SeqIA: A Python framework for extracting drought impacts from news archives

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software Pub Date : 2025-02-24 DOI:10.1016/j.envsoft.2025.106382

Miguel López-Otal , Fernando Domínguez-Castro , Borja Latorre , Javier Vela-Tambo , Jorge Gracia

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引用次数: 0

Abstract

Drought is a hazard that causes great economic, ecological, and human loss. With an ever-growing risk of climate change, their frequency and magnitude are expected to increase. While there are many indices and metrics available for the analysis of droughts, assessing their impacts represents one of the best ways to understand their magnitude and extent. However, there are no systematic records outlining these impacts.

To help in their ongoing creation, we present a software framework that leverages raw newspaper articles, identifies any drought-related ones, and automatically classifies them according to a set of socioeconomic impacts. The information is provided to the user in a structured format, including geographical coordinates and their date of reporting. Our approach employs state-of-the-art Transformer-based Natural Language Processing (NLP) techniques, which achieve great accuracy. We currently support newspaper articles in the Spanish language within Spain, but our framework can be expanded to other countries and languages.

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来源期刊

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.