A Comparative Study of Two Tree-Based Models for Predicting Flyrock Velocity at Open Pit Bench Mining

Blasting is a cost-effective technique to break hard rock volumes by using explosives in the mining and civil engineering realms. Moreover, although blasting is a designed process and plays an indispensable role in these industries, it can also have multiple adverse environmental impacts. One such effect is flyrock, which poses risks to nearby machinery, and residential structures, and can even lead to injuries or fatalities. To optimize blasting efficiency as well as restrict side effects, prediction of the blast aftereffects is vital. Therefore, the present work focuses on using two machine learning methods to predict the velocity of flyrock in the open pit mine. To address this issue, a comprehensive dataset was gathered from the open pit mine. Then, Decision Tree and Random Forest algorithms were employed to predict flyrock velocity. The Random Forest model demonstrated superior performance compared to the Decision Tree model. Nonetheless, the performance of the Decision Tree model was deemed satisfactory, as evidenced by its coefficient of determination value of 0.83, mean squared error (MSE) of 4.2, and mean absolute percentage error (MAPE) of 5.6%. Considering these metrics, it is reasonable to conclude that tree-based algorithms can be effective in predicting flyrock velocity.

Simulation of Runoff and Sediment Yield for a Kaneri Watershed Using SWAT Model

Watershed as an entry point acts as a beginning to address the issues of sustainable rainwater management for improving livelihoods. Extraction of watershed parameters using Geographical Information System (GIS) and use of simulation models is the current trend for hydrologic evaluation of watersheds. In the present study, the open Source Tool Quantum GIS 2.2.0 was used for preparation of maps to verify the spatial extent of the area. The Soil and Water Assessment Tool (SWAT) having an interface with Arc-View GIS software (ArcGIS 10.1 with Arc SWAT 2012 extension) was selected for the estimation of runoff and sediment yield from Kaneri watershed, located in Western Maharashtra region. The coefficient of determination (R2) for the monthly and yearly runoff was obtained as 0.849 and 0.951 respectively for the calibration period 1979 to 2000 and 0.801 and 0.950 respectively for the validation period 2001-2013. The R2 value in estimating the monthly and yearly sediment yield during calibration period was computed as 0.722 and 0.788 respectively. The R2 for monthly and yearly sediment yield values for validation period was observed to be 0.565 and 0.684 respectively.

Development of Empirical Models for the Estimation of CBR Value of Soil from Their Index Properties: A Case Study of the Ogbia-Nembe Road in Niger Delta Region of Nigeria

This study developed empirical-mathematical models to predict the California Bearing Ratio (CBR) using soil index properties in Ogbia-Nembe road in the Niger Delta region of Nigeria. The determination of CBR of soil is a laborious operation that requires a longer time and materials leading to increased cost and schedule;this can be reduced by adopting an empirical-mathematical model that can predict the CBR using other simpler soil index properties such as Plastic Limit (PL), the Liquid Limit (LL), the Plasticity Index (PI) and the Moisture Content (MC), which are less laborious and take lesser time to obtain. Thirteen models were developed to understand the relationship between these soil index properties: the independent variable and the California Bearing Ratio (CBR): the dependent variable;Six linear, Six quadratic and One multiple linear regression models were developed for this relationship. Analysis of variance (ANOVA) on the thirteen models showed that the Optimum Moisture Content (OMC) and the Maximum Dry Density (MDD) are better independent variables for the prediction of the CBR value of Ogbia-Nembe soil generating a quadratic model and a multiple linear regression model having a better coefficient of determination R2 = 0.96 and 0.94 respectively, mean square error (MSE) of 0.74 and 1.152 respectively with Root mean square errors of 0.861 and 1.073 accordingly. These models were used to predict the CBR of the soil. The CBR values predicted by the model were further compared with those of the actual experimental test and found to be relatively consistent with minimal variance. This establishes that CBR of any soil can be predicted from the Index Property of the soil and this is more economical and takes lesser time and can be universally adopted for soil investigation.

Zeno’s Paradoxes and Lie Tzu’s Dichotomic Wisdom Explained with Alpha Beta (αβ) Asymptotic Nonlinear Math (Including One Example on Second Order Nonlinear Phenomena)

Zeno’s paradoxes are a set of philosophical problems that were first introduced by the ancient Greek philosopher Zeno of Elea. Here is the first attempt to use asymptotic approach and nonlinear concepts to address the paradoxes. Among the paradoxes, two of the most famous ones are Zeno’s Room Walk and Zeno’s Achilles. Lie Tsu’s pole halving dichotomy is also discussed in relation to these paradoxes. These paradoxes are first-order nonlinear phenomena, and we expressed them with the concepts of linear and nonlinear variables. In the new nonlinear concepts, variables are classified as either linear or nonlinear. Changes in linear variables are simple changes, while changes in nonlinear variables are nonlinear changes relative to their asymptotes. Continuous asymptotic curves are used to describe and derive the equations for expressing the relationship between two variables. For example, in Zeno’s Room Walk, the equations and curves for a person to walk from the initial wall towards the other wall are different from the equations and curves for a person to walk from the other wall towards the initial wall. One walk has a convex asymptotic curve with a nonlinear equation having two asymptotes, while the other walk has a concave asymptotic curve with a nonlinear equation having a finite starting number and a bottom asymptote. Interestingly, they have the same straight-line expression in a proportionality graph. The Appendix of this discussion includes an example of a second-order nonlinear phenomenon. .

Studying beta diversity: ecological variation partitioning by multiple regression and canonical analysis

Aims Beta diversity is the variation in species composition among sites in a geographic region.Beta diversity is a key concept for understanding the functioning of ecosystems,for the conservation of biodiversity and for ecosystem management.The present report describes how to analyse beta diversity from community composition and associated environmental and spatial data tables.Methods Beta diversity can be studied by computing diversity indices for each site and testing hypotheses about the factors that may explain the variation among sites.Alternatively,one can carry out a direct analysis of the community composition data table over the study sites,as a function of sets of environmental and spatial variables.These analyses are carried out by the statistical method of partitioning the variation of the diversity indices or the community composition data table with respect to environmental and spatial variables.Variation partitioning is briefly described herein.Important findings Variation partitioning is a method of choice for the interpretation of beta diversity using tables of environmental and spatial variables.Beta diversity is an interesting‘currency’for ecologists to compare either different sampling areas or different ecological communities cooccurring in an area.Partitioning must be based upon unbiased estimates of the variation of the community composition data table that is explained by the various tables of explanatory variables.The adjusted coefficient of determination provides such an unbiased estimate in both multiple regression and canonical redundancy analysis.After partitioning,one can test the significance of the fractions of interest and plot maps of the fitted values corresponding to these fractions.