Use of artificial neural networks for habitat unit composition modeling

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Understanding the fish assemblage composition and assessing habitat preferences is an important requirement for ecological engineering practices for river restoration. This study collects fish and habitat data in the Tsengwen River Basin and Kaoping River Basin to provide criteria for fish habitat restoration projects. Fish samples were collected used prepositioned areal electrofishing devices designed for repeated grid sampling. The electrofishing data were used to identify habitat preferences and develop fish community-habitat models. A fuzzy rule-based model was used to characterize habitat units as deep pool, deep riffle, shallow riffle, and shallow pool based on measured depth and current velocity. The artificial neural networks then is used to model the connection between the autecology matrix analysis values and surveyed habitat unit compositions. The utility of the model was furthered using historical fisheries data in addition to the data collected as part of the study. The fish community-habitat model results provide a reference condition that can be used to guide stream restoration and ecological engineering decisions aimed at maintaining the natural ecological integrity and diversity of Taiwanese rivers.

Original languageEnglish
Title of host publicationProceedings of World Environmental and Water Resources Congress 2009 - World Environmental and Water Resources Congress 2009
Subtitle of host publicationGreat Rivers
Pages3159-3166
Number of pages8
DOIs
Publication statusPublished - 2009 Oct 26
EventWorld Environmental and Water Resources Congress 2009: Great Rivers - Kansas City, MO, United States
Duration: 2009 May 172009 May 21

Publication series

NameProceedings of World Environmental and Water Resources Congress 2009 - World Environmental and Water Resources Congress 2009: Great Rivers
Volume342

Other

OtherWorld Environmental and Water Resources Congress 2009: Great Rivers
CountryUnited States
CityKansas City, MO
Period09-05-1709-05-21

All Science Journal Classification (ASJC) codes

  • Environmental Science(all)

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