Publication Type
Journal Article
Journal Name
Algorithms
Publication Date
4-1-2020
Abstract
The process of moving from experimental data to modeling and characterizing the dynamics and interactions in natural processes is a challenging task. This paper proposes an interactive platform for fitting data derived from experiments to mathematical expressions and carrying out spatial visualization. The platform is designed using a component-based software architectural approach, implemented in R and the Java programming languages. It uses experimental data as input for model fitting, then applies the obtained model at the landscape level via a spatial temperature grid data to yield regional and continental maps. Different modules and functionalities of the tool are presented with a case study, in which the tool is used to establish a temperature-dependent virulence model and map the potential zone of efficacy of a fungal-based biopesticide. The decision support system (DSS) was developed in generic form, and it can be used by anyone interested in fitting mathematical equations to experimental data collected following the described protocol and, depending on the type of investigation, it offers the possibility of projecting the model at the landscape level.
Keywords
Component-based approach, Eclipse-RCP (Rich client platform), Interactive platform, Nonlinear regression, Software architecture, Spatial prediction
Recommended Citation
Guimapi, R., Mohamed, S., Biber-Freudenberger, L., Mwangi, W., Ekesi, S., Borgemeister, C., & Tonnang, H. (2020). Decision support system for fitting and mapping nonlinear functions with application to insect pest management in the biological control context. Algorithms, 13 (4) https://doi.org/10.3390/A13040104
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.