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

M A Sojitra,P A Pandya

doi:10.12944/cwe.10.2.19

Abstract

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.

Highlights

Rain is the most important phase of hydrologic cycle and prime requirement for living organism
Adaptive Neuro-Fuzzy Inference System (ANFIS) and Autoregressive Moving Average (ARIMA) models were developed by Suthartono et al.6 (2012) to forecast monthly rainfall at Pujon and Wagir in Indonesia
Chien-Lin Huang et. al9 (2014) constructed a typhoon precipitation forecast model which has forecasted rainfall one to six hours in advance using optimal model parameters and structures retrieved from a combination of the adaptive network-based fuzzy inference system (ANFIS)

Summary

Introduction

Rain is the most important phase of hydrologic cycle and prime requirement for living organism It comes in different forms like fog, mist, drizzle, snow, sleet and glaze. The rain water in the form of soil moisture and groundwater are the most important requirements for agricultural production and social development in country like India where 68 per cent of total geographical are comes under the rainfed condition. Adaptive Neuro-Fuzzy Inference System (ANFIS) and Autoregressive Moving Average (ARIMA) models were developed by Suthartono et al. (2012) to forecast monthly rainfall at Pujon and Wagir in Indonesia. Al (2014) constructed a typhoon precipitation forecast model which has forecasted rainfall one to six hours in advance using optimal model parameters and structures retrieved from a combination of the adaptive network-based fuzzy inference system (ANFIS). The present study will be useful to be prepared for problems regarding scarcity and drainage of rainfall for the Udaipur city and to farming community for decision making regarding sowing of crops

MATERIALS AND METHODS

Two Takagi-Sugeno if-then rules are contained in the rule base as given below

RESULTS AND DISCUSSION

Rainfall Prediction in the Northeast