NARX neural network approach for the monthly prediction of groundwater levels in Sylhet Sadar, Bangladesh

Groundwater is important for managing the water supply in agricultural countries like Bangladesh. Therefore, the ability to predict the changes of groundwater level is necessary for jointly planning the uses of groundwater resources. In this study, a new nonlinear autoregressive with exogenous inputs(NARX) network has been applied to simulate monthly groundwater levels in a well of Sylhet Sadar at a local scale. The Levenberg-Marquardt(LM) and Bayesian Regularization(BR) algorithms were used to train the NARX network, and the results were compared to determine the best architecture for predicting monthly groundwater levels over time. The comparison between LM and BR showed that NARX-BR has advantages over predicting monthly levels based on the Mean Squared Error(MSE), coefficient of determination(R^2), and Nash-Sutcliffe coefficient of efficiency(NSE). The results show that BR is the most accurate method for predicting groundwater levels with an error of ± 0.35 m. This method is applied to the management of irrigation water source, which provides important information for the prediction of local groundwater fluctuation at local level during a short period.

Artificial neural network based production forecasting for a hydrocarbon reservoir under water injection

As the conventional prediction methods for production of waterflooding reservoirs have some drawbacks, a production forecasting model based on artificial neural network was proposed, the simulation process by this method was presented, and some examples were illustrated. A workflow that involves a physics-based extraction of features was proposed for fluid production forecasting to improve the prediction effect. The Bayesian regularization algorithm was selected as the training algorithm of the model. This algorithm, although taking longer time, can better generalize oil, gas and water production data sets. The model was evaluated by calculating mean square error and determination coefficient, drawing error distribution histogram and the cross-plot between simulation data and verification data etc. The model structure was trained, validated and tested with 90% of the historical data, and blindly evaluated using the remaining. The predictive model consumes minimal information and computational cost and is capable of predicting fluid production rate with a coefficient of determination of more than 0.9, which has the simulation results consistent with the practical data.

Identifying spatiotemporal information of the point pollutant source indoors based on the adjoint-regularization method

Fast and accurate identification of the pollutant source location and release rate is important for improving indoor air quality.From the perspective of public health,identification of the airborne pathogen source in public buildings is particularly important for ensuring people’s safety and health.The existing adjoint probability method has difficulty in distinguishing the temporal source,and the optimization algorithm can only analyze a few potential sources in space.This study proposed an algorithm combining the adjoint-pulse and regularization methods to identify the spatiotemporal information of the point pollutant source in an entire room space.We first obtained a series of source-receptor response matrices using the adjoint-pulse method in the room based on the validated CFD model,and then used the regularization method and composite Bayesian inference to identify the release rate and location of the dynamic pollutant source.The results showed that the MAPEs(mean absolute percentage errors)of estimated source intensities were almost less than 15%,and the source localization success rates were above 25/30 in this study.This method has the potential to be used to identify the airborne pathogen source in public buildings combined with sensors for disease-specific biomarkers.

Reconstruction of incidence reporting rate for SARS-CoV-2 Delta variant of COVID-19 pandemic in the US

In recent years,advanced regularization techniques have emerged as a powerful tool aimed at stable estimation of infectious disease parameters that are crucial for future projections,prevention,and control.Unlike other system parameters,i.e.,incubation and recovery rates,the case reporting rate,Ψ,and the time-dependent effective reproduction number,R_(e)t,are directly influenced by a large number of factors making it impossible to pre-estimate these parameters in any meaningful way.In this study,we propose a novel iteratively-regularized trust-region optimization algorithm,combined with SuSvIuIvRD compartmental model,for stable reconstruction ofΨand R_(e)t from reported epidemic data on vaccination percentages,incidence cases,and daily deaths.The innovative regularization procedure exploits(and takes full advantage of)a unique structure of the Jacobian and Hessian approximation for the nonlinear observation operator.The proposed inversion method is thoroughly tested with synthetic and real SARS-CoV-2 Delta variant data for different regions in the United States of America from July 9,2021,to November 25,2021.Our study shows that case reporting rate during the Delta wave of COVID-19 pandemic in the US is between 12%and 37%,with most states being in the range from 15%to 25%.This confirms earlier accounts on considerable under-reporting of COVID-19 cases due to the impact of”silent spreaders”and the limitations of testing.

Structural finite element model updating using incomplete ambient vibration modal data

This paper presents an effective approach for updating finite element dynamic model from incomplete modal data identified from ambient vibration measurements.The proposed method is based on the relationship between the perturbation of structural parameters such as stiffness and mass changes and the modal data measurements of the tested structure such as measured mode shape readings.Structural updating parameters including both stiffness and mass parameters are employed to represent the differences in structural parameters between the finite element model and the associated tested structure.These updating parameters are then evaluated by an iterative solution procedure,giving optimised solutions in the least squares sense without requiring an optimisation technique.In order to reduce the influence of modal measurement uncertainty,the truncated singular value decomposition regularization method incorporating the quasi-optimality criterion is employed to produce reliable solutions for the structural updating parameters.Finally,the numerical investigations of a space frame structure and the practical applications to the Canton Tower benchmark problem demonstrate that the proposed method can correctly update the given finite element model using the incomplete modal data identified from the recorded ambient vibration measurements.

Further Improvements and Applications on a Theorem Due to Reich

In this paper, by using new analysis techniques, we have studied iterative construc- tion problem for finding zeros of accretive mappings in uniformly smooth Banach spaces, and improved a theorem due to Reich. As its application, we have deduced a strong convergence theorem of fixed points for continuous pseudo-contractions.