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suspended sediment yield modeling in Mahanadi River, India by multi-objective optimization hybridizing artificial intelligence algorithms

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International Journal of sediment Research 2021年第1期36卷 76-91页

作者： Arvind Yadav Snehamoy Chatterjee Sk Md Equeenuddin Department of Electronics and Computer Engineering Koneru Lakshmaiah Education Foundation Department of Mining Engineering National Institute of Technology Department of Geological and Mining Engineering and Sciences Michigan Technological University Department of Earth & Atmospheric Sciences National Institute of Technology

River sediment produced through weathering is one of the principal landscape modification processes on *** are an integral part of the hydrologic cycle and are the major geologic agents that erode the continents and transport water and sediments to the *** of suspended sediment yield is always a key parameter for planning and management of any river *** is always challenging to model sediment yield using traditional mathematical models because they are incapable of handling the complex non-linearity and *** suspended sediment modeling of the river depends on the number of factors such as rock type,relief,rainfall,temperature,water discharge and catchment *** this study,we proposed a hybrid genetic algorithm-based multi-objective optimization with artificial neural network(GA-MOO-ANN) with automated parameter tuning model using these factors to estimate the suspended sediment yield in the entire Mahanadi River *** model was validated by comparing statistically with other models,and it appeared that the GA-MOO-ANN model has the lowest root mean squared error(0.009) and highest coefficient of correlation(0.885) values among all comparative models(traditional neural network,multiple linear regression,and sediment rating curve) for all *** was also observed that the proposed model is the least biased(0.001) ***,the proposed GA-MOOANN is the most capable model,compared to other studied models,for estimating the suspended sediment yield in the entire Mahanadi river basin,*** results also suggested that the proposed GA-MOO-ANN model is unable to estimate suspended sediment yield satisfactorily at gauge stations having very small catchment areas whereas performing satisfactorily on locations having moderate to the large catchment *** models provide the best result at Tikarapara,the gauge station location in the extreme downstream,having the largest catchment area.

关键词： Artificial neural network Genetic algorithm Multi-objective genetic algorithm suspended sediment yield Mahanadi river

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Impact of climate change on sediment yield for Naran watershed,Pakistan

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International Journal of sediment Research 2016年第3期31卷 212-219页

作者： Farooq Azim Abdul Sattar Shakir Habib-ur-Rehman Afshan Kanwal Centre of Excellence in Water Resource Engineering(CEWRE) University of Engineering & Technology(UET) Government College University CEWRE UET Department of Civil Engineering UET

In this paper,the site-specific impact of climate change on sediment yield has been assessed for the Naran watershed,*** data has been gathered for period 1961-2010 and HaDCM3 GCM predictors of SRES scenarios A2 and B2 have been *** precipitation and temperature time series have been statistically downscaled for time horizon 2011-2040 and *** data show both increasing and decreasing changes with respect to the *** sediment yield for future related to climate change has been *** results show that the both snowy and monsoon seasonal stream discharges are expected to *** will lead to increase in annual suspended sediment ***-wise,a less discharge and more sediment yield are expected during the early *** study concluded that the climate change and variability are influencing the watershed,and suspended sediment yield is likely to increase in the future.

关键词： Climate change Downscaling Hydrologic modeling suspended sediment yield SHETRAN

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