Rainfall prediction using co-active neuro fuzzy inference system for umargaon watershed Nagpur India

Abstract

Rainfall forecasting represents a tremendously significant matter in field of hydrology. On other hand, rainfall is one of the most complicated effective hydrological processes in rainfall prediction. This study was undertaken to develop and evaluate the applicability of Co-Active Neuro Fuzzy Interface System (CANFIS) models to simulate rainfall from a watershed. The performance of the developed models, on the basis of training and testing, was judged on the basis of three statistical measures such as root mean squared error (MSE), coefficient of efficiency (CE) and correlation coefficient (r) during monsoon period (June to September) for Umargaon area in Nagpur, Maharashtra, India. The daily data of rainfall, sunshine hours, minimum temperature, maximum temperature and evaporation data were used for rainfall simulation. The appropriate parameter combination of input variables for CANFIS was used to simulate rainfall. The Neuro Solution 5.0 software and Microsoft Excel were used in analysis and the performance evaluation of developed models, respectively. The architecture of CANFIS was designed with Gaussian membership function, Takagi-Sugeno-Kang fuzzy model, hyperbolic tangent activation function and Delta-Bar-Delta learning algorithm. The result indicated that the predicted rainfall using CANFIS model was found to be in close agreement with the observed one for the Umargaon. Therefore, according to CANFIS model, the rainfall can be simulated using the data of maximum temperature, evaporation and sunshine hours. The result indicates that the CANFIS model is suitable for rainfall prediction in Nagpur.