TY - GEN
T1 - Development of inductive receiving water model for application in TMDLs
AU - Tufail, Mohammad
AU - Ormsbee, Lindell
PY - 2005
Y1 - 2005
N2 - Various receiving water models, such as HSPF, WASP5, and CE-QUAL are frequently used in the development of TMDLs, especially in the context of nutrient loadings and DO impacts. In most cases, such models can be extremely data intensive and difficult to use. This paper will discuss the development of two inductive models for DO response to nutrient loadings and its application in the development of a nutrient TMDL for the DO impaired Beargrass Creek in Louisville Kentucky. The associated models were developed using two separate AI modeling techniques: artificial neural networks and genetic programming/genetic algorithms. Data for use in constructing the two models was obtained from continuous water quality monitors that were strategically placed in the downstream reaches of the watershed as well as other discrete sampling for water quality constituents. Modeling of the system response was complicated by backwater effects from the Ohio River. The paper discusses the utility and advantages of use of inductive approach when adequate data sets are readily available. Copyright ASCE 2005.
AB - Various receiving water models, such as HSPF, WASP5, and CE-QUAL are frequently used in the development of TMDLs, especially in the context of nutrient loadings and DO impacts. In most cases, such models can be extremely data intensive and difficult to use. This paper will discuss the development of two inductive models for DO response to nutrient loadings and its application in the development of a nutrient TMDL for the DO impaired Beargrass Creek in Louisville Kentucky. The associated models were developed using two separate AI modeling techniques: artificial neural networks and genetic programming/genetic algorithms. Data for use in constructing the two models was obtained from continuous water quality monitors that were strategically placed in the downstream reaches of the watershed as well as other discrete sampling for water quality constituents. Modeling of the system response was complicated by backwater effects from the Ohio River. The paper discusses the utility and advantages of use of inductive approach when adequate data sets are readily available. Copyright ASCE 2005.
KW - Artificial intelligence
KW - Artificial neural networks
KW - Deductive models
KW - Genetic algorithms
KW - Inductive models
KW - Receiving water model
KW - Total maximum daily load
UR - https://www.scopus.com/pages/publications/37249004738
UR - https://www.scopus.com/pages/publications/37249004738#tab=citedBy
U2 - 10.1061/40792(173)269
DO - 10.1061/40792(173)269
M3 - Conference contribution
AN - SCOPUS:37249004738
SN - 0784407924
SN - 9780784407929
T3 - World Water Congress 2005: Impacts of Global Climate Change - Proceedings of the 2005 World Water and Environmental Resources Congress
SP - 269
BT - World Water Congress 2005
T2 - 2005 World Water and Environmental Resources Congress
Y2 - 15 May 2005 through 19 May 2005
ER -