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• W2506473059 abstract "In the present study, we developed and compared two artificial intelligences technique (AI) for simultaneous modelling and forecasting hourly dissolved oxygen (DO) in river ecosystem. The two techniques are: radial basis function neural network (RBFNN) and multilayer perceptron neural network (MLPNN). For the purpose of the study, we choose two stations from the United States Geological Survey: (USGS ID: 421015121471800) at Lost River Diversion Channel nr Klamath River, Oregon, USA (Latitude 42°10′15″, Longitude 121°47′18″ NAD83), with a total of 8703 data, and (USGS ID: 421401121480900) at Upper Klamath Lake at Link River Dam, Oregon USA (Latitude 42°14′01″, Longitude 121°48′09″ NAD83) with a total of 8552 data. The investigation is divided into two distinguished phase. Firstly, using four water quality variables that are, water pH, temperature (TE), specific conductance (SC), and sensor depth (SD); we compared five models (M1 to M5) with different combination of input variables. As a result of the first investigation we found that generally RBFNN outperform MLPNN according to the performances criteria calculated. In the second part of the study, six Different models (FM1 to FM6) having the same input data sets are developed for 1,12, 24,48,72 and 168 h ahead (in advance) forecasting. The performance of the RBFNN and MLPNN models in training, validation and testing sets are compared with the observed data. Our results reveal that the two models provided relatively similar results and they successfully forecasting DO with a high level of accuracy and the reliability of forecasting decreases with increasing the step ahead." @default.
• W2506473059 created "2016-08-23" @default.
• W2506473059 creator A5001907851 @default.
• W2506473059 date "2016-07-20" @default.
• W2506473059 modified "2023-10-16" @default.
• W2506473059 title "Simultaneous modelling and forecasting of hourly dissolved oxygen concentration (DO) using radial basis function neural network (RBFNN) based approach: a case study from the Klamath River, Oregon, USA" @default.
• W2506473059 cites W108641313 @default.
• W2506473059 cites W1268310917 @default.
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• W2506473059 cites W1974083066 @default.
• W2506473059 cites W1983373379 @default.
• W2506473059 cites W1988115241 @default.
• W2506473059 cites W1989471229 @default.
• W2506473059 cites W2001270632 @default.
• W2506473059 cites W2001773650 @default.
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• W2506473059 doi "https://doi.org/10.1007/s40808-016-0197-4" @default.
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