MULTIOBJECTIVE ECONOMETRIC SYSTEM OPTIMIZATION METHOD: AN APPROACH FOR WATER RESOURCES PLANNING AND MANAGEMENT

A Multiobjective Econometric System Optimization Methodology (MESOM) has been developed. MESOM uses an integrative approach of dynamic econometric systems, closeness-measure methods and mathematical optimization techniques for planning and managing of complex water resource systems, while adhering strictly to a faithful and accurate representation of the real system.; MESOM incorporates both the commensurable and noncommensurable objectives, and purposes such as water quality and quantity, demographic, economic, social and political issues, all to be combined into a proposed Best-Compromise-Optimization (BCO) approach. This provides a powerful tool that can generate a wide range of policy-decision alternatives and at the same time allows for a more realistic evaluation of the impact of critical hydrologic periods, land use planning, tax and price policies, water conservation policies, resources and technical constraints, and other common situations in a watershed that has never been considered in an integrative scheme before. MESOM is much more successful than the other models which have been used for planning and managing water resources because it successfully integrates all of these factors, without simplifying or changing the real situation.; MESOM has been tested, using data associated with the groundwater management problem in Orange County, California, USA. The research was conducted over a data set of 31 years, (1950-1981) and its results have been successfully applied for planning and management purposes for the year 1982. The MESOM has been developed as a dynamic econometric system to be integrated into an algorithm with a BCO approach. The econometric model is linear and relates exogenous (independent) to endogenous (dependent) variables by a system of ten linear simultaneous equations. The coefficients of this model are estimated by the Three Stage Least Squares (3SLS) method. The BCO approach is a fusion of the QP and LP methods; these methods are solved by the Wolfe's and Simplex's programming algorithms, respectively.