Abstract
Accurate rainfall prediction has become very complicated in recent times due to climate change and variability. The efficiency of classification algorithms in rainfall prediction has flourished. The study contributes to using various classification algorithms for rainfall prediction in the different ecological zones of Ghana. The classification algorithms include Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGB) and K-Nearest Neighbour (KNN). The dataset, consisting of various climatic attributes, was sourced from the Ghana Meteorological Agency spanning 1980 – 2019. The performance of the classification algorithms was examined based on precision, recall, f1-score, accuracy and execution time with various training and testing data ratios. On all three training and testing ratios: 70:30, 80:20 and 90:10, RF, XGB and MLP performed well, whereas KNN performed least across all zones. In terms of the execution time of the models, Decision Tree is consistently portrayed as the fastest, whereas MLP used the most run time.
Highlights
Accurate and timely rainfall prediction is expected to inject a new intervention phase to the affected sectors accosted with the negative propensities of rainfall extremes
It is observed that the performance of classifiers for the no-rain class is dominant on all three training and testing ratios which is indicative of low rainfall at the savannah zone which consistent with the findings in [18]
This research executed rainfall prediction in Ghana covering all the ecological zones using five (5) classification algorithms namely: Decision Tree, Random Forest, Multilayer Perceptron, Extreme Gradient Boosting and KNearest Neighbour. 41 years of past climatic data spanning 1980 – 2019 from the Ghana Meteorological Service was used for this study
Summary
Accurate and timely rainfall prediction is expected to inject a new intervention phase to the affected sectors accosted with the negative propensities of rainfall extremes. In developing countries, including Ghana, the primary water source for agriculture, hydropower generation, and others is rainfall Many classification algorithms such as Random Forest (RF), Decision Tree (DT), Neural Network (NN), K-Nearest Neighbour (KNN) and others have been investigated for the prediction of rainfall. [11] used machine learning techniques to build rainfall prediction models in some major cities in Australia by comparing Decision trees, Random Forest, Logistic regression, AdaBoost, Gradient boosting and KNearest Neighbour. The findings from the study showed Neural Networks with an F-score of 73.2%, which was the highest Insights from these studies suggest that machine learning algorithms perform well regarding rainfall prediction accuracy and timeliness.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
