Deciding Heavy Metal Levels in Soil Based on Various Ecological Information through Artificial Intelligence Modeling

Viability of two adaptive fuzzy systems based on fuzzy c means and subtractive clustering methods for modeling Cadmium in groundwater resources

“…In another study, Sari et al used ANNs for predicting heavy metals concentration of soil samples obtained from different altitudes on Mount Ida. Result of the research showed that the computed relative errors were significantly low for each of the considered elements (Fe, Mn, and Zn); and error ranges were found to be 1.0–4.1%, 1.0–4.2%, 1.5–7.1%, respectively, for the training, testing, and holdout data [ 53 ]. Alizamir et al have reported a good generalization performance of the ELM approach in surface water management the potential of the ANN-PSO model to predict the concentration of heavy metals in the Toyserkan Plain was useful to implement sustainable policies for groundwater management [ 54 ].…”

Modeling the effects of essential heavy metals on environmental pollution: A linear and nonlinear prediction model via cascade forward‐neural network

“…Artificial neural networks (ANNs), which do not need to provide similar strict assumptions, as they can overcome this problem, have become very popular, especially in recent years. ANNs are frequently and effectively used in a wide variety of scientific disciplines, such as environmental pollution [8][9][10], biomechanics [11], climatology [12], finance and economy [13], and medical application [14]. In addition, with the aim of predicting the amount of basic heavy metals such as Fe, Mn, and Ni, various machine learning (ML) methods, including ANNs and deep neural networks, have been used in the literature [15][16][17][18][19].…”