Performance Evaluation of Machine Learning Models in Predicting Dry and Wet Climatic Phases

Document Type : Regular Article


Department of Civil Engineering, University of Ibadan, Ibadan, Nigeria


Water resource and environmental engineers need accurate information in harnessing water for diverse uses, therefore it is expedient to accurately predict dry and wet climatic phases in order to ensure optimum water resource planning and management. This study examined the applicability of machine learning models for the prediction of extreme dry and wet conditions in Minna, North Central Nigeria. Recorded rainfall, maximum temperature, minimum temperature, relative humidity, wind speed, sunshine hours and estimated potential evapotranspiration were used as predictors in the machine learning models, while hydrological extremes estimated from standardized precipitation index (SPI) served as a response variable. The performance of Support Vector Machine (SVM) based on different kernel types and Artificial Neural Network (ANN) based on different network structures were assessed for the prediction of the different phases of the climate of the study area. The study showed that while normal meteorological conditions occurred for about 74.8% of the study period, 8.9%, 4.6% and 1.9% of this period were moderately wet, severely wet and extremely wet respectively, and 4.7%, 3.4% and 1.7% of the study period were moderately dry, severely dry and extremely dry respectively. Furthermore, SVM based on Radial Basis Kernel with a coefficient of determination of 0.64 outperformed other SVM types and ANN with two hidden layers; with of coefficient of determination 0.68 was found to perform better than ANN with single layers. Generally, ANN was found to have higher accuracy than SVM in predicting dry and wet climatic phases in North Central Nigeria.


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