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Article

Decision Support System for Fitting and Mapping Nonlinear Functions with Application to Insect Pest Management in the Biological Control Context

1
International Centre of Insect Physiology and Ecology (ICIPE), Nairobi P.O. Box 30772-00100, Kenya
2
Department of Computing, School of Computing & Information Technology, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi P.O. Box 62000-00200, Kenya
3
Center for Development Research (ZEF), Department of Ecology and Natural Resources Management, University of Bonn, Walter-Flex-Str. 3, 53113 Bonn, Germany
*
Author to whom correspondence should be addressed.
Algorithms 2020, 13(4), 104; https://doi.org/10.3390/a13040104
Received: 4 March 2020 / Revised: 16 April 2020 / Accepted: 16 April 2020 / Published: 24 April 2020
(This article belongs to the Special Issue Algorithms in Decision Support Systems)
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. View Full-Text
Keywords: Nonlinear regression; interactive platform; component-based approach; software architecture; Eclipse-RCP (Rich Client Platform); spatial prediction Nonlinear regression; interactive platform; component-based approach; software architecture; Eclipse-RCP (Rich Client Platform); spatial prediction
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MDPI and ACS Style

Guimapi, R.A.; Mohamed, S.A.; Biber-Freudenberger, L.; Mwangi, W.; Ekesi, S.; Borgemeister, C.; Tonnang, H.E.Z. Decision Support System for Fitting and Mapping Nonlinear Functions with Application to Insect Pest Management in the Biological Control Context. Algorithms 2020, 13, 104. https://doi.org/10.3390/a13040104

AMA Style

Guimapi RA, Mohamed SA, Biber-Freudenberger L, Mwangi W, Ekesi S, Borgemeister C, Tonnang HEZ. Decision Support System for Fitting and Mapping Nonlinear Functions with Application to Insect Pest Management in the Biological Control Context. Algorithms. 2020; 13(4):104. https://doi.org/10.3390/a13040104

Chicago/Turabian Style

Guimapi, Ritter A., Samira A. Mohamed, Lisa Biber-Freudenberger, Waweru Mwangi, Sunday Ekesi, Christian Borgemeister, and Henri E.Z. Tonnang 2020. "Decision Support System for Fitting and Mapping Nonlinear Functions with Application to Insect Pest Management in the Biological Control Context" Algorithms 13, no. 4: 104. https://doi.org/10.3390/a13040104

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