Assessing the effect of emotional unit of emotional ANN (EANN) in estimation of the prediction intervals of suspended sediment load modeling

This paper presents the construction of prediction intervals (PIs) associated with the Emotional Artificial Neural Network (EANN) via the Lower Upper Bound Estimation method (LUBE) and the classic Bootstrap method for the Suspended Sediment Load (SSL) modeling. As point prediction conveys no information about modeling reliability, PIs were applied. The constructed PIs via the EANN were also compared to those of the classic feed-forward Neural Network (FFNN) model. It was attempted to construct the PIs of the SSL modeling in both daily and monthly scales for two watersheds, Upper Rio Grande River, in the United States and the Lighvanchai River in Iran. The PIs were quantified via coverage and width criteria. In the LUBE method, Genetic Algorithm (GA) constructed the PIs by minimizing the cost function of Combinational coverage Width-based Criterion (CWC). Comparison of the results indicated that the criterion of the CWC for PIs of EANN was up to 72% and 78% lower than that to the PIs of the FFNN, respectively, in the LUBE and Bootstrap methods, which showed the reliability of the EANN. In addition, obtained results via the LUBE and the Bootstrap techniques denoted the lower level of uncertainty of the LUBE method. Comparing the CWC criterion of both methods indicated that for the LUBE method, CWC was up to 39% lower than that for the Bootstrap method. Also, the PIs of the FFNN for the Lighvanchai river modeling showed reliable results with CWC of 56% lower than Upper Rio Grande River, but PIs of the EANN led to lower level of uncertainty in Upper Rio Grande River modeling.

Automated summary

This paper introduces the construction of prediction intervals for Suspended Sediment Load modeling using Emotional Artificial Neural Network (EANN) and classic Neural Network models, comparing their reliability. Results show that EANN has higher reliability with Genetic Algorithm constructed PIs, reducing uncertainty levels. Additionally, the LUBE method outperforms the Bootstrap method in terms of reliability, with EANN showing lower uncertainty levels in Upper Rio Grande River modeling compared to FFNN.