko and Neo-Hookean model can fit the curve at 5%and 8%strain,respectively.The Mooney model can fit the curve at 50%strain.The modulus of rigidity evaluated from Mooney model is 0.0814±0.0027 MPa.The structure of EVA foam from SEM image shows that EVA structure is a closed cell with homogeneous porous structure.From the result,it is found that Mooney model can adjust the data better than other models.This model can be applied for mechanical response prediction of EVA foam and also for reference value in engineering application.

Physical model test and application of 3D printing rock-like specimens to laminated rock tunnels

Weak structural plane deformation is responsible for the non-uniform large deformation disasters in layered rock tunnels,resulting in steel arch distortion and secondary lining cracking.In this study,a servo biaxial testing system was employed to conduct physical modeling tests on layered rock tunnels with bedding planes of varying dip angles.The influence of structural anisotropy in layered rocks on the micro displacement and strain field of surrounding rocks was analyzed using digital image correlation(DIC)technology.The spatiotemporal evolution of non-uniform deformation of surrounding rocks was investigated,and numerical simulation was performed to verify the experimental results.The findings indicate that the displacement and strain field of the surrounding layered rocks are all maximized at the horizontal bedding planes and decrease linearly with the increasing dip angle.The failure of the layered surrounding rock with different dip angles occurs and extends along the bedding planes.Compressive strain failure occurs after excavation under high horizontal stress.This study provides significant theoretical support for the analysis,prediction,and control of non-uniform deformation of tunnel surrounding rocks.

Novel Hybrid X GBoost Model to Forecast Soil Shear Strength Based on Some Soil Index Tests

When building geotechnical constructions like retaining walls and dams is of interest,one of the most important factors to consider is the soil’s shear strength parameters.This study makes an effort to propose a novel predictive model of shear strength.The study implements an extreme gradient boosting(XGBoost)technique coupled with a powerful optimization algorithm,the salp swarm algorithm(SSA),to predict the shear strength of various soils.To do this,a database consisting of 152 sets of data is prepared where the shear strength(τ)of the soil is considered as the model output and some soil index tests(e.g.,dry unit weight,water content,and plasticity index)are set as model inputs.Themodel is designed and tuned using both effective parameters of XGBoost and SSA,and themost accuratemodel is introduced in this study.Thepredictionperformanceof theSSA-XGBoostmodel is assessedbased on the coefficient of determination(R2)and variance account for(VAF).Overall,the obtained values of R^(2) and VAF(0.977 and 0.849)and(97.714%and 84.936%)for training and testing sets,respectively,confirm the workability of the developed model in forecasting the soil shear strength.To investigate the model generalization,the prediction performance of the model is tested for another 30 sets of data(validation data).The validation results(e.g.,R^(2) of 0.805)suggest the workability of the proposed model.Overall,findings suggest that when the shear strength of the soil cannot be determined directly,the proposed hybrid XGBoost-SSA model can be utilized to assess this parameter.

Preliminary Model Study for Forecasting a Hot Weather Process in Guangdong Province Using CMA-TRAMS

In this paper, the CMA-TRAMS tropical high-resolution system was used to forecast a typical hot weather process in Guangdong, China with different horizontal resolutions and surface coverage. The results of resolutions of 0.02° and 0.06° were presented with the same surface coverage of the GlobeLand30 V2020, companies with the results of resolution 0.02° with the USGS global surface coverage. The results showed that, on the overall assessment the 2 km model performed better in forecasting 2 m temperature, while the 6 km model was more accurate in predicting 10 m wind speed. In the evaluation of representative stations, the 2 km model performed better in forecasting 2 m temperature and 2 m relative humidity at the coastal stations, and the 2 km model was also better in forecasting 2 m pressure at the representative stations. However, the 6 km model performed better in forecasting 10 m wind speed at the representative stations. Furthermore, the 2 km model, owing to its higher horizontal resolution, presented a more detailed stratification of various meteorological field maps, allowing for a more pronounced simulation of local meteorological element variations. And the use of the surface coverage data of the GlobeLand30 V2020 improved the forecasting of 2 m temperature, and 10 m wind speed compared to the USGS surface coverage data.

A Hybrid Model Evaluation Based on PCA Regression Schemes Applied to Seasonal Precipitation Forecast

Possible changes in the structure and seasonal variability of the subtropical ridge may lead to changes in the rainfall’s variability modes over Caribbean region. This generates additional difficulties around water resource planning, therefore, obtaining seasonal prediction models that allow these variations to be characterized in detail, it’s a concern, specially for island states. This research proposes the construction of statistical-dynamic models based on PCA regression methods. It is used as predictand the monthly precipitation accumulated, while the predictors (6) are extracted from the ECMWF-SEAS5 ensemble mean forecasts with a lag of one month with respect to the target month. In the construction of the models, two sequential training schemes are evaluated, obtaining that only the shorter preserves the seasonal characteristics of the predictand. The evaluation metrics used, where cell-point and dichotomous methodologies are combined, suggest that the predictors related to sea surface temperatures do not adequately represent the seasonal variability of the predictand, however, others such as the temperature at 850 hPa and the Outgoing Longwave Radiation are represented with a good approximation regardless of the model chosen. In this sense, the models built with the nearest neighbor methodology were the most efficient. Using the individual models with the best results, an ensemble is built that allows improving the individual skill of the models selected as members by correcting the underestimation of precipitation in the dynamic model during the wet season, although problems of overestimation persist for thresholds lower than 50 mm.

