Reservoir sedimentation resultingfromwatererosion isan importantenvironmentalissuein manycountries where storage of water is crucial for economic and agricultural development. Therefore, this paper reports resultsfrom analysis of the soil hydrological response, i.e. soil water erosion, to simulated rainfall resulting in sediment accumulation at the reservoir of Ekbatan Dam (Hamedanprovince, Iran). Also, another objective of this study was to simulate the future trends in reservoir sedimentation (soil loss rate; SLR) from indoor rainfall simulator data by multiple linear regression (MLR) and Artificial Neural Networks (ANNs). For this research, three sampling points with different types of soils were chosen including clayey sand soil (SC-SM), silty soil (ML), and clayey soil (CL). The input parameters were slope gradient (sin θ), soil type (St), water content (w),drydensityðϒdÞ; shearstrength (τ),unconfinedcompressivestrength(qu),permeability(k),and California bearing ratio (CBR). Using MLR and ANN methods, 7 models were developed with 2 constant predictors (i.e. sin θ and St) and 6 free predictors which were added in each step one by one. Among MLR models, model 5 with St, sin θ,ϒd, τ, w, andqu as input parameters was statistically significant. Among ANN models, model 4 withSt, sinθ,ϒd, τ, andw as input parameters, 9 nodes, and 1 hidden layer was statistically significant. The root mean square error (RMSE), mean error (ME), and correlation coefficient (R) values were 1.433kg/m2 h,0.0195kg/m2 h,and0.698fortheMLRmodeland0.38kg/m2 h,0.151kg/m2 h,and0.98forthe ANN model, respectively. These resultsshow that the ANN model could better predict the SLR in comparison to the MLR model. The results also demonstrate that shear strength, among the strength parameters, had a greater impact on the SLR than compressive strengths (qu and CBR). Last but not the least, the reservoir sedimentationwas estimatedforall methodsandcomparedwiththe observeddata.Theresultsindicatethat the ANN mode
