Supplementary code for model the ecological risk index (ERI) values of selected heavy metals in the bottom sediments of Zemborzyce Lake

Introduction This repository contains MATLAB and R scripts, as well as corresponding datasets, used to model the ecological risk index (ERI) values of selected heavy metals, including Chromium (Cr), Nickel (Ni), Copper (Cu), Zinc (Zn), Cadmium (Cd), and Lead (Pb) in the bottom sediments of Zemborzyce Lake. - In MATLAB, artificial neural network (ANN) models were developed using the Neural Network App to predict ERI values at a specific depth level based on surface layer ERI values. - In RStudio, multiple linear regression (MLR) models were developed to estimate ERI values for each metal using surface layer data. # Files in the Repository 1. MATLAB Files: ANN_Zn.m - Script for training the neural network to predict ERI values for Zinc (Zn). ANN_Ni.m - Script for training the neural network to predict ERI values for Nickel (Ni). ANN_Cu.m - Script for training the neural network to predict ERI values for Copper (Cu). ANN_Cr.m - Script for training the neural network to predict ERI values for Chromium (Cr). ANN_Cd.m - Script for training the neural network to predict ERI values for Cadmium (Cd). ANN_Pb.m - Script for training the neural network to predict ERI values for Lead (Pb). data.mat - MATLAB data file containing the training and validation dataset used for the modeling of ecological risk index values. 2. R Files: All-MLR.R: R script for creating multiple linear regression models to predict ERI values for all elements. [ metal ].xlsx: Data files containing learning datasets for individual metals (e.g., Cr.xlsx, Ni.xlsx). [ metal ]test.xlsx: Datasets containing test data for each metal (e.g., Crtest.xlsx, Nitest.xlsx). [ metal ]test2.xlsx: Datasets containing the entire dataset for each metal (e.g., Crtest2.xlsx, Nitest2.xlsx). # Data Description The datasets include ERI values for the heavy metals Chromium (Cr), Nickel (Ni), Copper (Cu), Zinc (Zn), Cadmium (Cd), and Lead (Pb) in the surface layer and a specified depth level of Zemborzyce Lake sediments. The goal of both the ANN and MLR models is to predict ...