Special Issue "Application of Numerical Models and Data-Driven Intelligent Systems in Flood Forecasting"

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

Deadline for manuscript submissions: closed (10 May 2022) | Viewed by 2325

Special Issue Editors

Prof. Dr. Ray-Shyan Wu
E-Mail Website
Guest Editor
Department of Civil Engineering, National Central University, Taoyuan, Taiwan
Interests: hydrology; disaster mitigation; water resource management
Dr. Dong-Sin Shih
E-Mail Website
Guest Editor
Department of Civil Engineering, National Yang Ming Chiao Tung University, Tainan, Taiwan
Interests: hydrology; disaster mitigation; numerical modeling

Special Issue Information

Dear Colleagues,

Floods are the most cataclysmic of disasters among the natural hazards. The World Meteorological Organization has claimed that flash floods account for approximately 85% of flooding cases and also have the highest mortality rate of the natural hazards. They are among the world’s deadliest disasters with more than 5000 lives lost annually. Thus, flood disasters not only largely affect people’s lives and properties but lead to severe damage to infrastructures and economies. However, floods are also a natural outcome of rivers and are highly nonlinear in localized watershed systems. How to establish a suitable flood forecasting system for local contexts to protect people from disaster is a crucial issue.

Accompanying the great advances in computational facilities in recent years, the use of numerical approaches to implement high-resolution simulations is becoming more feasible. In addition, given the vast range of novel technologies available in the domains of sensing systems, communication networks, cloud/edge computing, machine learning, data-driven methods, etc., the above state-of-the-art techniques are readily available for application toward establishing an intelligent flood forecasting system to protect people from danger.

We look forward to receiving contributions in the form of research articles and reviews for this Special Issue. Specific topics of interest include but are not limited to the following:

  • Smart Flood Forecasting System Using IoT & AI
  • Comparative Studies of Very Short-Term Flood Forecasting Using Physics-Based and Data-Driven Prediction Models
  • Flood Forecast and Early Warning with High-Resolution Ensemble Rainfall from Numerical Weather Prediction Model
  • Application of Numerical Models for Improvement of Flood Preparedness
  • An Operational High-Performance Forecasting System for City-Scale Pluvial Flash Floods
  • Improving Operational Flood Forecasting Using Data Assimilation
  • Flood Prediction Using Machine Learning Models

Prof. Dr. Ray-Shyan Wu
Dr. Dong-Sin Shih
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 2200 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

  • flood forecasting
  • numerical model
  • data-driven model
  • Internet of Things (IoT)
  • sensing systems
  • cloud/edge computing
  • machine learning
  • early warning systems

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Real-Time Flood Warning System Application
Water 2022, 14(12), 1866; https://doi.org/10.3390/w14121866 - 10 Jun 2022
Viewed by 421
Abstract
The reliability of weather radar data in real-time flood forecasting and early warning system remain ambivalent due to high uncertainty in Quantitative Precipitation Forecasts (QPF). In this study, a methodology is presented with the objective to improve the flood forecasting results with the [...] Read more.
The reliability of weather radar data in real-time flood forecasting and early warning system remain ambivalent due to high uncertainty in Quantitative Precipitation Forecasts (QPF). In this study, a methodology is presented with the objective to improve the flood forecasting results with the application of radar rainfall calculated in three different ways. The QPF radar rainfall forecast data of four typhoon events in Fèngshān River Basin, Taiwan, were simulated using the WASH123D numerical model. The simulated results were corrected using a physical real-time correction technique and compared with direct simulation without correction for all three QPF calculation methods. According to model performance evaluation criteria, in the third method of QPF calculation, flood peak error was the lowest in all three methods, indicating better results for flood forecasting and can be used for flood early warning systems. The impact of the real-time correction technique was assessed using mass balance analysis. It was found that flow change is between 16% and 42% from direct simulation, indicating being on the safe side in case of a flood warning. However, the impact of the real-time physical correction on the water level itself is in a reasonable range. Still, QPF rainfall correction/calculation is more important to obtain accurate results for flood forecasting. Therefore, the application of real-time correction to correct the model water level has a certain degree of credibility, which is the mass balance of the model. This approach is recommended for flood forecasting early warning systems. Full article
Show Figures

Figure 1

Article
River Stage Modeling with a Deep Neural Network Using Long-Term Rainfall Time Series as Input Data: Application to the Shimanto-River Watershed
Water 2022, 14(3), 452; https://doi.org/10.3390/w14030452 - 02 Feb 2022
Cited by 1 | Viewed by 508
Abstract
The increasing frequency of devastating floods from heavy rainfall—associated with climate change—has made river stage prediction more important. For steep, forest-covered mountainous watersheds, deep-learning models may improve prediction of river stages from rainfall. Here we use the framework of multilayer perceptron (MLP) neural [...] Read more.
The increasing frequency of devastating floods from heavy rainfall—associated with climate change—has made river stage prediction more important. For steep, forest-covered mountainous watersheds, deep-learning models may improve prediction of river stages from rainfall. Here we use the framework of multilayer perceptron (MLP) neural networks to develop such a river stage model. The MLP is constructed for the Shimanto river, which lies in southwestern Japan under a mild, rain-heavy climate. Our input for stage estimation, as well as prediction, is a long-term rainfall time series. With a one-year time series of rainfall, the model estimates the stage with RMSE less than 67 cm for about 10 m of stage peaks, as well as accurately simulating stage-time fluctuations. Furthermore, the forecast model can predict the stage without rainfall forecasts up to three hours ahead. To estimate the base flow stages as well as flood peaks with high precision, we found that the rainfall time series should be at least one year. This indicates that the use of a long rainfall time series enables one to model the contributions of ground water and evaporation. Given that the delay between the arrival time of rainfall at a rain-gauge to the outlet change is well-simulated, the physical concepts of runoff appear to be soundly embedded in the MLP. Full article
Show Figures

Figure 1

Article
Hydraulic Numerical Simulations of La Sabana River Floodplain, Mexico, as a Tool for a Flood Terrain Response Analysis
Water 2021, 13(24), 3516; https://doi.org/10.3390/w13243516 - 09 Dec 2021
Cited by 1 | Viewed by 836
Abstract
The floodplain of La Sabana River, Guerrero State, Mexico, was subject to disastrous floods due to the passage of extreme weather phenomena. This is a situation facing many ungauged rivers in Mexico, as well as in other developing countries, where increased urbanization and [...] Read more.
The floodplain of La Sabana River, Guerrero State, Mexico, was subject to disastrous floods due to the passage of extreme weather phenomena. This is a situation facing many ungauged rivers in Mexico, as well as in other developing countries, where increased urbanization and a lack of monitoring systems make many inhabited areas more vulnerable to flooding. The purpose of this work is to provide a tool for determining the flood terrain response to flooding based on a hydraulic study. This methodology combines a hydrological analysis of the river basin with the floodplain hydraulic study for the precise identification of overflow points and the resulting flood levels. Results show that, for an ungauged river, hydraulic analysis is an essential tool for determining the main potential flood points and establishing whether the river has the capacity to contain floods. Specifically, it is shown that La Sabana River is predisposed to overflow long before the river reaches its maximum flow, even in correspondence with more frequent flood scenarios. This study shows a further application that a hydraulic model can have to improve flood risk preparedness for ungauged rivers of regions where other types of monitoring tools cannot be used. Full article
Show Figures

Figure 1

Back to TopTop