A data assimilation-based forecast model of outer radiation belt electron fluxes

Because radiation belt electrons can pose a potential threat to the safety of satellites orbiting in space,it is of great importance to develop a reliable model that can predict the highly dynamic variations in outer radiation belt electron fluxes.In the present study,we develop a forecast model of radiation belt electron fluxes based on the data assimilation method,in terms of Van Allen Probe measurements combined with three-dimensional radiation belt numerical simulations.Our forecast model can cover the entire outer radiation belt with a high temporal resolution(1 hour)and a spatial resolution of 0.25 L over a wide range of both electron energy(0.1-5.0 MeV)and pitch angle(5°-90°).On the basis of this model,we forecast hourly electron fluxes for the next 1,2,and 3 days during an intense geomagnetic storm and evaluate the corresponding prediction performance.Our model can reasonably predict the stormtime evolution of radiation belt electrons with high prediction efficiency(up to~0.8-1).The best prediction performance is found for~0.3-3 MeV electrons at L=~3.25-4.5,which extends to higher L and lower energies with increasing pitch angle.Our results demonstrate that the forecast model developed can be a powerful tool to predict the spatiotemporal changes in outer radiation belt electron fluxes,and the model has both scientific significance and practical implications.

Comparative Analysis of Machine Learning Models for PDF Malware Detection:Evaluating Different Training and Testing Criteria

The proliferation of maliciously coded documents as file transfers increase has led to a rise in sophisticated attacks.Portable Document Format(PDF)files have emerged as a major attack vector for malware due to their adaptability and wide usage.Detecting malware in PDF files is challenging due to its ability to include various harmful elements such as embedded scripts,exploits,and malicious URLs.This paper presents a comparative analysis of machine learning(ML)techniques,including Naive Bayes(NB),K-Nearest Neighbor(KNN),Average One Dependency Estimator(A1DE),RandomForest(RF),and SupportVectorMachine(SVM)forPDFmalware detection.The study utilizes a dataset obtained from the Canadian Institute for Cyber-security and employs different testing criteria,namely percentage splitting and 10-fold cross-validation.The performance of the techniques is evaluated using F1-score,precision,recall,and accuracy measures.The results indicate that KNNoutperforms other models,achieving an accuracy of 99.8599%using 10-fold cross-validation.The findings highlight the effectiveness of ML models in accurately detecting PDF malware and provide insights for developing robust systems to protect against malicious activities.

Analysis of Spatial and Temporal Variation and Forecast Model of Sandstorm Weather in Ulanqab City

Based on the data of sandstorm at 11 stations in Ulanqab City from 1990 to 2021,the spatial and temporal variation characteristics of sand-storm weather were analyzed firstly,and then the conceptual models of cold front and Mongolian cyclone sandstorm were obtained by analyzing sandstorm cases.Finally,the forecast points of the two types of sandstorm weather were given to provide some scientific basis and reference for the prediction of local sandstorm weather in the future.

Intermittent disturbance mechanical behavior and fractional deterioration mechanical model of rock under complex true triaxial stress paths

Mechanical excavation,blasting,adjacent rockburst and fracture slip that occur during mining excavation impose dynamic loads on the rock mass,leading to further fracture of damaged surrounding rock in three-dimensional high-stress and even causing disasters.Therefore,a novel complex true triaxial static-dynamic combined loading method reflecting underground excavation damage and then frequent intermittent disturbance failure is proposed.True triaxial static compression and intermittent disturbance tests are carried out on monzogabbro.The effects of intermediate principal stress and amplitude on the strength characteristics,deformation characteristics,failure characteristics,and precursors of monzogabbro are analyzed,intermediate principal stress and amplitude increase monzogabbro strength and tensile fracture mechanism.Rapid increases in microseismic parameters during rock loading can be precursors for intermittent rock disturbance.Based on the experimental result,the new damage fractional elements and method with considering crack initiation stress and crack unstable stress as initiation and acceleration condition of intermittent disturbance irreversible deformation are proposed.A novel three-dimensional disturbance fractional deterioration model considering the intermediate principal stress effect and intermittent disturbance damage effect is established,and the model predicted results align well with the experimental results.The sensitivity of stress states and model parameters is further explored,and the intermittent disturbance behaviors at different f are predicted.This study provides valuable theoretical bases for the stability analysis of deep mining engineering under dynamic loads.

