Derivation of Optimized Equations for Estimation of Dispersion Coefficient in Natural Streams Using Hybridized ANN With PSO and CSO Algorithms

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Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry

The main aims and contributions of the present paper are to use new soft computing methods for the simulation of scour geometry (depth/height and locations) in a comparative framework. Five models were used for the prediction of the dimension and location of the scour pit. The five developed models in this study are multilayer perceptron (MLP) neural network, radial basis functions (RBF) neural network, adaptive neuro fuzzy inference systems (ANFIS), multiple linear regression (MLR), and multiple non-linear regression (MNLR) in comparison with empirical equations. Four non-dimensional geometry parameters of scour hole shape are predicted by these models including the maximum scour depth (S), the distance of S from the weir (XS), the maximum height of downstream deposited sediments (hd), and distance of hd from the weir (XD). The best results over train data derived for XS/Z and hd/Z by the MLP model with R2 are 0.95 and 0.96 respectively; the best predictions for S/Z and XD/Z are from the ANFIS model with R2 0.91 and 0.96 respectively. The results indicate that the application of MLP and ANFIS results in the accurate prediction of scour geometry for the designing of stable grade control structures in alluvial irrigation channels

Qualitative Zoning of Shahr-e-Babak Aquifer Based on its Corrosiveness, Sedimentation, and Applicability for Agricultural, Drinking, and Pressure Irrigation Uses

Water quality management in groundwater aquifers requires accurate water quality monitoring to ensure they meet a variety of relevant standards. Given the rather few reported studies in the field, the present study was designed and implemented to investigate the groundwater quality of Shahr-e-Babak aquifer that is exploited for agricultural, mining, drinking, and industrial consumptions. The kriging and IDW (power:1-3) techniques with spherical, exponential, and Gaussian variogramsare were compared using R2, RMSE, MAE, and RSS indices to find the optimum model for determining the spatial variability of sixteen groundwater quality parameters. Multi-purpose zoning of groundwater quality was accomplished in the ArcGIS environment in terms of the Wilcox, Schuler, drip and sprinkle irrigation as delineated by Iran Power Ministry, corrosiveness, and sedimentation standards as well as the WHO and IRISI indices before spatial correlations were determined accordingly. Based on the water quality zoning maps thus derived, Langelier, Corrosiveness, and Ryznar indices were several times larger than the threshold levels across the whole aquifer plain, indicating the wide corrosiveness of the water in industrial applications. The results also revelaed that 93% of the groundwater in the plain area was classified as C4-S1 and C4-S2, which are unsuitable for irrigation, while only 1.3% of the groundwater was acceptable for drinking uses. Drip irrigation zoning revealed that 64% of the plain area had the lowest water quality.The undesirable quality zones in a vast area of the aquifer investigated calls for accurate quality monitoring and management to meet the development objectives in the region

Derivation of Optimized Equations for Estimation of Dispersion Coefficient in Natural Streams Using Hybridized ANN With PSO and CSO Algorithms

publishedVersio