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Mapping Impervious Cover in Catchments Using High Spatial Resolution Aerial Imagery
Published in Yeqiao Wang, Fresh Water and Watersheds, 2020
Jessica Morgan, Yeqiao Wang, Naomi Detenbeck
A regression analysis was run to identify the relationship between NLCD and NAIP data. The ratio of NLCD to NAIP IC was then compared to the NAIP data within the NHDPlus catchments to identify where the NLCD data are adequate and where they may be overestimating or underestimating IC. Classification and Regression Tree (CART) analyses were run using Systat 13.1 (Systat Software, Inc. San Jose, CA) to quantitatively assess the level at which the NLCD underestimates or overestimates IC. CART analysis compares independent and dependent variables through a series of binary splits (Breiman et al. 1984). CART was run for all NHD catchments in the study area using the least absolute deviation option for choosing splits with bootstrapping (sample of 800 repeated 1,000 times, maximum = 2 splits, p = 0.05 stopping rule). CART was rerun for the 41 catchments larger than 10 km2 in the study area using the least absolute deviation option for choosing splits with bootstrapping (sample of 38 repeated 1,000 times, maximum = 2 splits, p = 0.05 stopping rule).
Automated and Non-Automated Fertigation Systems for Cucumber Inside a Polyhouse
Published in Megh R. Goyal, B. J. Pandian, Management Strategies for Water Use Efficiency and Micro Irrigated Crops, 2019
Anjaly C. Sunny, V. M. Abdul Hakkim
The data collected was subjected to ANOVA (Analysis of Variance) and Student-t-test and executed using the software SYSTAT and MS Excel. CRD design was used for the analysis. Wherever the results were significant, critical differences were worked out at probability level p < 0.05. The non-significant differences were denoted as NS. With respect to Student t-test, if the calculated value exceeds the table value, then the treatment is significantly different at that level of probability based on the hypothesis tested. In the present study, it was considered a significant difference at p = 0.05, and this means that if the null hypothesis were correct (i.e. the treatments do not differ) then “t” value has to be greater as this, on less than 5% of occasions. This means that the treatments do differ from one another, but we still have nearly a 5% chance of being wrong in reaching this conclusion.
Queuing theory and its application in mines
Published in Amit Kumar Gorai, Snehamoy Chatterjee, Optimization Techniques and their Applications to Mine Systems, 2023
Amit Kumar Gorai, Snehamoy Chatterjee
The distributions of the observed inter-arrival times and loading times (Table 10.5) should be examined for estimating the best-fit probability density function (PDF). The outliers in the data should be removed before estimating the best PDF. After removal of outliers, the best fit PDF can be estimated by using any of the software like SPSS, SYSTAT, MATLAB, etc. Here, the data is fitted with the exponential probability density function using MATLAB software with good accuracy, as shown in Figure 10.12. However, most mining applications are highly complex, and accurate fitting of the observed data is important for queuing analysis. The probability density function for inter-arrival distribution and service time distribution is estimated as follows: f(t)=1μ1e−tμ1=15.59e−t5.59f(t)=1μ2e−tμ2=13.91e−t3.91
Comparing flow cytometry and microscopy in the quantification of vital aquatic organisms in ballast water
Published in Journal of Marine Engineering & Technology, 2020
Louis Peperzak, Eva-Maria Zetsche, Stephan Gollasch, Luis Felipe Artigas, Simon Bonato, Veronique Creach, Pieter de Vré, George B.J. Dubelaar, Joël Henneghien, Ole-Kristian Hess-Erga, Roland Langelaar, Aud Larsen, Brian N. Maurer, Albert Mosselaar, Euan D. Reavie, Machteld Rijkeboer, August Tobiesen
The Coefficient of Variation (CV) was calculated for flow cytometer measurements as a measure of precision. It is the ratio of the standard deviation (SD) to the mean (SD/mean) × 100%. The relative precisions of the flow rates were also computed by dividing the 95% confidence interval (CI) of the mean flow rate by the mean flow rate. The 95% CI was calculated in Excel from n observations and the standard deviation (SD) as: ± t × SD/SQRT(n), with t from a t-table for t(0.975) with df = n−1 (df = degrees of freedom). The Kruskal-Wallis one-way ANOVA test statistic and the Dwass-Steel-Chritchlow-Fligner Test for All Pairwise Comparisons were made in SYSTAT. Linear regression and t-tests were also performed in SYSTAT. The significance level used was 95% (P < 0.05).