A Hybrid ANFIS-GA Approach for Estimation of Regional Rainfall Amount

Abstract

Effective use and management of ever-diminishing water resources are critically important to thefuture of humanity. At this point, rainfall is one of the most important factors that supply waterresources, but the fact that the rainfall higher is more than normal causes many disasters such asflood, erosion. Therefore, rainfall amount must be analyzed mathematically, statistically orheuristically in order to take precautions, in the region. In this study, an Adaptive Neuro FuzzyInference System - Genetic Algorithm (ANFIS-GA) based hybrid model was proposed forestimation of regional rainfall amount. Purpose of the study is to minimize the loss of life andgoods for people of the region by estimating the amount of annual rainfall and ensuring effectivemanagement of water resources and allowing some evaluations and preparations according topossible climate changes. The estimation model was developed by coding in the MATLABpackage program. In the development of the model, 3650 meteorological data from 2008-2018years belonging to Basel, a Swiss city, were utilized. The real data were tested on both theArtificial Neural Network (ANN) and the hybrid ANFIS-GA model. The obtained resultsdemonstrated that the training R-value of the suggested ANFIS-GA model was 0.9920, the testingR-value was 0.9840 and the error ratio was 0.0011. This clearly shows that predictiveperformance of the model is high and error level is low, and therefore that hybrid approaches suchas ANFIS-GA can be easily used in predicting meteorological events.