Abstract
Suspended sediment load (SSL) prediction in streams or rivers is essential for sustainable environmental systems and water resource engineering. Sediment has a major influence on working capacity of reservoir and dams. In last two decades, data-driven models have been commonly used for modelling hydrological phenomena that are complex in nature. In this study, support vector machine (SVM) with fruit fly optimisation algorithm (SVM-FOA) has been employed to predict SSL at Rajghat station of Narmada river basin, India utilising collected hydro-meteorological data. Accuracy of SVM and SVM-FOA models was assessed based on statistical indices and graphical representation. Statistical assessment reveals that prediction by SVM-FOA model was significantly better than traditional SVM model. Overall, applied hybrid artificial intelligence model achieved excellent SSL prediction.
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Sahoo, A., Samantaray, S., Sathpathy, D.P. (2022). Prediction of Sediment Load Through Novel SVM-FOA Approach: A Case Study. In: Bhateja, V., Khin Wee, L., Lin, J.CW., Satapathy, S.C., Rajesh, T.M. (eds) Data Engineering and Intelligent Computing. Lecture Notes in Networks and Systems, vol 446. Springer, Singapore. https://doi.org/10.1007/978-981-19-1559-8_30
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DOI: https://doi.org/10.1007/978-981-19-1559-8_30
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