Special Issue "Mitigation Techniques for Water-Induced Natural Disasters: The State of the Art"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management, Policy and Governance".

Deadline for manuscript submissions: 3 December 2021.

Special Issue Editors

Prof. Dr. Hongey Chen
E-Mail Website
Guest Editor
National Taiwan University, Taipei, Taiwan/National Science and Technology Center for Disaster Reduction, New Taipei, Taiwan
Interests: engineering geology; geohazards; slope development
Dr. Wei-Bo Chen
E-Mail Website
Guest Editor
National Science and Technology Center for Disaster Reduction, New Taipei 231, Taiwan
Interests: hydrodynamic and hydraulic modeling; storm tide and storm wave modeling
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

According to the 2020 edition of the United Nations World Water Development Report (UN WWDR 2020), about 74% of all natural disasters were water-related between 2001 and 2018. The total number of deaths exceeded 166,000 only because of floods and droughts during the past 20 years. Additionally, floods and droughts caused total economic damage of almost USD 700 billion and affected over 3 billion people worldwide. Water-induced natural disasters can be categorized as floods, droughts, landslides, storm surges, storm waves, and tsunami and are expected to worsen with climate change. Hence, there is still a growing demand for novel techniques that could be adopted for mitigating water-induced natural disasters.

In order to improve our capabilities and understandings for management, resilience, monitor, analysis, prediction, forecast, and hindcast of water-induced natural disasters, this Special Issue is intended to collect the latest and state-of-the-art studies on floods, droughts, landslides, storm surges, storm waves, and tsunami disasters. Research focusing on model development and applications using state-of-the-art methods is welcome. We look forward to receiving contributions in the form of research articles and reviews for this Special Issue. Topics include but are not limited to the following:

  • Monitor and prediction of natural disaster due to water-induced natural disasters;
  • Preparing an emergency evacuation plan for water-induced natural disasters;
  • Improving disaster resilience to water-induced natural disasters;
  • Statistical and big data analysis for floods, landslides, storm surges, storm waves, and tsunami disasters;
  • Artificial intelligence techniques for simulating and predicting water-induced natural disasters;
  • Risk assessment of future water-induced natural disasters;
  • Numerical method and its applications to water-induced natural disasters.

Prof. Dr. Hongey Chen
Dr. Wei-Bo Chen
Guest Editors

Manuscript Submission Information

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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.

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Keywords

  • Monitor and prediction
  • Emergency evacuation
  • Disaster resilience
  • Climate change
  • Numerical modeling
  • Statistical and big data analysis
  • Artificial intelligence techniques

Published Papers (2 papers)

