Untangling hybrid hydrological models with explainable artificial intelligence

Author:

Althoff Daniel1,Bazame Helizani Couto2,Nascimento Jessica Garcia2

Affiliation:

1. Agricultural Engineering Department, Federal University of Viçosa (UFV), Av. Peter Henry Rolfs, s.n., Viçosa, MG 36570-900, Brazil

2. Biosystems Engineering Department, College of Agriculture ‘Luiz de Queiroz’, University of São Paulo (ESALQ/USP), Av. Pádua Dias, 11, Piracicaba, SP 13418-900, Brazil

Abstract

Abstract Hydrological models are valuable tools for developing streamflow predictions in unmonitored catchments to increase our understanding of hydrological processes. A recent effort has been made in the development of hybrid (conceptual/machine learning) models that can preserve some of the hydrological processes represented by conceptual models and can improve streamflow predictions. However, these studies have not explored how the data-driven component of hybrid models resolved runoff routing. In this study, explainable artificial intelligence (XAI) techniques are used to turn a ‘black-box’ model into a ‘glass box’ model. The hybrid models reduced the root-mean-square error of the simulated streamflow values by approximately 27, 50, and 24% for stations 17120000, 27380000, and 33680000, respectively, relative to the traditional method. XAI techniques helped unveil the importance of accounting for soil moisture in hydrological models. Differing from purely data-driven hydrological models, the inclusion of the production storage in the proposed hybrid model, which is responsible for estimating the water balance, reduced the short- and long-term dependencies of input variables for streamflow prediction. In addition, soil moisture controlled water percolation, which was the main predictor of streamflow. This finding is because soil moisture controls the underlying mechanisms of groundwater flow into river streams.

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

IWA Publishing

Subject

Management, Monitoring, Policy and Law,Environmental Science (miscellaneous),Water Science and Technology

Reference52 articles.

1. Modeling monthly streamflow in mountainous basin by MARS, GMDH-NN and DENFIS using hydroclimatic data;Neural Computing and Applications,2020

2. Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model;Stochastic Environmental Research and Risk Assessment,2020

3. ETo-Brazil: a daily gridded reference evapotranspiration data set for Brazil (2000–2018);Water Resources Research,2020

4. Köppen's climate classification map for Brazil;Meteorologische Zeitschrift,2013

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3