A hierarchical Food-Energy-Water Nexus (FEW-N) decision-making approach for Land Use Optimization

Abstract

The land use allocation problem is an important issue for a sustainable development. Land use optimization can have a profound influence on the provisions of interconnected elements that strongly rely on the same land resources, such as food, energy, and water. However, a major challenge in land use optimization arises from the multiple stakeholders and their differing, and often conflicting, objectives. Industries, agricultural producers and developers are mainly concerned with profits and costs, while government agents are concerned with a host of economic, environmental and sustainability factors. In this work, we developed a hierarchical FEW-N approach to tackle the problem of land use optimization and facilitate decision making to decrease the competition for resources and significantly contribute to the sustainable development of the land. We formulate the problem as a Stackelberg duopoly game, a sequential game with two players – a leader and a follower (Stackelberg, 2011). The government agents are treated as the leader (with the objective to minimize the competition between the FEW-N), and the agricultural producers and land developers as the followers (with the objective to maximize their profit). This formulation results into a bi-level mixed-integer programming problem that is solved using a novel bi-level optimization algorithm through ARGONAUT. ARGONAUT is a hybrid optimization framework which is tailored to solve high- dimensional constrained grey-box optimization problems via connecting surrogate model identification and deterministic global optimization. Results show that our data-driven approach allows us to provide feasible solutions to complex bi-level problems, which are essentially very difficult to solve deterministically.

A Survey on Sustainable Surrogate-Based Optimisation

“…Examples of optimisers used in the SMBO framework are NSGA-II [47,48], simulated annealing [35], multi-objective particle swarm optimisation [49], and more. Bi-level optimisation problems were often the result of having multiple stakeholders-which is common in sustainable applications, such as road users and government [50], or land owners and government [42]. OtP problems often appeared when outside factors such as traffic load or weather came into play [39,51].…”

“…The framework is the same as in Figure 1, but with f consisting of two nested problems. An example of an included study that uses this framework for sustainable land development is [42], where the problem is formulated as a Stackelberg game between government (who wants to balance land use between food, energy and water) and land developers (who want to maximise profit), and a surrogate model approximates the decisions of the land developers.…”

A Neural Network Based Superstructure Optimization Approach to Reverse Osmosis Desalination Plants

“…To generate sustainable solutions for RO systems it is vital to consider the system implications to resources other than water, including energy and food, referred to as food-energy-water nexus (FEWN) [49][50][51][52] when optimizing the system. Namany et al optimized the FEWN for various food security scenarios realizing that the utilization of RO reduces the environmental impact of solutions [53].…”

Urban Rain Flood Ecosystem Design Planning and Feasibility Study for the Enrichment of Smart Cities

“…These climatic changes have led to the rising demand for food, water, and energy resources worldwide [10,11]. Thus, a systematic approach involves both the interdisciplinary and transdisciplinary research initiatives to deal with the complex challenges [12,13]. The challenges lied in the lack of human expertise to deal with the climatic changes, inadequate human resources, low budget, and less autonomy in risk management due to government authorities' dependence [14,15].…”