TY - GEN
T1 - Analyzing the low flow trends in northwest Indiana using neural network
AU - Zeng, Le
AU - Viswanathan, Chandramouli
AU - Brion, Gail
PY - 2012
Y1 - 2012
N2 - This research focuses on the low flow regime of Lake Michigan watershed in the northwest Indiana, located in the southern tip of Lake Michigan. Low flow is mainly contributed through groundwater flow. Ecosystem survival depends on the low flow regime (Sala et al., 2000). Changes in landuse pattern, climate change, river training works and many other factors influences the low flow. This region had encountered lot of changes in the landuse pattern during the last 50 years. Runoff from five watersheds was used in this work. Analysis such as single station trend, regional trend, 7Q10 flow, flow duration curve and seasonal trends were conducted systematically to understand the behavior of low flow regime. Historic data such as daily flow, monthly mean temperature, monthly rainfall and land use changes were considered. To study the influence of different factors, artificial neural network based approach was used. Using relative strength effect (RSE) associated with each input neuron, the influencing factors were examined. This study indicates an increasing trend in the low flow regime for this region.
AB - This research focuses on the low flow regime of Lake Michigan watershed in the northwest Indiana, located in the southern tip of Lake Michigan. Low flow is mainly contributed through groundwater flow. Ecosystem survival depends on the low flow regime (Sala et al., 2000). Changes in landuse pattern, climate change, river training works and many other factors influences the low flow. This region had encountered lot of changes in the landuse pattern during the last 50 years. Runoff from five watersheds was used in this work. Analysis such as single station trend, regional trend, 7Q10 flow, flow duration curve and seasonal trends were conducted systematically to understand the behavior of low flow regime. Historic data such as daily flow, monthly mean temperature, monthly rainfall and land use changes were considered. To study the influence of different factors, artificial neural network based approach was used. Using relative strength effect (RSE) associated with each input neuron, the influencing factors were examined. This study indicates an increasing trend in the low flow regime for this region.
UR - http://www.scopus.com/inward/record.url?scp=84866133167&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84866133167&partnerID=8YFLogxK
U2 - 10.1061/9780784412312.176
DO - 10.1061/9780784412312.176
M3 - Conference contribution
AN - SCOPUS:84866133167
SN - 9780784412312
T3 - World Environmental and Water Resources Congress 2012: Crossing Boundaries, Proceedings of the 2012 Congress
SP - 1772
EP - 1778
BT - World Environmental and Water Resources Congress 2012
T2 - World Environmental and Water Resources Congress 2012: Crossing Boundaries
Y2 - 20 May 2012 through 24 May 2012
ER -