Megjelent Fertő Imre és szerzőtársai tanulmánya az Irrigation and Drainage folyóiratban


Flexible on-request (arranged) water delivery is applicable in existing canals that operate manually. A challenge facing this method is the diversity and multiplicity of requests across the network, making planning for water distribution very difficult and time-consuming. Therefore, we need a suitable tool to derive the proper operation of structures quickly to improve network performance compared to rotational delivery. This study uses intelligent optimization methods to prepare on-request operational instructions of irrigation canals under unsteady flow conditions. A meta-model was developed using artificial neural networks (ANNs) for the first reach of the Aghili canal for simulating unsteady flow in canals to speed up the process. The meta-model was combined with a genetic algorithm (GA) to determine optimal operational instructions. The error rate of the ANN model was smaller than 2.5%, indicating excellent performance of the developed model. Two scenarios of 3- and 6-h delivery were defined to compare the optimal operation of the ANN-GA model with conventional operation. For both scenarios, the percentage of improvement was remarkable, and on average, it was above 50%. The utility of the developed model for shorter deliveries is significant. Moreover, the model can be generalized to other reaches.
⇒ tovább a tanulmányra

Felhasználási feltételek
Intézményünk országos ésnemzetközi hálózati kapcsolatátaz NIIF program biztosítja
Közgazdaság- és Regionális Tudományi Kutatóközpont Közgazdaság-tudományi Intézet
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