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Observation-Driven Understanding, Prediction, and Management in Hydrological/Hydraulic Hazard and Risk...
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Observation-Driven Understanding, Prediction, and Management in Hydrological/Hydraulic Hazard and Risk Studies

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

Deadline for manuscript submissions: closed (28 February 2021) | Viewed by 18836

Special Issue Editors

Prof. Dr. Jan Franklin Adamowski

E-Mail Website
Guest Editor
Faculty of Agricultural and Environmental Sciences, Department of Bioresource Engineering, McGill University, Montréal, QC H3A 0G4, Canada
Interests: water resources modeling; integrated, collaborative and adaptive water resources management
Special Issues, Collections and Topics in MDPI journals
Dr. Raffaele Albano

E-Mail Website
Guest Editor
School of Engineering, University of Basilicata, Potenza, Italy
Interests: floods; water resource management; soil moisture; hydrology; artificial intelligence; GIS; geomorphic method; DEM; geospatial analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This inter-Journal (IJGI/Water) Special Issue seeks to promote new and innovative studies, experiences, and models, in an effort to improve water resources management through the implementation of new algorithms, measurement systems, and Earth observation (EO) data. Challenges posed by contemporary issues such as climate change, population pressure, and increasingly complex social interactions have led to increased usage of geo-information in different phases of water resources management. Real-time access to data and the use of high-resolution spatial information provided by EO-based applications and environmental monitoring techniques have several advantages over traditional fieldwork expeditions. These include safety, the obtention of a synoptic view of the region of interest, data availability extending back several years and, in many cases, cost savings. Fortunately, the advent of new and more powerful sensors (e.g., UAVs, SAR, Lidar, GPS, citizen) provides an opportunity to image, assess, and quantify water resources management more comprehensively than ever before. Concurrently, the power of computers and newly developed algorithms has grown sharply (e.g., machine learning and system dynamic models, image classification and change detection); in particular, the integrated use of recent algorithms and EO monitoring techniques provides scientists and engineers with valuable spatial information to study hydrologic–hydraulic processes operating at different spatiotemporal scales in data-scarce environments. These studies target the monitoring and forecasting of natural risks (e.g., floods, droughts, extreme rainfall events). By providing managers and emergency officials with access to a wealth of time-continuous information for assessment and analysis of small- to large-scale natural hazards around the globe, such studies inform and improve management and emergency responses.

Contributions are solicited that address the challenge of updating and re-inventing the way water resources management and both high resource- and data-intensive processes are carried out. This Special Issue is dedicated to multi(cross/inter/trans)-disciplinary contributions with an operational user-oriented perspective, especially those focused on demonstrating the benefits of drawing upon geo-information data and models and EO sensors for water resources management.

You may choose our Joint Special Issue in ISPRS International Journal of Geo-Information.

Prof. Dr. Jan Franklin Adamowski
Dr. Raffaele Albano
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

  • geospatial information
  • remote sensing
  • citizen science
  • risk management
  • machine learning
  • environmental monitoring
  • floods prediction
  • Earth observation system
  • UAV
  • SAR
  • dynamic WebGIS
  • hydrological and hydraulic modeling
  • upscaling and downscaling
  • change detection
  • 2D and 3D mapping
  • disaster relief and recovery

Published Papers (5 papers)

