Abstract
Prediction of suspended sediment load in rivers plays a significant part in managing hydraulic structures and water resources problems. The main purpose of this study is to use Multilayer Perceptron (MLP) for predicting suspended sediment load (SSL) of Kalahandi station, India. Back-propagation is the most prevalent technique to minimize errors in an MLP model. Yet, this technique has some shortcomings like becoming trapped in local minima and low convergence speed. Therefore, the present study aims at applying an innovative new hybrid model integrating whale optimization algorithm (WOA) with MLP (MLP-WOA) for minimizing errors and improving prediction accuracy of simple MLP model. Quantitative performance indices coefficient of determination (R2), root mean square error (RMSE) and Willmott index (WI) were used for assessment of proposed models. Conclusive results indicate that developed hybrid model is highly effective and produces considerably better accurateness than simple model. It was also concluded that WOA algorithm can increase prediction accurateness of MLP model.
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Mohanta, N.R., Panda, S.K., Singh, U.K., Sahoo, A., Samantaray, S. (2022). MLP-WOA Is a Successful Algorithm for Estimating Sediment Load in Kalahandi Gauge Station, India. In: Saraswat, M., Roy, S., Chowdhury, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications . Lecture Notes in Networks and Systems, vol 288. Springer, Singapore. https://doi.org/10.1007/978-981-16-5120-5_25
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