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Bayesian model construction based on data-experts oriented approaches for assessing the phosphate effluents effects

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Abstract

In the near future, it is estimated that our world would attend a war over waters. Whereas, the effluents of the laundry of phosphate damages more and more the watershed in mining areas. The analysis of these impacts is in fact a crucial task for the preservation of the area’s water resources. In this paper, we introduced a hybrid approach-based Bayesian network model construction for assessing the phosphate laundry effluents effects. We proposed a novel discrete Bayesian Network model which is built using a hybrid approach based on Expert and data-oriented methods for structure learning. Our aim is to propose a novel data-oriented method for resolving the Bayesian network (BN) structure learning that will be improved basing on the experts’ knowledge for efficient modelling of the cause-effect relationships. And then the parameters learning procedure is performed basing on the Expectation Maximization algorithm (EM). The evaluation of the proposed data-oriented method based on two well-known benchmarking BNs demonstrates the superiority of the proposed method in terms of BN structure evaluation metrics (i.e. structure difference, correct edges, added edges, reversed edges and deleted edges). The proposed BN model allows the assessment of the groundwater quality taking into consideration several chemical factors and influencers on the absorption of discharged metals. Depending on a real collected water samples from the Gafsa phosphatic areas (southwestern Tunisia), we built the BN model which permits the analysis of different basic physico-chemical variables and its dependencies. Moreover, the generated results illustrate that our technique has higher performance compared with the other Bayesian techniques.

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Acknowledgements

The research leading to these results has received funding from the Ministry of Higher Education and Scientific Research of Tunisia under the grant agreement number LR11ES48.

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Authors and Affiliations

  1. REsearch Groups in Intelligent Machines, University of Sfax, National School of Engineers (ENIS), BP 1173, Sfax, 3038, Tunisia

    Emna Benmohamed, Hela Ltifi & Mounir Ben Ayed

  2. Computer Science and Mathematics Department, Faculty of Sciences and Techniques of Sidi Bouzid, University of Kairouan, Kairouan, Tunisia

    Hela Ltifi

  3. Computer Science and Communication Department, Faculty of Sciences of Sfax, Route Sokra Km 3.5- BP, 1171- 3000, Sfax, Tunisia

    Mounir Ben Ayed

Authors
  1. Emna Benmohamed
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  2. Hela Ltifi
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  3. Mounir Ben Ayed
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Correspondence to Emna Benmohamed.

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Appendix

Appendix

(Fig. 15)

Fig. 15
figure 15

Location of the study area in the simplified geologic map [4]

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Benmohamed, E., Ltifi, H. & Ayed, M.B. Bayesian model construction based on data-experts oriented approaches for assessing the phosphate effluents effects. Appl Intell 52, 16475–16496 (2022). https://doi.org/10.1007/s10489-021-03105-8

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