A Parallel Hybrid Testing Technique for Tri-Programming Model-Based Software Systems

Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple levels.Combining different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve performance.During the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these errors.Numerous studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming models.Despite the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been developed.Therefore,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot detect.For the first time,this paper presents a classification of operational errors that can result from the integration of the three models.This paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and OpenACC.This hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic technology.The hybrid technique can detect more errors because it combines two distinct technologies.The proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environment are left to the dynamic technology,which completes the validation.

Statistical Time Series Forecasting Models for Pandemic Prediction

COVID-19 has significantly impacted the growth prediction of a pandemic,and it is critical in determining how to battle and track the disease progression.In this case,COVID-19 data is a time-series dataset that can be projected using different methodologies.Thus,this work aims to gauge the spread of the outbreak severity over time.Furthermore,data analytics and Machine Learning(ML)techniques are employed to gain a broader understanding of virus infections.We have simulated,adjusted,and fitted several statistical time-series forecasting models,linearML models,and nonlinear ML models.Examples of these models are Logistic Regression,Lasso,Ridge,ElasticNet,Huber Regressor,Lasso Lars,Passive Aggressive Regressor,K-Neighbors Regressor,Decision Tree Regressor,Extra Trees Regressor,Support Vector Regressions(SVR),AdaBoost Regressor,Random Forest Regressor,Bagging Regressor,AuoRegression,MovingAverage,Gradient Boosting Regressor,Autoregressive Moving Average(ARMA),Auto-Regressive Integrated Moving Averages(ARIMA),SimpleExpSmoothing,Exponential Smoothing,Holt-Winters,Simple Moving Average,Weighted Moving Average,Croston,and naive Bayes.Furthermore,our suggested methodology includes the development and evaluation of ensemble models built on top of the best-performing statistical and ML-based prediction methods.A third stage in the proposed system is to examine three different implementations to determine which model delivers the best performance.Then,this best method is used for future forecasts,and consequently,we can collect the most accurate and dependable predictions.

A model‑free approach to do long‑term volatility forecasting and its variants

Volatility forecasting is important in financial econometrics and is mainly based on the application of various GARCH-type models.However,it is difficult to choose a specific GARCH model that works uniformly well across datasets,and the traditional methods are unstable when dealing with highly volatile or short-sized datasets.The newly pro-posed normalizing and variance stabilizing(NoVaS)method is a more robust and accu-rate prediction technique that can help with such datasets.This model-free method was originally developed by taking advantage of an inverse transformation based on the frame of the ARCH model.In this study,we conduct extensive empirical and simu-lation analyses to investigate whether it provides higher-quality long-term volatility forecasting than standard GARCH models.Specifically,we found this advantage to be more prominent with short and volatile data.Next,we propose a variant of the NoVaS method that possesses a more complete form and generally outperforms the current state-of-the-art NoVaS method.The uniformly superior performance of NoVaS-type methods encourages their wide application in volatility forecasting.Our analyses also highlight the flexibility of the NoVaS idea that allows the exploration of other model structures to improve existing models or solve specific prediction problems.

Impacts of Increasing Model Resolutions and Shortening Forecast Lead Times on QPFs in South China During the Rainy Season

This study investigated the impacts of increasing model resolutions and shortening forecast lead times on the quantitative precipitation forecast(QPF)for heavy-rainfall events over south China during the rainy seasons in 2013-2020.The control experiment,where the analysis-forecast cycles run with model resolutions of about 3 km,was compared to a lower-resolution experiment with model resolutions of about 9 km,and a longer-term experiment activated 12 hours earlier.Rainfall forecasting in the presummer rainy season was significantly improved by improving model resolutions,with more improvements in cases with stronger synoptic-scale forcings.This is partially attributed to the improved initial conditions(ICs)and subsequent forecasts for low-level jets(LLJs).Forecasts of heavy rainfall induced by landfalling tropical cyclones(TCs)benefited from increasing model resolutions in the first 6 hours.Forecast improvements in rainfall due to shortening forecast lead times were more significant at earlier(1-6 h)and later(7-12 h)lead times for cases with stronger and weaker synoptic-scale forcings,respectively,due to the area-and case-dependent improvements in ICs for nonprecipitation variables.Specifically,significant improvements mainly presented over the northern South China Sea for low-level onshore wind of weak-forcing cases but over south China for LLJs of strong-forcing cases during the presummer rainy season,and over south China for all the nonprecipitation variables above the surface during the TC season.However,some disadvantages of higher-resolution and shorter-term forecasts in QPFs highlight the importance of developing ensemble forecasting with proper IC perturbations,which include the complementary advantages of lower-resolution and longer-term forecasts.