Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.8
3.0
Journals
Water
Special Issues
Advances in Hydroinformatics for Flood Modelling for Management
share announcement

Advances in Hydroinformatics for Flood Modelling for Management

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydraulics and Hydrodynamics".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 4709

Special Issue Editors

Dr. Ioana Popescu

E-Mail Website
Guest Editor
IHE Delft Institute for Water Education, 2611 AX Delft, The Netherlands
Interests: computational methods; aspects of flood modeling and vulnerability related to floods; lake and reservoir modeling and water supply systems modeling and optimisation
Dr. Andreja Jonoski

E-Mail Website
Guest Editor
Hydroinformatics Chair Group at IHE Delft Institute for Water Education, 2611 AX Delft, The Netherlands
Interests: hydroinformatics; hydrology; water resources; groundwater; decision support systems

Special Issue Information

Dear Colleagues,

You are invited to submit to this Special Edition addressing novel concepts and innovative approaches related to modeling for floods in view of better management. The implementation of hydroinformatics techniques for supporting decisions in the case of floods is the main focus of this Special Issue. Papers covering reviews, methods, and original research articles are all welcome.

The journal Special Issue invites papers on topics across a broad spectrum of hydroinformatics, from flood processes, modeling, risk, and citizen science contributions to flood modeling, remote sensing, and mobile phone apps, for better decision making and management of floods.

Dr. Ioana Popescu
Dr. Andreja Jonoski
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • hydroinformatics
  • decision support systems
  • flood modeling
  • simulation optimization for flood models
  • earth observation data for flood modeling
  • physically based models
  • AI for flood modeling
  • flood modeling/management with stakeholders

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

25 pages, 54309 KiB  
Article
A Sink Screening Approach for 1D Surface Network Simplification in Urban Flood Modelling
by Guohan Zhao, Ole Mark, Thomas Balstrøm and Marina B. Jensen
Water 2022, 14(6), 963; https://doi.org/10.3390/w14060963 - 18 Mar 2022
Cited by 5 | Viewed by 1972
Abstract
Sinks configure the surface networks for overland flow processes representations during 1D hydrodynamic modelling. The excessive number of sinks detected from high-resolution DEMs can boost 1D computational costs significantly. To pursue optimal sink numbers and their optimal spatial distribution, a Volume Ratio Sink [...] Read more.
Sinks configure the surface networks for overland flow processes representations during 1D hydrodynamic modelling. The excessive number of sinks detected from high-resolution DEMs can boost 1D computational costs significantly. To pursue optimal sink numbers and their optimal spatial distribution, a Volume Ratio Sink Screening (VRSS) method was developed to screen for computationally important sinks, while compensating for volume losses from removed (unimportant) sinks, such that 1D hydrodynamic modelling yields faster computing times without significant loss of accuracy. In comparison with an existing geometry-based sink screening method, we validated this method by conducting sensitivity analyses for the proposed screening criteria in three Danish case areas of distinct topographies. Two iterative procedures were programmed to assess and compare their sink screening performances in terms of sink number reductions and volume loss reductions, and a volume loss solver was developed to quantify catchment-wide volume losses in the 1D surface network. Compared to a geometry-based sink screening method, the VRSS method performs more robustly and produces more efficient reductions in the number of sinks, as well as efficient reductions in volume losses. Full article
(This article belongs to the Special Issue Advances in Hydroinformatics for Flood Modelling for Management)
►▼ Show Figures

Figure 1

15 pages, 2275 KiB  
Article
Lost in Optimization of Water Distribution Systems: Better Call Bayes
by Antonio Candelieri, Andrea Ponti, Ilaria Giordani and Francesco Archetti
Water 2022, 14(5), 800; https://doi.org/10.3390/w14050800 - 3 Mar 2022
Cited by 2 | Viewed by 2153
Abstract
The main goal of this paper is to show that Bayesian optimization can be regarded as a general framework for the data-driven modelling and solution of problems arising in water distribution systems. Scenario-based hydraulic simulation and Monte Carlo are key tools in modelling [...] Read more.
The main goal of this paper is to show that Bayesian optimization can be regarded as a general framework for the data-driven modelling and solution of problems arising in water distribution systems. Scenario-based hydraulic simulation and Monte Carlo are key tools in modelling in water distribution systems. The related optimization problems fall into a simulation/optimization framework in which objectives and constraints are often black box. Bayesian optimization (BO) is characterized by a surrogate model, usually a Gaussian process but also a random forest, as well as neural networks and an acquisition function that drives the search for new evaluation points. These modelling options make BO nonparametric, robust, flexible, and sample efficient, making it particularly suitable for simulation/optimization problems. A defining characteristic of BO is its versatility and flexibility, given, for instance, by different probabilistic models, in particular different kernels, different acquisition functions. These characteristics of the Bayesian optimization approach are exemplified by two problems: cost/energy optimization in pump scheduling and optimal sensor placement for early detection of contaminant intrusion. Different surrogate models have been used both in explicit and implicit control schemes, showing that BO can drive the process of learning control rules directly from operational data. BO can also be extended to multi-objective optimization. Two algorithms are proposed for multi-objective detection problems using two different acquisition functions. Full article
(This article belongs to the Special Issue Advances in Hydroinformatics for Flood Modelling for Management)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop