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ESTIMATING REFERENCE EVAPOTRANSPIRATION FROM LIMITED CLIMATIC DATA USING ARTIFICIAL NEURAL NETWORKS
The objective of this study is to test artificial neural networks (ANNs) for estimating reference evapotranspiration (ETo) with limited climatic data for Mahanadi Reservoir Project (MRP) area, in Raipur (Chhatisgarh). The study employed the three feed-forward type ANNs for computing the ETo. The networks, using varied input combinations of climatic variables have been trained using the quasi-Newton training algorithm. The ANNs are trained to estimate ETo from weekly climate data as input and the Penman-Monteith (P-M) estimate as output. The model estimates are compared with P-M method. Standard error of estimate (SEE) and model efficiency were used for the performance evaluation of the models. The analyses suggest that the ETo can be computed from air temperature using the ANN approach in MRP region. Further based on the results obtained, it can also be concluded that ANN performed well when the input (first) layer receives the input variables consisting of all quantities that can influence the output.
ESTIMATING REFERENCE EVAPOTRANSPIRATION FROM LIMITED CLIMATIC DATA USING ARTIFICIAL NEURAL NETWORKS
The objective of this study is to test artificial neural networks (ANNs) for estimating reference evapotranspiration (ETo) with limited climatic data for Mahanadi Reservoir Project (MRP) area, in Raipur (Chhatisgarh). The study employed the three feed-forward type ANNs for computing the ETo. The networks, using varied input combinations of climatic variables have been trained using the quasi-Newton training algorithm. The ANNs are trained to estimate ETo from weekly climate data as input and the Penman-Monteith (P-M) estimate as output. The model estimates are compared with P-M method. Standard error of estimate (SEE) and model efficiency were used for the performance evaluation of the models. The analyses suggest that the ETo can be computed from air temperature using the ANN approach in MRP region. Further based on the results obtained, it can also be concluded that ANN performed well when the input (first) layer receives the input variables consisting of all quantities that can influence the output.
ESTIMATING REFERENCE EVAPOTRANSPIRATION FROM LIMITED CLIMATIC DATA USING ARTIFICIAL NEURAL NETWORKS
Ms.Chauhan, S. (author) / Shrivastava, R. K. (author)
ISH Journal of Hydraulic Engineering ; 15 ; 34-44
2009-01-01
11 pages
Article (Journal)
Electronic Resource
Unknown
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