Uncertainty Analysis on Hybrid Double Feedforward Neural Network Model for Sediment Load Estimation with LUBE Method Xiao-Yun Chen 1 & Kwok-Wing Chau 1 Received: 31 October 2017 /Accepted: 4 July 2019 / Published online: 10 July 2019 # Springer Nature B.V. 2019 Abstract The assessment of uncertainty prediction has become a necessity for most modeling studies within the hydrology community. This paper addresses uncertainty analysis on a novel hybrid double feedforward neural network (HDFNN) model for generating the sediment load predic- tion interval (PI). By using the Lower Upper Bound Estimation (LUBE) method, the lower and upper bounds are directly generated as outputs of neural network based models. Coverage Width-based Criterion (CWC) is employed as an objective function for searching high quality PIs. The LUBE-based model is then applied to estimate sediment loads of Muddy Creek in Montana of USA. Results demonstrate the suitability of HDFNN-LUBE model in producing PI in both 90% and 95% confidence levels (CL). It is capable of generating appropriate lower bounds of PIs with narrow intervals. Partitioning analysis reveals consistently excellent performances of HDFNN model in constructing PI in terms of low, medium and high loads. These results therefore verify the reliability and potentiality of the HDFNN model for sediment load estimation with uncertainty. LUBE shows its efficiency in uncertainty prediction as well, which could be used to quantify total uncertainty of data-driven models. Keywords Uncertainty analysis . Hybrid double feedforward neural network . Sediment load estimation . Lower upper bound estimation 1 Introduction Although applications of data-driven models for real-time predictions of river flows and sediment loads have been well established over past years, the analysis of uncertainty is usually disregarded. Yet, uncertainty is always inherent in hydrological models, which may be imported by inputs, model parameters or their own structures. Generally, the lack of Water Resources Management (2019) 33:3563–3577 https://doi.org/10.1007/s11269-019-02318-4 * Kwok-Wing Chau cekwchau@polyu.edu.hk 1 Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong