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Water resource planning and management using motivated machine learning

Water resources planning and management require problem resolution and optimized use of resources. Since many objectives in water management are conflicting, it is hard to devise one optimum strategy. A simulation tool capable of optimized multi-objective analysis to satisfy a multiplicity of goals is needed to support water decision making. This paper suggests an integrated modelling framework to assist with time consuming and difficult tasks of decision making by water management practitioners and to harmonize economic uses of water resources. Motivated machine learning, presented in this paper, supports intelligent decision-making processes in dynamically changing environments and could be used to consider alternative water management policies. Motivated learning systems learn to properly control the environment with competing goals. They provide a natural support for multi-objective decision making in an active search for balance between conflicting situations and adverse environmental conditions. A case study of optimized machine learning water management decisions is presented.