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

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Abstract

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%.

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Authors and Affiliations

  1. Laboratoire d’Analyse et de Modélisation des Phénomènes Aléatoires (LAMPA), University Mouloud MAMMERI Tizi ouzou, Tizi Ouzou, Algeria

    Youcef Attaf, Mourad Lazri & Yacine Mohia

  2. Laboratoire de Recherche en Eco-Innovation Industrielle et Energétique (LR2E), ECAM-EPMI/Quartz-Lab Cergy pontoise, Cergy-Pontoise Cedex, France

    Karim Labadi, Fethi Ouallouche & Rafik Absi

Authors
  1. Youcef Attaf
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  2. Mourad Lazri
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  4. Yacine Mohia
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  5. Fethi Ouallouche
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  6. Rafik Absi
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Contributions

Youcef Attaf: Conceptualization, methodology, writing. Mourad Lazri: Conceptualization, methodology, software, formal analysis, investigation, writing – original draft. Karim Labadi: Methodology, investigation, resources, writing – original draft, review and editing. Yacine Mohia: Conceptualization, methodology, validation, formal analysis and data curation. Fethi Oualouche: Project administration, supervision, funding acquisition and visualization. Rafik Absi: Investigation, resources, writing – original draft, review and editing.

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Correspondence to Mourad Lazri.

Additional information

Communicated by Parthasarathi Mukhopadhyay

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Attaf, Y., Lazri, M., Labadi, K. et al. Enhancement of ANN performance for remote sensing rainfall estimate in northern Algeria using ensemble learning methods. J Earth Syst Sci 133, 92 (2024). https://doi.org/10.1007/s12040-024-02303-5

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