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Research

17 pages, 11528 KiB  
Article
Transfer Learning with Convolutional Neural Networks for Rainfall Detection in Single Images
by Nicla Maria Notarangelo, Kohin Hirano, Raffaele Albano and Aurelia Sole
Water 2021, 13(5), 588; https://0-doi-org.brum.beds.ac.uk/10.3390/w13050588 - 24 Feb 2021
Cited by 12 | Viewed by 3732
Abstract
Near real-time rainfall monitoring at local scale is essential for urban flood risk mitigation. Previous research on precipitation visual effects supports the idea of vision-based rain sensors, but tends to be device-specific. We aimed to use different available photographing devices to develop a [...] Read more.
Near real-time rainfall monitoring at local scale is essential for urban flood risk mitigation. Previous research on precipitation visual effects supports the idea of vision-based rain sensors, but tends to be device-specific. We aimed to use different available photographing devices to develop a dense network of low-cost sensors. Using Transfer Learning with a Convolutional Neural Network, the rainfall detection was performed on single images taken in heterogeneous conditions by static or moving cameras without adjusted parameters. The chosen images encompass unconstrained verisimilar settings of the sources: Image2Weather dataset, dash-cams in the Tokyo Metropolitan area and experiments in the NIED Large-scale Rainfall Simulator. The model reached a test accuracy of 85.28% and an F1 score of 0.86. The applicability to real-world scenarios was proven with the experimentation with a pre-existing surveillance camera in Matera (Italy), obtaining an accuracy of 85.13% and an F1 score of 0.85. This model can be easily integrated into warning systems to automatically monitor the onset and end of rain-related events, exploiting pre-existing devices with a parsimonious use of economic and computational resources. The limitation is intrinsic to the outputs (detection without measurement). Future work concerns the development of a CNN based on the proposed methodology to quantify the precipitation intensity. Full article
(This article belongs to the Special Issue Observation-Driven Understanding, Prediction, and Management in Hydrological/Hydraulic Hazard and Risk Studies)
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18 pages, 5161 KiB  
Article
Prediction of Heavy Rain Damage Using Deep Learning
by Kanghyeok Lee, Changhyun Choi, Do Hyoung Shin and Hung Soo Kim
Water 2020, 12(7), 1942; https://0-doi-org.brum.beds.ac.uk/10.3390/w12071942 - 08 Jul 2020
Cited by 18 | Viewed by 3514
Abstract
Heavy rain damage prediction models were developed with a deep learning technique for predicting the damage to a region before heavy rain damage occurs. As a dependent variable, a damage scale comprising three categories (minor, significant, severe) was used, and meteorological data 7 [...] Read more.
Heavy rain damage prediction models were developed with a deep learning technique for predicting the damage to a region before heavy rain damage occurs. As a dependent variable, a damage scale comprising three categories (minor, significant, severe) was used, and meteorological data 7 days before the damage were used as independent variables. A deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN), which are representative deep learning techniques, were employed for the model development. Each model was trained and tested 30 times to evaluate the predictive performance. As a result of evaluating the predicted performance, the DNN-based model and the CNN-based model showed good performance, and the RNN-based model was analyzed to have relatively low performance. For the DNN-based model, the convergence epoch of the training showed a relatively wide distribution, which may lead to difficulties in selecting an epoch suitable for practical use. Therefore, the CNN-based model would be acceptable for the heavy rain damage prediction in terms of the accuracy and robustness. These results demonstrated the applicability of deep learning in the development of the damage prediction model. The proposed prediction model can be used for disaster management as the basic data for decision making. Full article
(This article belongs to the Special Issue Observation-Driven Understanding, Prediction, and Management in Hydrological/Hydraulic Hazard and Risk Studies)
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21 pages, 9397 KiB  
Article
Large Scale Flood Risk Mapping in Data Scarce Environments: An Application for Romania
by Raffaele Albano, Caterina Samela, Iulia Crăciun, Salvatore Manfreda, Jan Adamowski, Aurelia Sole, Åke Sivertun and Alexandru Ozunu
Water 2020, 12(6), 1834; https://0-doi-org.brum.beds.ac.uk/10.3390/w12061834 - 26 Jun 2020
Cited by 18 | Viewed by 5058
Abstract
Large-scale flood risk assessment is essential in supporting national and global policies, emergency operations and land-use management. The present study proposes a cost-efficient method for the large-scale mapping of direct economic flood damage in data-scarce environments. The proposed framework consists of three main [...] Read more.
