水-能源-粮食-生态系统关系数据驱动建模简介

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Environmental Modelling & Software Pub Date : 2024-08-10 DOI:10.1016/j.envsoft.2024.106182
Elise Jonsson , Andrijana Todorović , Malgorzata Blicharska , Andreina Francisco , Thomas Grabs , Janez Sušnik , Claudia Teutschbein
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引用次数: 0

摘要

实现水、能源、粮食和生态系统(WEFE)部门(WEFE 关系)的资源安全至关重要。这就需要使用定量建模,而定量建模会带来许多挑战,因为这是一个复杂的系统,是物理科学和社会科学的交汇点。然而,随着世界环流数据的普及,以数据为导向的系统建模方法正变得越来越可行。在此,我们将讨论 WEFE 关系建模中的两个主要问题:系统识别和控制。系统识别使用机器学习算法从数据中获取动态模型,这在许多具有与水环结类似特征的学科中都大有可为。与此同时,控制算法通过操纵系统来实现目标,并在制定纽带政策方面发挥着重要作用。尽管这些算法前景广阔,但数据驱动建模仍是一个庞大而艰巨的领域,因此我们在此对这一领域进行介绍性概述,重点介绍纽带的应用。
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An introduction to data-driven modelling of the water-energy-food-ecosystem nexus

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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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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