Abstract:
One of the main fields of weather forecasting is rainfall prediction, which is significant for water
resource management, food production plan and different activity plans in nature. The appearance of
stretched dry period or intensive rain at the critical stages of the crop growth and development may
lead to serious reduce crop yield. Certainly, the accurate forecasting in rainfall could present useful
information for water resource administration, flood control and disaster relief. This study proposed
several soft computing models for long term rainfall prediction based on monthly meteorological
dataset for 13 years, the models are IBK, K-Star, M5P, adaptive neuro-fuzzy inference system
(ANFIS), Meta vote, bagging, staking and ensemble by using different machine learning schemes
such as hybrid intelligent system, data mining, meta learning and ensemble algorithms.. The results
show the accuracies of both ANFIS and the ensemble model are satisfied and ANFIS showed
relatively more accurate results.
