人工智能在水文研究和流域生态修复中的应用综述

Fernando Morante-Carballo , Mirka Arcentales-Rosado , Jhon Caicedo-Potosí , Paúl Carrión-Mero
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引用次数: 0

摘要

水资源管理是河流流域可持续发展的基础。水质受到人类活动污染的影响。在这方面,恢复退化的流域有助于土壤恢复、可持续水资源管理、重新造林、生物多样性保护和减轻人类影响。人工智能(AI)通过优化水文研究和生态恢复中的决策和数据分析,创新数据管理和分析过程。本研究旨在通过分析科学数据库的知识结构、研究方向和趋势,分析与人工智能在水文地质与流域生态恢复研究中的整合相关的科学信息。该方法包括三个阶段:i)搜索标准和数据处理(Scopus-Web of Science);Ii)智力和概念结构分析;iii)应用系统评价和荟萃分析(PRISMA)方法的首选报告项目。结果表明,共有171项记录,近四年科学产出增长4.49%,主要集中在人工神经网络(10.53%)、人工智能(3.51%)、遗传算法(1.17%)和机器学习(1.17%)。这种增加是由于近年来人为压力造成的气候变化,特别是在农业部门,由于对化肥和农药污染的高需求。这一问题促使人们寻求影响更深远的环境管理技术,使其成为一个潜在的研究领域。中国(72.51%)和美国(25.73%)是该地区产量贡献最突出的国家。另一方面,发展中国家在这方面的研究较少,如南非(2.92%)、哥伦比亚(1.17%)和阿根廷(0.58%)等。该分析确定了将人工智能应用于水资源优化和水质预测的机遇和挑战,为可持续流域管理提供了创新的概念框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial intelligence applications in hydrological studies and ecological restoration of watersheds: A systematic review
Water resources management is fundamental to the sustainability of river basins. Water quality is affected by pollution caused by human activities. In this context, the restoration of degraded watersheds helps soil recovery, sustainable water management, reforestation, biodiversity conservation and mitigation of human impacts. Artificial intelligence (AI) innovates data management and analysis processes by optimising decision-making and data analysis in hydrological studies and ecological restoration. This research aims to analyse scientific information related to the integration of AI in studies on hydrogeology and ecological restoration of watersheds by analysing scientific databases for knowledge of the intellectual structure, lines and trends of research. The methodology includes three phases: i) search criteria and data processing (Scopus-Web of Science); ii) analysis of the intellectual and conceptual structure; and iii) application of the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) method. The results indicate that there is a total of 171 records, with a 4.49% growth in scientific production in the last four years, focusing on artificial neural networks (10.53%), artificial intelligence (3.51%), genetic algorithms (1.17%) and machine learning (1.17%). This increase is due to the climatic variation generated in recent years, driven by anthropogenic pressures, especially in the agricultural sector due to the high demand for fertiliser and pesticide pollution. This problem has prompted the search for more far-reaching environmental management technologies, making it a potential niche for study. China (72.51%) and the United States (25.73%) are the most outstanding contributors to production in this area. On the other hand, there is less research in this area in developing countries such as South Africa (2.92%), Colombia (1.17%), and Argentina (0.58%), among others. This analysis identifies opportunities and challenges in applying AI for water resource optimisation and water quality prediction, providing an innovative conceptual framework for sustainable watershed management.
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