Forecasting contamination in an ecosystem based on a network model

Sarı M., Yalcin I. E., TANER M., Cosgun T., ÖZYİĞİT İ. İ.

Environmental monitoring and assessment, vol.195, no.5, pp.536, 2023 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 195 Issue: 5
  • Publication Date: 2023
  • Doi Number: 10.1007/s10661-023-11050-x
  • Journal Name: Environmental monitoring and assessment
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Compendex, EMBASE, Environment Index, Food Science & Technology Abstracts, Geobase, Greenfile, MEDLINE, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Page Numbers: pp.536
  • Keywords: Environmental pollution, Cadmium, Chromium, Lead, Neural network model
  • Istanbul Technical University Affiliated: Yes

Abstract

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.