Modeling Invasive Alien Plant Species - Fuzzy-Based Uncertainty (Hardcover)

Modelling Invasive Alien Plant Species: Fuzzy Based Uncertainty presents the application of different fuzzy set theory techniques in developing risk assessment models for invasive plant species- those whose introduction and spread outside their natural range threatens local biodiversity. Invasion risk of species is expressed by biological traits which would be considered as the risk factors accompanied with uncertainty and imprecision. The book considers both quantitative and qualitative inputs in modelling the invasive risk by incorporating different mathematical models based on fuzzy set theory operators, interval methods, and fuzzy linguistic operators. The proposed models can be applied for investigating risk of invasive alien plant species in various regions and conditions. Features: Uniquely merges mathematical models with biological expressions. Presents different factor-based models as a case study on the risk of invasive alien plant species. Explains how users can perform primary-level risk assessment through fuzzy modeling techniques. Appropriate for upper-level students, researchers, and practicing professionals, this book shows how conventional approaches such as probability theory can be of limited use to solve issues of uncertainty, and how they fuzzy set theory plays a better role in understanding uncertain system dynamics, such invasive plant modelling.

General

Imprint:	Crc Press
Country of origin:	United Kingdom
Release date:	October 2021
First published:	2022
Authors:	H.O.W. Peiris • S. Chakraverty • S.S.N. Perera • S.M.W. Ranwala
Dimensions:	234 x 156 x 20mm (L x W x T)
Format:	Hardcover
Pages:	182
ISBN-13:	978-0-367-75809-7
Categories:	Books > Professional & Technical > Agriculture & farming > Agricultural science Books > Science & Mathematics > Mathematics > Applied mathematics > Fuzzy set theory Books > Science & Mathematics > Biology, life sciences > Botany & plant sciences > General Promotions
LSN:	0-367-75809-1
Barcode:	9780367758097

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