Spatial Assessment of Water River Pollution Using the Stochastic Block Model: Application in Different Station in the Litani River, Lebanon

  • Alya ATOUI Univ Paris Est Creteil, France
  • Abir El Haj Universite de Poitiers, France
  • Yousri Slaoui Universite de Poitiers, France
  • Ali Fadel CNRS - Universite de Tours, France
  • Kamal Slim National Council for Scientific Research (CNRS), Beirut, Lebanon
  • Samir Abbad Andaloussi Univ Paris Est Creteil, France
  • Régis Moilleron Univ Paris Est Creteil,France
  • Zaher KHRAIBANI Lebanese University, Nabatieh, Lebanon
Keywords: Litani River, Water quality, Physicochemical parameters, Stochastic Block Model, Clustering, Lebanon.


Water pollution is a major global environmental problem. In Lebanon, water pollution threatens public health and biological diversity. In this work, a non-classical classification method was used to assess water pollution in a Mediterranean River. A clustering proposal method based on the stochastic block model (SBM) was used as an application on physicochemical parameters in three stations of the Litani River to regroup these parameters in different clusters and identify the evolution of the physicochemical parameters between the stations. Results showed that the used method gave advanced findings on the distribution of parameters between inter and intra stations. This was achieved by calculating the estimated connection matrices between the obtained clusters and the probability vector of belonging of the physicochemical parameters to each cluster in the different stations. In each of the three stations, the same two clusters were obtained, the difference between them was in the estimated connection matrices and the estimated cluster membership vectors. The power of SBM proposed methods is demonstrated in simulation studies and a new real application to the sampling physicochemical parameters in Litani River. First, we compare the proposed method to the classical principal component analysis (PCA) method then to the Hierarchical and the K-means clustering methods. Results showed that these classical methods gave the same two clusters as the proposed method. However, unlike the proposed SBM method, classical approaches are not able to show the blocks structure of the three stations.


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How to Cite
ATOUI, A., El Haj, A., Slaoui, Y., Fadel, A., Slim, K., Andaloussi, S. A., Moilleron, R., & KHRAIBANI, Z. (2022). Spatial Assessment of Water River Pollution Using the Stochastic Block Model: Application in Different Station in the Litani River, Lebanon. Statistics, Optimization & Information Computing, 10(4), 1204-1221.
Research Articles