Neuro-Fuzzy Approaches for Modeling the Wet Season Tropical Rainfall

Abstract

Information on rainfall variations is a matter of great importance in agricultural countries. Climate and rainfall are non-linear natural phenomena whose measurement leads to complex data, primarily due to noise patterns and distribution heterogeneity. Therefore, it is difficult to develop an appropriate model in practice by using conventional modeling techniques. This study presents the use of a neuro-fuzzy system for modeling wet season tropical rainfall. The advantage of this technique was the possibility of a modified environment of input parameters for improving the data representation. Two approaches used in the neuro-fuzzy models were classification and prediction. The neuro-fuzzy classification model firstly produced a simple rule base that enables improved interpretability of variation in rainfall rate. The given fuzzy classification rules were then utilized to generate a neuro-fuzzy inference system in order to predict rainfall variation. This approach measured the accuracy of the prediction model according to the root mean square error (RMSE) estimation. The models resulted low values of the RMSE indicated that the prediction models are reliable in representing the recent inter-annual variation of the wet season tropical rainfall.

Spatial and Temporal Analysis of Drought Forecasting on Rivers of South India

“…They showed that the same error occurred in both models. The neurofuzzy system was employed by Annas et al (2006) [12] to model tropical rainfall in the wet season. The low root mean squared error values of the models showed that the model forecasts are reliable.…”

Rainfall Events Evaluation Using Adaptive Neural-Fuzzy Inference System

“…A neural-fuzzy system for modeling wet season tropical rainfall has been used [23]. Fallah-Ghalhary et al has used fuzzy inference system in the northeast of Iran for forecasting rainfall time series based on the rainfall data [24].…”