Updating the neural network sediment load models using different sensitivity analysis methods: a regional application Reza Asheghi, Seyed Abbas Hosseini, Mojtaba Saneie and Abbas Abbaszadeh Shahri ABSTRACT The amount of transported sediment load by streams is a vital but high nonlinear dynamic process in water resources management. In the current paper, two optimum predictive models subjected to artificial neural network (ANN) were developed. The employed inputs were then prioritized using diverse sensitivity analysis (SA) methods to address new updated but more efficient ANN structures. The models were found through the 263 processed datasets of three rivers in Idaho, USA using nine different measured flow and sediment variables (e.g., channel geometry, geomorphology, hydraulic) for a period of 11 years. The used parameters were selected based on the prior knowledge of the conventional analyses in which the effect of suspended load on bed load was also investigated. Analyzed accuracy performances using different criteria exhibited improved predictability in updated models which can lead to an advanced understanding of used parameters. Despite different SA methods being employed in evaluating model parameters, almost similar results were observed and then verified using relevant sensitivity indices. It was demonstrated that the ranked parameters using SA due to covering more uncertainties can be more reliable. Evaluated models using sensitivity indices showed that contribution of suspended load on predicted bed load is not significant. Reza Asheghi Seyed Abbas Hosseini (corresponding author) Mojtaba Saneie Abbas Abbaszadeh Shahri Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran E-mail: abbas_hoseyni@srbiau.ac.ir Key words | artificial neural network, sensitivity analysis, transported sediment loads, updated model INTRODUCTION The transported sediments by rivers as a complicated set of processes between stream flow, geologic, geomorphic, and organic factors is an important but critical regionally specific concern in the hydrological perspective to realize how rivers work (e.g., Melesse et al. ; Hajbabaei et al. ; Sari et al. ; Jin et al. ). Such sediments can be very informative in assessment of engineering purposes (e.g., channels, reservoirs, and dams), geo-environmental and ecosystem impacts (e.g., protection of fish and wildlife habitats), and river basin management (e.g., soil erosion, transported sediments, and pollutants) (e.g., Kisi et al. ; Bouzeria et al. ; Jin et al. ). Thereby, prediction of sediment loads has become an important issue in many countries in introducing schemes for river water monitoring. Modeling approaches are the common way to estimate transported sediment loads. However, the effects of the involved parameters due to model structure, hydrological, time-series inputs, geological, geomorphological, hydrologi- cal and hydraulic features on predicted sediment loads should be considered. The wide variety of involved par- ameters exhibit no accepted universal approach to predict all types of sediment loads (Ma et al. ; Leimgruber et al. ; Asheghi & Hosseini ). This indicates why several modeling tools for simulating sediment loads have 562 © IWA Publishing 2020 Journal of Hydroinformatics | 22.3 | 2020 doi: 10.2166/hydro.2020.098 Downloaded from http://iwaponline.com/jh/article-pdf/22/3/562/693089/jh0220562.pdf by guest on 07 March 2023