Application of ANN and fuzzy logic algorithms for streamflow modelling of Savitri catchment

Journal of Earth system science

Mahesh Kothari1¹  K D Gharde2² 

[1] Department of Soil and Water Engineering, College of Technology and Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur 313 001, India.$$;SWCE, College of Technology and Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur, India.$$

关键词: Transferred function;    Sigmoid;    back-propagation;    membership function;    defuzzification.;   

DOI  :  

学科分类：天文学(综合)

来源: Indian Academy of Sciences

【 摘 要 】

The streamflow prediction is an essentially important aspect of any watershed modelling. The black box models (soft computing techniques) have proven to be an efficient alternative to physical (traditional) methods for simulating streamflow and sediment yield of the catchments. The present study focusses on development of models using ANN and fuzzy logic (FL) algorithm for predicting the streamflow for catchment of Savitri River Basin. The input vector to these models were daily rainfall, mean daily evaporation, mean daily temperature and lag streamflow used. In the present study, 20 years (1992–2011) rainfall and other hydrological data were considered, of which 13 years (1992–2004) was for training and rest 7 years (2005–2011) for validation of the models. The mode performance was evaluated by R, RMSE, EV, CE, and MAD statistical parameters. It was found that, ANN model performance improved with increasing input vectors. The results with fuzzy logic models predict the streamflow with single input as rainfall better in comparison to multiple input vectors. While comparing both ANN and FL algorithms for prediction of streamflow, ANN model performance is quite superior.

【 授权许可】

Unknown