Enhancement of ANN performance for remote sensing rainfall estimate in northern Algeria using ensemble learning methods

In machine learning, ensemble learning methods (ELM) consist of combining several machine learning algorithms to obtain better quality predictions compared to a single model. The basic idea of this theory is to learn a set of classifiers and allow them to vote. In this paper, to correctly apply the ELM for enhancing of an artificial neural network (ANN) performances, a strategy was devised which is to divide the data to be classified into two categories, ‘easy-to-classify’ category and ‘difficult-to-classify’ category using a main ANN. Hence, reliable ANN and unreliable ANN are created and applied for the classification of ‘easy-to-classify’ data and for the classification of ‘difficult-to-classify’ data, respectively. The AdaBoost algorithm and Bagging algorithm are implemented separately on the unreliable ANN. To increase performance, the AdaBoost results and Bagging results are merged. The developed scheme is applied to remote sensing images from Meteosat Second Generation (MSG). The final results show very interesting performances in the case of the fusion of the results from AdaBoost-ANN and the results from Bagging-ANN (Ada/Bag-ANN). Indeed, the POD, FAR, CSI and Bias pass from 87.2%, 17.4%, 80.8% and 1.3 (ANN) to 96.8%, 06.8%, 92.7% and 1.1 (Ada/Bag-ANN), respectively. The same trend was observed in the case of precipitation estimates. The estimates obtained from the developed model (Ada/Bag-ANN) largely surpass those obtained from the use of ANN without ELM. Compared to ECST (Enhanced Convective Stratiform Technique), EPSAT-SG (Second Generation Satellite Precipitation Estimation), TAMSAT (Tropical Applications of Meteorology using SATellite), and RFE-2.0 (Rain Fall Estimate) which showed correlation coefficients of 87%, 81%, 76% and 71%, respectively, the Ada/Bag-ANN method shows significantly better results with a correlation coefficient of 94%.