Next Article in Journal
Endoreversible Models for the Thermodynamics of Computing
Previous Article in Journal
A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection
Open AccessArticle

Using Probabilistic Approach to Evaluate the Total Population Density on Coarse Grids

by Manal Alqhtani 1,2,* and Khaled M. Saad 2,3
School of Mathematics, College of Engineering and Physical Sciences, The University of Birmingham, Birmingham B15 2TT, UK
Department of Mathematics, College of Sciences and Arts, Najran University, Najran 11001, Saudi Arabia
Department of Mathematics, Faculty of Applied Science, Taiz University, Taiz 6803, Yemen
Author to whom correspondence should be addressed.
Entropy 2020, 22(6), 658;
Received: 13 May 2020 / Revised: 9 June 2020 / Accepted: 11 June 2020 / Published: 14 June 2020
Evaluation of the population density in many ecological and biological problems requires a satisfactory degree of accuracy. Insufficient information about the population density, obtained from sampling procedures negatively, impacts on the accuracy of the estimate. When dealing with sparse ecological data, the asymptotic error estimate fails to achieve a reliable degree of accuracy. It is essential to investigate which factors affect the degree of accuracy of numerical integration methods. When the number of traps is less than the recommended threshold, the degree of accuracy will be negatively affected. Therefore, available numerical integration methods cannot guarantee a satisfactory degree of accuracy, and in this sense the error will be probabilistic rather than deterministic. In other words, the probabilistic approach is used instead of the deterministic approach in this instance; by considering the error as a random variable, the chance of obtaining an accurate estimation can be quantified. In the probabilistic approach, we determine a threshold number of grid nodes required to guarantee a desirable level of accuracy with the probability equal to one. View Full-Text
Keywords: sparse data; coarse grid; sampling; ecological monitoring sparse data; coarse grid; sampling; ecological monitoring
Show Figures

Figure 1

MDPI and ACS Style

Alqhtani, M.; Saad, K.M. Using Probabilistic Approach to Evaluate the Total Population Density on Coarse Grids. Entropy 2020, 22, 658.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop