An Enhanced Feed-Forward Back Propagation Levenberg–Marquardt Algorithm for Suspended Sediment Yield Modeling

Rivers are dynamic geological agents on the earth which transport weathered materials of continent to sea. Estimation suspended sediment yield (SSY) is essential for management, planning, and designing in any river basin system. SSY critical due its complex nonlinear processes, not captured by conventional regression methods. Rainfall, temperature, water discharge, SSY, rock type, relief, catchment area data 11 gauging stations were utilized develop robust artificial intelligence (AI), similar an artificial-neural-network (ANN)-based model prediction. The developed highly generalized global single ANN using a large amount was applied at individual prediction Mahanadi River basin, one India’s largest peninsular rivers. It appeared that proposed had lowest root-mean-squared error (0.0089) mean absolute (0.0029) along with highest coefficient correlation (0.867) values among all comparative models (sediment rating curve multiple linear regression). provided best accuracy Tikarapara stations. most suitable substitute over other also noticed combined eleven performed better than from only. These approaches suggested systems their ease implementation performance.