Abstract
Attaining resource security in the water, energy, food, and ecosystem (WEFE) sectors, the WEFE nexus, is paramount. This necessitates the use of quantitative modelling, which presents many challenges, as this is a complex system acting at the intersection of the physical- and social sciences. However, as WEFE data is becoming more widely available, data-driven methods of modelling this system are becoming increasingly viable. Here, we discuss two main problems in WEFE nexus modelling: system identification and control. System identification uses Machine Learning algorithms to obtain dynamical models from data and have shown promise in many disciplines with similar characteristics as the nexus. Meanwhile, control algorithms manipulate a system to achieve objectives and are becoming instrumental in shaping nexus policy. Despite the promise of these algorithms, data-driven modelling is a vast and daunting field, and so here we provide an introductory overview of this field, with emphasis on nexus applications.
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