• google scholor

  • Views: 4693

  • PDF Downloads: 870

Comparative Study of Daily Rainfall Forecasting Models Using Adaptive-Neuro Fuzzy Inference System (ANFIS)

M. A. Sojitr1 * , R. C. Purohit1 and P. A. Pandya2

1 Department of Agricultural, Junagadh Agricultural University, Junagadh, 362001 Gujarat India

DOI: http://dx.doi.org/10.12944/CWE.10.2.19

The study was carried out to develop rainfall forecasting Models. Adaptive Neuro-Fuzzy Inference System (ANFIS) was used for developing Models rainfall of Udaipur city. Two data sets were prepared using 35 year of weather parameters i.e. wet bulb temperature, mean temperature, relative humidity and evaporation of previous day and previous moving average week were used to prepare case I and case II respectively. Gaussian and Generalized Bell membership functions were used to prepare models. Statistical and hydrologic performance indices of ANFIS (Gaussian, 5) gave better performance among developed four models. The study showed that sensitivity analysis revealed wet bulb temperature is most sensible parameter followed by mean temperature, relative humidity and evaporation.


Rainfall forecasting; Forecasting models; ANFIS; Sensitivity analysis; Fuzzy Logic Systems

Copy the following to cite this article:

Sojitra M. A, Purohit R. C, Pandya P. A. Comparative Study of Daily Rainfall Forecasting Models Using Adaptive-Neuro Fuzzy Inference System (ANFIS). Curr World Environ 2015;10(2) DOI:http://dx.doi.org/10.12944/CWE.10.2.19

Copy the following to cite this URL:

Sojitra M. A, Purohit R. C, Pandya P. A. Comparative Study of Daily Rainfall Forecasting Models Using Adaptive-Neuro Fuzzy Inference System (ANFIS). Curr World Environ 2015;10(2). Available from: http://www.cwejournal.org/?p=11969

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.