@article{MTMT:2876693,
	title = {An ArcGIS Tool for Modeling the Climate Envelope with Feed-Forward ANN},
	url = {https://m2.mtmt.hu/api/publication/2876693},
	author = {Bede-Fazekas, Ákos and Horváth, Levente and Trájer, Attila János and Gregorics, Tibor},
	doi = {10.1080/08839514.2015.1004612},
	journal-iso = {APPL ARTIF INTELL},
	journal = {APPLIED ARTIFICIAL INTELLIGENCE},
	volume = {29},
	unique-id = {2876693},
	issn = {0883-9514},
	abstract = {This paper is about the development and the application of an ESRI ArcGIS tool which implements multi-layer, feed-forward artificial neural network (ANN) to study the climate envelope of species. The supervised learning is achieved by backpropagation algorithm. Based on the distribution and the grids of the climate (and edaphic data) of the reference and future periods the tool predicts the future potential distribution of the studied species. The trained network can be saved and loaded. A modeling result based on the distribution of European larch (Larix decidua Mill.) is presented as a case study.},
	year = {2015},
	eissn = {1087-6545},
	pages = {233-242},
	orcid-numbers = {Bede-Fazekas, Ákos/0000-0002-2905-338X; Trájer, Attila János/0000-0003-3248-6474; Gregorics, Tibor/0000-0002-9503-9623}
}