ABSTRACT
Mathematical models used to investigate the physical, chemical, and biological properties of complex environmental systems have been well received by the scienti€c community and are widely employed in simulation, prediction, classi€cation, and decision-support inquiries. Of fundamental importance in modeling efforts is the capacity to categorize potentially harmful circumstances and substances and quantitatively evaluate the degree of possible consequences of catastrophic events. Models must factor in a wide range of interacting processes (both natural and anthropogenic) over varied time intervals in domains of different scales (local, mesoscale, regional, hemispherical, and global) with inherent uncertainties, sensitivity theory, and multidimensional factor analysis taken into consideration. Information acquired from comprehensive modeling efforts can be bene€cial for monitoring natural environmental quality, assessing risk, developing guidelines and regulations, and planning for future economic activity. Neural network approaches in particular have shown great promise in this €eld, given their capacity to model complex nonlinear systems with expansive relevance to an ever-expanding array of applications. They have the ability to distinctively model contaminants both spatially and temporally across a wide assortment of matrices, act as tools for elucidating complex interrelationships and contributory mechanisms, and have displayed widespread use and applicability in environmental decision-making and management processes.
