Comprehensive evaluation of machine learning models for suspended sediment load inflow prediction in a reservoir

Suspended sediment load (SSL) flowing into a reservoir contributes to the overall safety of dam. Owing to the complexity and stochastic nature of sedimentation, accurate prediction of reservoir SSL inflow is still challenging. Moreover, research and application of machine learning (ML) techniques fo...

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Veröffentlicht in:	Stochastic environmental research and risk assessment 2021-09, Vol.35 (9), p.1805-1823
Hauptverfasser:	Idrees, Muhammad Bilal, Jehanzaib, Muhammad, Kim, Dongkyun, Kim, Tae-Woong
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptive systems Aquatic Pollution Artificial neural networks Chemistry and Earth Sciences Computational Intelligence Computer Science Correlation coefficient Correlation coefficients Dam safety Dams Deep learning Earth and Environmental Science Earth Sciences Environment Evaluation Fuzzy logic Genetic algorithms Inflow Learning algorithms Learning theory Machine learning Math. Appl. in Environmental Science Neural networks Original Paper Physics Prediction models Probability Theory and Stochastic Processes Radial basis function Reservoir operation Reservoir sedimentation Risk assessment Root-mean-square errors Sediment load Sedimentation Sedimentation & deposition Sediments Statistics for Engineering Stream discharge Stream flow Support vector machines Suspended load Suspended sediments Waste Water Technology Water inflow Water Management Water outflow Water Pollution Control Water temperature
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