Multi-objective genetic algorithm optimization of artificial neural network for estimating suspended sediment yield in Mahanadi River basin, India

Rivers are an integral part of the hydrological cycle and are the major geological agents which erode the continents and transport water and sediments to the oceans. Thus rivers act an important link between continents and oceans for the mass balance. Estimating the suspended sediment yield is one o...

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Vydáno v:	International journal of river basin management Ročník 18; číslo 2; s. 207 - 215
Hlavní autoři:	Yadav, Arvind, Satyannarayana, Penke
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Abingdon Taylor & Francis 02.04.2020 Taylor & Francis Ltd
Témata:	Agents (artificial intelligence) Algorithms Artificial intelligence Artificial neural network Artificial neural networks basins Catchment areas Complex variables Continents Correlation coefficient Correlation coefficients Discharge measurement Estimation Fluvial sediments Gaging stations Genetic algorithms Geology Hybrid systems hybrids Hydrologic cycle Hydrological cycle Hydrology India labor Labour Mahanadi River Mass balance Mathematical models Model accuracy multi-objective genetic algorithm Multiple objective analysis Neural networks Oceans Optimization Parameters Rain Rainfall River basins Rivers Samplers Sediment sediment contamination Sediment load Sediment yield Sediments Stream discharge Suspended load suspended sediment suspended sediment yield Suspended sediments temperature variance water discharge Watersheds India
ISSN:	1571-5124, 1814-2060, 1814-2060
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