Forecasting contamination in an ecosystem based on a network model

Murat Sari*, Ibrahim Ertugrul Yalcin, Mahmut Taner, Tahir Cosgun, Ibrahim Ilker Ozyigit

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


This paper aims to predict heavy metal pollution based on ecological factors with a new approach, using artificial neural networks (ANNs), by significantly removing typical obstacles like time-consuming laboratory procedures and high implementation costs. Pollution prediction is crucial for the safety of all living things, for sustainable development, and for policymakers to make the right decisions. This study focuses on predicting heavy metal contamination in an ecosystem at a significantly lower cost because pollution assessment still primarily relies on conventional methods, which are recognized to have disadvantages. To accomplish this, the data collected for 800 plant and soil materials have been utilized in the production of an ANN. This research is the first to use an ANN to predict pollution very accurately and has found the network models to be very suitable systemic tools for modelling in pollution data analysis. The findings appear are promising to be very illuminating and pioneering for scientists, conservationists, and governments to swiftly and optimally develop their appropriate work programs to leave a functioning ecosystem for all living things. It has been observed that the relative errors calculated for each of the polluting heavy metals for training, testing, and holdout data are significantly low.

Original languageEnglish
Article number536
JournalEnvironmental Monitoring and Assessment
Issue number5
Publication statusPublished - May 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.


The authors are thankful to Dr. M. E. Uras (Marmara University, Türkiye) for his technical support.

FundersFunder number
Marmara Üniversitesi


    • Cadmium
    • Chromium
    • Environmental pollution
    • Lead
    • Neural network model


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