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Research

Article
Prediction of River Stage Using Multistep-Ahead Machine Learning Techniques for a Tidal River of Taiwan
Water 2021, 13(7), 920; https://doi.org/10.3390/w13070920 - 27 Mar 2021
Viewed by 628
Abstract
Time-series prediction of a river stage during typhoons or storms is essential for flood control or flood disaster prevention. Data-driven models using machine learning (ML) techniques have become an attractive and effective approach to modeling and analyzing river stage dynamics. However, relatively new [...] Read more.
Time-series prediction of a river stage during typhoons or storms is essential for flood control or flood disaster prevention. Data-driven models using machine learning (ML) techniques have become an attractive and effective approach to modeling and analyzing river stage dynamics. However, relatively new ML techniques, such as the light gradient boosting machine regression (LGBMR), have rarely been applied to predict the river stage in a tidal river. In this study, data-driven ML models were developed under a multistep-ahead prediction framework and evaluated for river stage modeling. Four ML techniques, namely support vector regression (SVR), random forest regression (RFR), multilayer perceptron regression (MLPR), and LGBMR, were employed to establish data-driven ML models with Bayesian optimization. The models were applied to simulate river stage hydrographs of the tidal reach of the Lan-Yang River Basin in Northeastern Taiwan. Historical measurements of rainfall, river stages, and tidal levels were collected from 2004 to 2017 and used for training and validation of the four models. Four scenarios were used to investigate the effect of the combinations of input variables on river stage predictions. The results indicated that (1) the tidal level at a previous stage significantly affected the prediction results; (2) the LGBMR model achieves more favorable prediction performance than the SVR, RFR, and MLPR models; and (3) the LGBMR model could efficiently and accurately predict the 1–6-h river stage in the tidal river. This study provides an extensive and insightful comparison of four data-driven ML models for river stage forecasting that can be helpful for model selection and flood mitigation. Full article
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Article
An Operational High-Performance Forecasting System for City-Scale Pluvial Flash Floods in the Southwestern Plain Areas of Taiwan
Water 2021, 13(4), 405; https://doi.org/10.3390/w13040405 - 04 Feb 2021
Cited by 1 | Viewed by 544
Abstract
A pluvial flash flood is rapid flooding induced by intense rainfall associated with a severe weather system, such as thunderstorms or typhoons. Additionally, topography, ground cover, and soil conditions also account for the occurrence of pluvial flash floods. Pluvial flash floods are among [...] Read more.
A pluvial flash flood is rapid flooding induced by intense rainfall associated with a severe weather system, such as thunderstorms or typhoons. Additionally, topography, ground cover, and soil conditions also account for the occurrence of pluvial flash floods. Pluvial flash floods are among the most devastating natural disasters that occur in Taiwan, and these floods always /occur within a few minutes or hours of excessive rainfall. Pluvial flash floods usually threaten large plain areas with high population densities; therefore, there is a great need to implement an operational high-performance forecasting system for pluvial flash flood mitigation and evacuation decisions. This study developed a high-performance two-dimensional hydrodynamic model based on the finite-element method and unstructured grids. The operational high-performance forecasting system is composed of the Weather Research and Forecasting (WRF) model, the Storm Water Management Model (SWMM), a two-dimensional hydrodynamic model, and a map-oriented visualization tool. The forecasting system employs digital elevation data with a 1-m resolution to simulate city-scale pluvial flash floods. The extent of flooding during historical inundation events derived from the forecasting system agrees well with the surveyed data for plain areas in southwestern Taiwan. The entire process of the operational high-performance forecasting system prediction of pluvial flash floods in the subsequent 24 h is accomplished within 8–10 min, and forecasts are updated every six hours. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: A High-Speed City-Scale Flash Flood Forecast System for Plain Areas in the Southeast of Taiwan
Authors: Tzu-Yin Chang; Huei-Shuin Fu; Shih-Chun Hsiao; Wei-Bo Chen; Lee-Yaw Lin
Affiliation: National Science and Technology Center for Disaster Reduction, New Taipei City 23143, Taiwan; Department of Hydraulic and Ocean Engineering, National Cheng Kung University, Tainan City 70101, Taiwan
Abstract: A flash flood is rapid flooding induced by intense rainfall associated with a severe weather system such as a thunderstorm or typhoon. Additionally, topography, ground cover, and soil conditions also account for the occurrence of flash floods. Flash floods are one of the most devastating natural disasters in Taiwan and usually occur within a few minutes or hours of excessive rainfall. Flash floods usually threaten the large plain areas with high population densities; therefore, there is a great need to implement an operational high-speed forecasting system (OHFS) for flash flood predictions and evacuation decisions. This study developed a high-speed two-dimensional hydrodynamic model based on the finite element method and unstructured grids. The OHFS is composed of the Weather Research and Forecasting (WRF) Model, Storm Water Management Model (SWMM), two-dimensional hydrodynamic model, and map-oriented visualization tool. The OHFS employs the digital elevation data with a 1-m resolution to simulate the city-scale flash flood. The flooding extents of historical inundation events derived from the OHFS agree well with the surveyed data for plain areas in the southeast of Taiwan. The entire process of the OHFS for predicting flash floods in the next 24 hours is accomplished within 8–10 minutes and the forecasts are updated every six hours.

