University of Tehran Press Iranian Journal of Soil and Water Research 2008-479X 46 3 2015 09 23 Prediction of Longitudinal Dispersion Coefficient in Natural Streams using Soft Computing Techniques Prediction of Longitudinal Dispersion Coefficient in Natural Streams using Soft Computing Techniques 385 394 56728 10.22059/ijswr.2015.56728 FA Somayyeh Soltani-Gerdefaramarzi Assistant Professor, Faculty of Agriculture and Natural Resources, Ardakan University Ruhollah Taghizadeh-Mehrjerdi Assistant Professor, Faculty of Agriculture and Natural Resources, Ardakan University Mohsen Ghasemi PhD Candidate, Water Engineering, Isfahan University of Technology Journal Article 2014 05 21 To accurately estimate the longitudinal dispersion coefficient is important and indispensable in river modeling. Many theoretical as well as empirical formulations have been proposed to determine the longitudinal dispersion coefficient, but these have not been put into consideration because of their great error, and as well the complexity of the phenomenon. The main aim followed in the present paper is to investigate the method as well as equations developed for dispersion coefficient estimation and assessment of the accuracy of these methods in comparison with real data and developing an accurate methodology for dispersion coefficient determination making use of such soft computing techniques as, neural, genetic programming and Neuron-Fuzzy Inference System.ANFIS approach ended up with the excellent results of: R² = 0.87, RMSE = 72.21, CRM = 0.103 and EF=0.75 as compared with the existing predictors of dispersion coefficient. In total ANFIS approach is hereby proposed as a most acceptable technique for estimating the longitudinal dispersion coefficient. To accurately estimate the longitudinal dispersion coefficient is important and indispensable in river modeling. Many theoretical as well as empirical formulations have been proposed to determine the longitudinal dispersion coefficient, but these have not been put into consideration because of their great error, and as well the complexity of the phenomenon. The main aim followed in the present paper is to investigate the method as well as equations developed for dispersion coefficient estimation and assessment of the accuracy of these methods in comparison with real data and developing an accurate methodology for dispersion coefficient determination making use of such soft computing techniques as, neural, genetic programming and Neuron-Fuzzy Inference System.ANFIS approach ended up with the excellent results of: R² = 0.87, RMSE = 72.21, CRM = 0.103 and EF=0.75 as compared with the existing predictors of dispersion coefficient. In total ANFIS approach is hereby proposed as a most acceptable technique for estimating the longitudinal dispersion coefficient. https://ijswr.ut.ac.ir/article_56728_9e9b4aab0c6515629480be07841dfdcb.pdf