Forecasting contamination in an ecosystem based on a network model

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.

Automated summary

This paper introduces a new approach using artificial neural networks (ANNs) to predict heavy metal pollution based on ecological factors, removing obstacles like time-consuming laboratory procedures and high implementation costs. Pollution prediction is crucial for safety, sustainable development, and effective decision-making. By applying ANNs to the collected data of 800 plant and soil materials, this study accurately predicts pollution, offering a promising and pioneering method for scientists, conservationists, and governments to develop effective work programs for a functioning ecosystem. The relative errors for each polluting heavy metal in training, testing, and holdout data are found to be significantly low.