Resources such as water, energy, and food are interrelated. The interaction and dependency between water resources, energy, and agriculture is referred to as the “Νexus.” The interdependence of these resources causes some difficulties in their management. To address management challenges and respond to resource sustainability concerns, a comprehensive planning and monitoring approach, together with integrated scenario applications that consider trade-offs and synergies, is essential at regional, national, and global levels worldwide. This requires a shift away from conventional thinking. In addition, it is now of great importance to address the impacts of climate change on resources through policies that focus on adaptation, resilience, and mitigation. Therefore, producers and policymakers will need new tools and methods to look for development opportunities that can be managed wisely to be prepared for the future [1]. 

In today’s world, it is crucial to study the impact of sectoral productions at regional, national, and global levels, using innovative tools and methods such as Artificial Intelligence approaches, especially taking into account the effects of climate change, to ensure future sustainability. The introduction of an integrated planning and monitoring methodology, together with scenario applications that focus on understanding trade-offs and synergies and go beyond conventional thinking, will make it easier for policymakers to address resource sustainability issues at both basin and country levels. Consequently, this approach has the potential to foster new perspectives and agreements between all stakeholders. 

Artificial intelligence (AI) has a wide range of capabilities, including learning from data to improve performance over time, reasoning to make decisions, and problem-solving in complex scenarios. Machine learning, a subset of AI, enables systems to recognize patterns and make predictions. The capabilities of AI include learning, reasoning, problem-solving, and interacting with different types of information, making it a powerful tool for various fields [2]. 

Numerous models and tools have been used to study Νexus. A literature search on the Web of Science is shown in Figure 1. The summarized results of these studies are shown in Figure 1, most of which are based on linear models. Nevertheless, the use of models that incorporate artificial intelligence (AI) is proving to be more accurate in explaining the complex interaction between food, energy, and water resources. 

Figure 1. The interlinkages of nexus modeling. 

In addition, there are several newly used models of artificial intelligence in the Nexus approach in the literature [3], [4], [5], [6], [7], [8], [9], [10], [11]. The most important topics and relationships of these studies are presented in Figure 2. As can be seen from the Figure, the studies focus on AI-based nexus modeling in energy efficiency related to economic growth, water supply systems, and crop production. More studies on AI-based nexus modeling are needed to understand better reasoning, problem-solving, and interaction with different types of information to make AI a powerful tool for various fields. 

Figure 2. The interlinkages of nexus modeling via AI. 

Keywords: Nexus; Modelling; Artificial Intelligence. 

References 

[1] P. Behrens, M. T. H. van Vliet, T. Nanninga, B. Walsh, and J. F. D. Rodrigues, “Climate change and the vulnerability of electricity generation to water stress in the European Union,” Nat. Energy, vol. 2, no. 8, Jul. 2017. 

[2]  Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., … Zhang, J. (2021, November 28). Artificial intelligence: A powerful paradigm for scientific research. Innovation, Vol. 2. https://doi.org/10.1016/j.xinn.2021.100179 

[3] Coskun Dilcan, C., & Aydinalp Koksal, M. (2022). Forecasting The Water Consumption of Hydroelectricity Power Plants in The Context of The Water-Energy Nexus Based on An Artificial Intelligence Approach. IWA 4th Regional Conference on Diffuse Pollution & Eutrophication (IWA DIPCON) 24-28 October, Istanbul, TURKEY, 392–397. Istanbul. 

[4] Coskun Dilcan, Ç., & Aydınalp Köksal, M. (2023). A Novel Methodology to Predict the Thermal Powerplants’ Water Consumption in the Context of Water-Electricity-Climate Nexus. EWRA 2023 “Managing Water-Energy-Land-Food under Climatic, Environmental and Social Instability,” 213–215. 

[5] Govindan, R., & Al-Ansari, T. (2019, September 1). Computational decision framework for enhancing resilience of the energy, water and food nexus in risky environments. Renewable and Sustainable Energy Reviews, Vol. 112, pp. 653–668. https://doi.org/10.1016/j.rser.2019.06.015 

[6] Montazeri, A., Chahkandi, B., Gheibi, M., Eftekhari, M., Wacławek, S., Behzadian, K., & Campos, L. C. (2023). A novel AI-based approach for modelling the fate, transportation and prediction of chromium in rivers and agricultural crops: A case study in Iran. Ecotoxicology and Environmental Safety, 263. https://doi.org/10.1016/j.ecoenv.2023.115269 

[7] Tariq, R., Cetina-Quiñones, A. J., Cardoso-Fernández, V., Daniela-Abigail, H. L., Soberanis, M. A. E., Bassam, A., & De Lille, M. V. (2021). Artificial intelligence assisted techno-economic optimization scenarios of hybrid energy systems for water management of an isolated community. Sustainable Energy Technologies and Assessments, 48. https://doi.org/10.1016/j.seta.2021.101561 

[8] Uen, T. S., Chang, F. J., Zhou, Y., & Tsai, W. P. (2018). Exploring synergistic benefits of Water-Food-Energy Nexus through multi-objective reservoir optimization schemes. Science of the Total Environment, 633, 341–351. https://doi.org/10.1016/j.scitotenv.2018.03.172 

[9] Helmbrecht, J., Pastor, J., & Moya, C. (2017). Smart Solution to Improve Water-energy Nexus for Water Supply Systems. Procedia Engineering, 186, 101–109. https://doi.org/10.1016/j.proeng.2017.03.215 

[10] Zhou, Y., Guo, S., Xu, C. Y., Chang, F. J., Chen, H., Liu, P., & Ming, B. (2020). Stimulate hydropower output of mega cascade reservoirs using an improved Kidney Algorithm. Journal of Cleaner Production, 244. https://doi.org/10.1016/j.jclepro.2019.118613 

[11] D’amore, G., Di Vaio, A., Balsalobre-Lorente, D., & Boccia, F. (2022). Artificial Intelligence in the Water–Energy–Food Model: A Holistic Approach towards Sustainable Development Goals. Sustainability (Switzerland), 14(2). https://doi.org/10.3390/su14020867