Abstract
Predicting the amount of daily rainfall improves agricultural productivity and secures food and water supply to keep citizens healthy. To predict rainfall, several types of research have been conducted using data mining and machine learning techniques of different countries’ environmental datasets. An erratic rainfall distribution in the country affects the agriculture on which the economy of the country depends on. Wise use of rainfall water should be planned and practiced in the country to minimize the problem of the drought and flood occurred in the country. The main objective of this study is to identify the relevant atmospheric features that cause rainfall and predict the intensity of daily rainfall using machine learning techniques. The Pearson correlation technique was used to select relevant environmental variables which were used as an input for the machine learning model. The dataset was collected from the local meteorological office at Bahir Dar City, Ethiopia to measure the performance of three machine learning techniques (Multivariate Linear Regression, Random Forest, and Extreme Gradient Boost). Root mean squared error and Mean absolute Error methods were used to measure the performance of the machine learning model. The result of the study revealed that the Extreme Gradient Boosting machine learning algorithm performed better than others.
Highlights
Based on the distribution of rainfall in Ethiopia, three distinct seasons are identified which are Belg, Kiremt and Bega
All relevant environmental features important for rainfall prediction were not used. this paper examined the machine learning algorithms using data collected from one meteorology station which is relatively small in size and selected the appropriate environmental features that correlate with rainfall positively or negatively to examine the performance of the daily rainfall amount prediction machine learning algorithms using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE)
The Pearson Correlation coefficient experimental results on the given data showed that the attributes such as year, month, day, and wind speed had no significant impact on the prediction of rainfall
Summary
Based on the distribution of rainfall in Ethiopia, three distinct seasons are identified which are Belg, Kiremt and Bega. According to Ehsan et al [1] three seasons are; the ‘short’ rains (belg: February–May), followed by the long rains (kiremt: June–September) and the dry season (Bega: October–January). Kiremt is the main Ethiopian rainy season, and Ethiopia receives a substantial fraction of its annual rainfall during this season, which is very important for its water resources management and agriculture production. The northwestern part of the country at which this research is conducted experiences higher rainfall amounts from June to September that send a flood into the Blue Nile. Droughts and floods have been a major and persistent challenge of the management of water resources, agroeconomic, livestock growth, and food production in Ethiopia. To use the rainfall water efficiently, rainfall prediction is unquestionable research area in Ethiopia
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