Regression Modelling for Precipitation Prediction Using Genetic Algorithms
Asyrofa Rahmi, Wayan Firdaus Mahmudy
This paper discusses the formation of an appropriate regression models in precipitation prediction. Precipitation prediction has a major influence to multiply the agricultural production of potatoes for some farmers in Tengger, East Java, Indonesia. Periodically, the precipitation has non-linear patterns. By using a non-linear approach, problems in prediction of precipitation shows the results closer. Genetic algorithm functioning choose precipitation period which which form the best model. To prevent early convergence, testing the best combination value of crossover rate and mutation rate. Based on the RMSE value of each methods on every locations, prediction using GA-Non Linear Regression is better than Fuzzy Tsukamoto for each locations. Compared to GSTAR-SUR, precipitation prediction using GA is better. This has been proved that for 3 locations GA is superior and on 1 location, GA has least value of deviation level.