TY - JOUR
T1 - Deduction of reservoir operating rules for application in global hydrological models
AU - Coerver, Hubertus M.
AU - Rutten, Martine M.
AU - Van De Giesen, Nick C.
PY - 2018
Y1 - 2018
N2 - A big challenge in constructing global hydrological models is the inclusion of anthropogenic impacts on the water cycle, such as caused by dams. Dam operators make decisions based on experience and often uncertain information. In this study information generally available to dam operators, like inflow into the reservoir and storage levels, was used to derive fuzzy rules describing the way a reservoir is operated. Using an artificial neural network capable of mimicking fuzzy logic, called the ANFIS adaptive-network-based fuzzy inference system, fuzzy rules linking inflow and storage with reservoir release were determined for 11 reservoirs in central Asia, the US and Vietnam. By varying the input variables of the neural network, different configurations of fuzzy rules were created and tested. It was found that the release from relatively large reservoirs was significantly dependent on information concerning recent storage levels, while release from smaller reservoirs was more dependent on reservoir inflows. Subsequently, the derived rules were used to simulate reservoir release with an average Nash-Sutcliffe coefficient of 0.81.
AB - A big challenge in constructing global hydrological models is the inclusion of anthropogenic impacts on the water cycle, such as caused by dams. Dam operators make decisions based on experience and often uncertain information. In this study information generally available to dam operators, like inflow into the reservoir and storage levels, was used to derive fuzzy rules describing the way a reservoir is operated. Using an artificial neural network capable of mimicking fuzzy logic, called the ANFIS adaptive-network-based fuzzy inference system, fuzzy rules linking inflow and storage with reservoir release were determined for 11 reservoirs in central Asia, the US and Vietnam. By varying the input variables of the neural network, different configurations of fuzzy rules were created and tested. It was found that the release from relatively large reservoirs was significantly dependent on information concerning recent storage levels, while release from smaller reservoirs was more dependent on reservoir inflows. Subsequently, the derived rules were used to simulate reservoir release with an average Nash-Sutcliffe coefficient of 0.81.
UR - http://www.scopus.com/inward/record.url?scp=85041397772&partnerID=8YFLogxK
UR - http://resolver.tudelft.nl/uuid:3f3293ad-cc91-44a2-a077-29fe23de677d
U2 - 10.5194/hess-22-831-2018
DO - 10.5194/hess-22-831-2018
M3 - Article
AN - SCOPUS:85041397772
SN - 1027-5606
VL - 22
SP - 831
EP - 851
JO - Hydrology and Earth System Sciences
JF - Hydrology and Earth System Sciences
IS - 1
ER -