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Lost in calibration: why people still do not calibrate their models, and why they still should – a case study from urban drainage modelling

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TLDR
The comparison of the model results using the different typical design storm events from all the surrounding data points showed substantial differences for the assessment of the sewers regarding urban flooding, emphasizing the necessity of uncertainty analysis for hydrodynamic models.
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This article is published in Water Science and Technology.The article was published on 2016-11-18 and is currently open access. It has received 37 citations till now. The article focuses on the topics: Uncertainty analysis.

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Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network

TL;DR: This work proposes a new approach that exploits existing surveillance camera systems to provide qualitative flood level trend information at scale and uses a deep convolutional neural network to detect floodwater in surveillance footage and a novel qualitative flood index as a proxy for water level fluctuations visible from a surveillance camera's viewpoint.
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Towards a smart water city: A comprehensive review of applications, data requirements, and communication technologies for integrated management

TL;DR: In this paper, a review of existing and potential applications related to network-based urban water infrastructure (UWI) is presented, characterised by different spatial and temporal resolution of measurement and control data.
Journal ArticleDOI

Towards a smart water city: A comprehensive review of applications, data requirements, and communication technologies for integrated management

TL;DR: In this article , a review of existing and potential applications related to network-based urban water infrastructure (UWI) is presented, characterised by different spatial and temporal resolution of measurement and control data.
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Event selection and two-stage approach for calibrating models of green urban drainage systems

TL;DR: In this article, 14 single-and two-stage strategies for selecting the calibration events were tested in calibration of a high and low-resolution Storm Water Management Model (SWMM) of a predominantly green urban area.
References
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River flow forecasting through conceptual models part I — A discussion of principles☆

TL;DR: In this article, the principles governing the application of the conceptual model technique to river flow forecasting are discussed and the necessity for a systematic approach to the development and testing of the model is explained and some preliminary ideas suggested.
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Software, Data and Modelling News: A new applications manual for the Storm Water Management Model (SWMM)

TL;DR: A new manual, the ''SWMM Applications Manual'', has been added to this collection, which contains nine worked-out examples addressing common stormwater management and design problems encountered in practice.
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Automatic Calibration of the U.S. EPA SWMM Model for a Large Urban Catchment

TL;DR: In this article, the authors used GIS and stormwater model with a constrained optimization technique to estimate runoff parameters, and ten storms were used for calibration and validation, and the calibrated model predicted the observed outputs with reasonable accuracy.
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Towards a roadmap for use of radar rainfall data in urban drainage

TL;DR: In this article, the uncertainties and methods for obtaining accurate rainfall measurements at high resolution and with areal extents consistent with urban catchments are described and improvement of quantitative precipitation measurements are described in this overview with specific research and operational examples of radar use in urban hydrology.
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Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling

TL;DR: It was found that SCEM-UA and AMALGAM produce results quicker than GLUE in terms of required number of simulations, but modellers should select the method which is most suitable for the system they are modelling, as GLUE requires the lowest modelling skills and is easy to implement.
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