Abstract
The prediction method for the sediment deposition in the Ishidegawa dam reservoir, using artificial neural network (ANN), is proposed. Monthly sediment deposition VsM is calculated by the ANN trained with data of rainfall and sediment deposition in the past 20 years. Input elements of the ANN are monthly rainfall depth TRM, monthly maximum inflow discharge QmM, monthly number of days with daily inflow discharge more than 10m3/s (N10M) and monthly number of days with daily inflow discharge between 1m3/s and 10m3/s (N1M). In the first step, three input elements QmM, N10M, N1M are estimated by sub-ANN with monthly rainfall TRM, and the input element of the ANN for VsM is reduced to be only one element TRM. Target monthly sediment deposition VsM is given by distributing the observed yearly sediment deposition VsY according to the ratio of monthly rainfall TRM to yearly rainfall TRY. It is proved that yearly sediment deposition can be estimated well by the ANN based on the monthly rainfall depth as an input.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
