Evolutionary Multi-objective Optimization Algorithms To Environmental Management and Planning With Water Resources Case Studies

Environmental management and planning problems cover important real life areas. These problems may include the scarcity of groundwater resource, the optimality of a multi-reservoir system, the management of forest resources, the air quality monitoring networks, the municipal solid waste policies, etc. Management and planning targets by authorities consist in allocations at appropriate places and times, protection from disasters, maintenance of quality (e.g., water quality, water pollution control, nitrate concentration diminishing), sustainable development of the groundwater resources. The formalization of such optimization problems includes multiple objectives and constraints. The multiple objectives consist in maximizing/minimizing of various aspects of environmental management, e.g., maximizing of irrigation releases, maximizing the hydropower production, maximizing net returns, minimizing costs, minimizing the investment in water development, minimizing groundwater quality deterioration, etc.. Physical, biological, economic and environmental constraints are e. g., constraint of surface water balance, water supply constraints, water quality constraints, economic constraints (demand, resource costs, etc.), reservoir storage constraints. The eco-environmental objectives are often conflicting (e.g., the optimum use of water resources under conflicting demands. The use of multi-objective optimization allows a simultaneous treatment of all the objectives and constraints. The solutions take the form of non-dominated Pareto solutions, which enable the decision makers to study the tradeoffs between the objectives (e.g. , between profitability and risks). Most of the environmental domains are faced to uncertainties due to variability (e.g., climate, rainfalls, hydrologic variability, environmental policy, markets, etc.), imprecision and lack of data, vagueness of judgments by decision makers. These uncertainties lead to extend the analysis to fuzzy environments. This presentation is then concerned with decision-making methods in an environmental management and planning, where multiple conflicting objectives are used under a fuzzy environment by using a niched Pareto algorithm. Key-Words: multi-objective optimization – evolutionary algorithm – genetic search method fuzzy data environmental management – water resources and forest planning.