Mathematical Modeling of Water Quality: Streams, Lakes and Reservoirs

This book is the first to deal comprehensively with the subject of mathematical modeling of water quality in streams, lakes, and reservoirs. About one third of the book is devoted to model development processes -- identification, formulation, parameter estimation, calibration, sensitivity testing, and application -- and a thorough review of the mathematical principles and techniques of modeling. Emphasis is placed on well documented models, representative of the current state of the art, to illustrate capabilities and limitations for the simulation of water quality. About two thirds of the book deals with specific applications of models for simulation of water quality in natural water bodies. Topics covered include modeling of temperature, dissolved oxygen and phytoplankton growth in streams, development and application of one-dimensional models of stratified impoundments, two- and three-dimensional modeling of circulation and water quality in large lakes, thermally stratified plumes and cooling ponds, ecology of lakes and reservoirs, modeling of toxic substances, and the use of models in water quality management and decision making

Application of AI Techniques for Identification of Unknown Groundwater Pollution Sources

A new methodology for the identification of unknown groundwater pollution sources under uncertainties and sparsity of data is developed. This methodology is based on the concept of Artificial Intelligence, machine learning and optimal statistical pattern recognition using Bayes' Optimal Decision Rule. The function of the optimal pattern recognition system is to optimally match extracted features of the simulated breakthrough curves with observed sets of concentration measurements in the field with a comparable set obtained by simulating groundwater transport for various candidate disposal conditions. In order to make the application of this methodology more practical, an Expert System (ES) was developed. The Expert System uses the results obtained by applying the optimal pattern-recognition algorithm to select a particular set of pollution-source locations and magnitudes. The Expert System also has the capability of adding measures of confidence to alternative selections of sources made by the pattern-recognition system, such that these solutions can be ranked according to the subjective confidences supplied by the users. The performance of the pattern-recognition system and the Expert System was evaluated for a selected study area