Large-scale flood risk assessment is essential in supporting national and global policies, emergency operations and land-use management. The present study proposes a cost-efficient method for the large-scale mapping of direct economic flood damage in data-scarce environments. The proposed framework consists of three main stages: (i) deriving a water depth map through a geomorphic method based on a supervised linear binary classification; (ii) generating an exposure land-use map developed from multi-spectral Landsat 8 satellite images using a machine-learning classification algorithm; and (iii) performing a flood damage assessment using a GIS tool, based on the vulnerability (depth–damage) curves method. The proposed integrated method was applied over the entire country of Romania (including minor order basins) for a 100-year return time at 30-m resolution. The results showed how the description of flood risk may especially benefit from the ability of the proposed cost-efficient model to carry out large-scale analyses in data-scarce environments. This approach may help in performing and updating risk assessments and management, taking into account the temporal and spatial changes in hazard, exposure, and vulnerability. Full article
(This article belongs to the Special Issue Observation-Driven Understanding, Prediction, and Management in Hydrological/Hydraulic Hazard and Risk Studies)
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13 pages, 6571 KiB  
Article
Extreme Inundation Statistics on a Composite Beach
by Ahmed Abdalazeez, Ira Didenkulova, Denys Dutykh and Céline Labart
Water 2020, 12(6), 1573; https://0-doi-org.brum.beds.ac.uk/10.3390/w12061573 - 31 May 2020
Cited by 2 | Viewed by 2289
Abstract
The runup of initial Gaussian narrow-banded and wide-banded wave fields and its statistical characteristics are investigated using direct numerical simulations, based on the nonlinear shallow water equations. The bathymetry consists of the section of a constant depth, which is matched with the beach [...] Read more.
The runup of initial Gaussian narrow-banded and wide-banded wave fields and its statistical characteristics are investigated using direct numerical simulations, based on the nonlinear shallow water equations. The bathymetry consists of the section of a constant depth, which is matched with the beach of constant slope. To address different levels of nonlinearity, time series with five different significant wave heights are considered. The selected wave parameters allow for also seeing the effects of wave breaking on wave statistics. The total physical time of each simulated time-series is 1000 h (~360,000 wave periods). The statistics of calculated wave runup heights are discussed with respect to the wave nonlinearity, wave breaking and the bandwidth of the incoming wave field. The conditional Weibull distribution is suggested as a model for the description of extreme runup heights and the assessment of extreme inundations. Full article
(This article belongs to the Special Issue Observation-Driven Understanding, Prediction, and Management in Hydrological/Hydraulic Hazard and Risk Studies)
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14 pages, 7557 KiB  
Article
Smoothed Particle Hydrodynamics Modeling with Advanced Boundary Conditions for Two-Dimensional Dam-Break Floods
by Domenica Mirauda, Raffaele Albano, Aurelia Sole and Jan Adamowski
Water 2020, 12(4), 1142; https://0-doi-org.brum.beds.ac.uk/10.3390/w12041142 - 16 Apr 2020
Cited by 9 | Viewed by 3301
Abstract
To simulate the dynamics of two-dimensional dam-break flow on a dry horizontal bed, we use a smoothed particle hydrodynamics model implementing two advanced boundary treatment techniques: (i) a semi-analytical approach, based on the computation of volume integrals within the truncated portions [...] Read more.
To simulate the dynamics of two-dimensional dam-break flow on a dry horizontal bed, we use a smoothed particle hydrodynamics model implementing two advanced boundary treatment techniques: (i) a semi-analytical approach, based on the computation of volume integrals within the truncated portions of the kernel supports at boundaries and (ii) an extension of the ghost-particle boundary method for mobile boundaries, adapted to free-slip conditions. The trends of the free surface along the channel, and of the impact wave pressures on the downstream vertical wall, were first validated against an experimental case study and then compared with other numerical solutions. The two boundary treatment schemes accurately predicted the overall shape of the primary wave front advancing along the dry bed until its impact with the downstream vertical wall. Compared to data from numerical models in the literature, the present results showed a closer fit to an experimental secondary wave, reflected by the downstream wall and characterized by complex vortex structures. The results showed the reliability of both the proposed boundary condition schemes in resolving violent wave breaking and impact events of a practical dam-break application, producing smooth pressure fields and accurately predicting pressure and water level peaks. Full article
(This article belongs to the Special Issue Observation-Driven Understanding, Prediction, and Management in Hydrological/Hydraulic Hazard and Risk Studies)
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