Artificial intelligence models for prediction of monthly rainfall without climatic data for meteorological stations in Ethiopia

Abstract

Global climate change is affecting water resources and other aspects of life in many countries. Rainfall is the most significant climate element affecting the livelihood and well-being of the majority of Ethiopians. Rainfall variability has a great impact on agricultural production, water supply, transportation, the environment, and urban planning. Because all agricultural activities and subsequent national crop production hinge on the amount and distribution of rainfall, accurate monthly and seasonal predictions of this rainfall are vital for agricultural planning. Rainfall prediction is also useful for governmental, non-governmental, and private agencies in making long-term decisions and planning in numerous areas such as farming, early warning of potential hazards, drought mitigation, disaster prevention, and insurance policy. Artificial Intelligence (AI) has been widely used in almost every area, and rainfall prediction is one of them. In this study, we attempt to investigate the use of AI-based models to predict monthly rainfall at 92 Ethiopian meteorological stations. The applicability of Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models in predicting long-term monthly precipitation was investigated using geographical and periodicity component (longitude, latitude, and altitude) data collected from 2011 to 2021. The experimental results reveal that the ANFIS model outperforms the ANN model in all assessment criteria across all testing stations. The Nash–Sutcliffe efficiency coefficients were 0.995 for ANFIS and 0.935 for ANN over testing stations.