Missing data with gaps is always an obstacle to effective planning and management of water resources. Complete reliable hydrological time series are necessary for the optimal design A study was conducted fill in missing streamflow 54 observation stations across Turkey. This process done aid various statistical estimation methods. Estimations were performed by using Linear regression (LR), Artif...

Inspections have been shown to be an effective means of detecting defects early on in the software development life cycle. However, they are not always successful or beneficial as they are affected by a number of technical and managerial factors. One important aspect is to understand what are the factors that affect inspection effectiveness (the rate of detected defects) in a given environment,...

Credit-risk models that are designed for general application across sectors may not be suitable the construction industry, which has unique characteristics and financial risks require specialised modelling approaches. Moreover, advanced bankruptcy-prediction often used to achieve highest accuracy in large modern datasets. Therefore, aim of this research is creation enterprise-bankruptcy predict...

Little research can be found in relation to the stability of anisotropic and heterogenous soils three dimensions. In this paper, we propose a study on three-dimensional (3D) undrained slopes clay using advanced upper lower bounds finite element limit analysis (FELA). The obtained solutions are normalized, presented by number that is function geometrical ratios two material ratios, i.e. depth ra...

A work zone bottleneck in a roadway network can cause traffic delays, emissions and safety issues. Accurate measurement and prediction of work zone travel time can help travelers make better routing decisions and therefore mitigate its impact. Historically, data used for travel time analyses comes from fixed loop detectors, which are expensive to install and maintain. With connected vehicle tec...

Multivariate Adaptive Regression Spline (MARS) is a simple and powerful non-parametric machine learning algorithm that automatizes the selection of non-linear terms in regression models. In this study, we propose using MARS spatial framework to account for potential non-linearities effects Using relatively large data set 17,000 dwellings St. Petersburg (Russia), examine how works. The empirical...

Landscape metrics are widely used in landscape planning and land use management. Understanding how respond with scales can provide more accurate prediction information; however, ignoring the interference of multi-scale interaction may lead to a severe systemic bias. In this study, we quantitatively analyzed scaling sensitivity based on predict their optimal scale ranges. Using big data method, ...

Parallel computing enables the analysis of very large data sets using large collections of flexible models with many variables. The computational methods are based on ideas from computational linear algebra and can draw on the extensive research on parallel algorithms in this area. Many algorithms for the direct and iterative solution of penalised least squares problems and for updating can be ...

Prolonged and obstructed labor are the type of abnormal that may lead to maternal fetal mortality. This study established model for predicting prolonged in East Nusa Tenggara. A health facilities-based case-control was conducted November 2017 among 570 women who gave birth at public facilities Data were obtained by reviewing antenatal records, mother's card, partographs. In bivariate analysis, ...