Automated hydrologic forecasting using open-source sensors: Predicting stream depths across 200,000 km2

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Environmental Modelling & Software Pub Date : 2024-07-08 DOI:10.1016/j.envsoft.2024.106137
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Abstract

Wireless sensor networks support decision-making in diverse environmental contexts. Adoption of these networks has increased dramatically due to technological advances that have increased value while lowering cost. However, real-time information only allows for reactive management. As most interventions take time, predictions across these sensor networks enable better planning and decision making. Prediction models across large water level and discharge sensor networks do exist. However, they have limitations in their accessibility, automaticity, and data requirements. We present an open-source method for automatically generating computationally cheap rainfall-runoff models for any depth or discharge sensor given only its measurements and location. We characterize reliability in a real-world case study across 200,000 km2, evaluate long-term accuracy, and assess sensitivity to measurement noise and errors in catchment delineation. The method’s accuracy, computational efficiency, and automaticity make it a valuable asset to support operational decision making for diverse stakeholders including bridge inspectors and utilities.

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利用开源传感器进行自动水文预报:预测 20 万平方公里的溪流深度
无线传感器网络支持各种环境背景下的决策。由于技术进步提高了价值,同时降低了成本,这些网络的应用急剧增加。然而,实时信息只能进行被动管理。由于大多数干预措施都需要时间,因此通过这些传感器网络进行预测可以更好地进行规划和决策。目前确实存在大型水位和排水传感器网络的预测模型。然而,它们在可访问性、自动性和数据要求方面存在局限性。我们提出了一种开源方法,可为任何水深或排水传感器自动生成计算成本低廉的降雨-径流模型,只需给出其测量值和位置。我们在实际案例研究中对 20 万平方公里范围内的可靠性进行了描述,评估了长期准确性,并评估了对测量噪声和集水区划分误差的敏感性。该方法的准确性、计算效率和自动性使其成为支持包括桥梁检测人员和公用事业公司在内的各利益相关方进行运营决策的宝贵资产。
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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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