TY - GEN
T1 - Use of artificial neural networks for habitat unit composition modeling
AU - Suen, Jian Ping
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=70350147116&partnerID=8YFLogxK
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U2 - 10.1061/41036(342)319
DO - 10.1061/41036(342)319
M3 - Conference contribution
AN - SCOPUS:70350147116
SN - 9780784410363
T3 - Proceedings of World Environmental and Water Resources Congress 2009 - World Environmental and Water Resources Congress 2009: Great Rivers
SP - 3159
EP - 3166
BT - Proceedings of World Environmental and Water Resources Congress 2009 - World Environmental and Water Resources Congress 2009
T2 - World Environmental and Water Resources Congress 2009: Great Rivers
Y2 - 17 May 2009 through 21 May 2009
ER -