Title: A Numerical Study on Pluvial Flood Mitigation by Engineering Measures
Authors: Sen-Hai Yeh; Wen-Dar-Guo; Shen Chiang; Chih-Hsin Chang; Wei-Bo Chen
Affiliation: National Science and Technology Center for Disaster Reduction, New Taipei City 23143, Taiwan
Abstract: Pluvial flood is one of the severest natural disasters in Taiwan. A pluvial flood usually occurs in the flat or low-lying areas when the drainage system is saturated with intense rainfall. This study applied a two-dimensional (2-D) zero-inertia (diffusive wave) model to evaluate the flood mitigation performance of two engineering measures, one is a drainage improvement project (P1) and the other is a combination of drainage improvement and road raising project (P2). The Manning coefficients in the two-dimensional model were determined through the latest land-use data of the study area. The 24-hour cumulative precipitation for 5-, 25-, 100-, 500-year return periods were served as meteorological boundary conditions to drive the 2-D model. The results indicate that the utilization of the P1 was more significant in mitigating the flooding extent, the maximum reduced rate of the flooded area can reach 63% when the 24-hour cumulative precipitation of 5-year return period was imposed on the 2-D model. Regarding the decrease in the number of people impacted by flood, the P2 only performed better with a reduced rate of 69% for excreting the 24-hour cumulative precipitation of 5-year return period on the 2-D model. However, the P1 is superior to P2 in reducing the number of affected people under the conditions for 24-hour cumulative precipitation of 100-, 500-year return period. The overall ability of the engineering measures for flood mitigation decreases as the return period increases.

Title: Prediction of River Stage Using Multistep-Ahead Machine Learning Techniques for a Tidal River of Taiwan
Authors: Wen-Dar Guo; Wei-Bo Chen; Sen-Hai Yeh; Chih-Hsin Chang
Affiliation: National Science and Technology Center for Disaster Reduction, New Taipei City 23143, Taiwan
Abstract: Prediction of time series river stage during typhoon or storm periods is essential for flood control or flood disaster preventions. Data-driven model using machine learning (ML) techniques has become an attractive and effective approach to simulate and analyze river stage dynamics. However, the application of a relatively new ML technique, namely light gradient boosting machine regression (LGBMR), to predict the river stage in the tidal river is rarely investigated. In this paper, the data-driven ML models in the framework of multistep-ahead prediction methodology are presented and evaluated for river stage modelling. Four ML techniques including the support vector regression (SVR), random forest regression (RFR), multilayer perceptron regression (MLPR) and light gradient boosting machine regression (LGBMR) are employed to establish data-driven ML models with Bayesian optimization. The models are applied to simulate the river stage hydrographs for a tidal reach of the Lan-Yang River Basin in northeastern Taiwan. Historical records of rainfall, river stage and tidal level were collected from 2004 to 2017, and used for training and verifications of four ML models. Four scenarios were conducted to investigate the influence of combinations of input variables on river stage predictions. Results indicated that: (1) the tidal level at previous time stage significantly affects the prediction results; (2) the LGBMR model presents a better prediction performance than the SVR, RFR and MLPR; and (3) the LGBMR model can efficiently and accurately predict river stage for the lead time of 1–6 h in a tidal river. This study provides an extensive and insightful comparison among four ML models for modeling river stages, which could be useful for the mitigation of fluvial flooding disaster in Taiwan.

Title: A GIS-Based Flood War Game Assistance Platform-an Example of New Taipei City, Taiwan
Authors: Wen-Ray Su; Yong-Jun Lin; Chun-Hung Huang; Chun-Hung Yang; Yuan-Fan Tsai
Affiliation: National Science and Technology Center for Disaster Reduction, Taipei, Taiwan; Center for Weather Climate and Disaster Research, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan; Department of Social and Regional Development, National Taipei University of Education, Taipei, Taiwan
Abstract: The resources needed to conduct a war game is much less than that of the full-scale exercise, so it is promoted to the local government from 2009 in Taiwan. In the past, the scenarios of the war game are given before it launched, which made the participants make a response plan in advance and make it ineffective. The scenarios are not given beforehand nowadays, and we call it a no-script war game. The participants of the war game need a common platform to debriefing what they plan to do. The flooding is one of the significant disasters in Taiwan due to its geographical location. To meet the needs, we propose a GIS-Based Flood War Game Assistance Platform (FWGAP). It can perform spatial analysis quickly. Three ways in FWGAP can estimate the flooded area: (1) setting up the center and flooding depth with DEM; (2) using historical flooding spots; and (3) using potential flooding maps. FWGAP has tools for estimating the affected population and the affected vulnerable population. It also has functions of searching resources such as shelters and hospitals in nearby flooded areas. Increasing fidelity, FWGAP integrates Closed-Circuit Televisions (CCTVs), Google street maps, and 3D buildings to display the flooding areas. The applications of FWGAP in the city-level and at the district-level were shown. The survey shows that 56% of participants agreed that FGWAP helps grasp the location of the disaster relief resources in the GIS map. About half of the participants believed that no-script flooding war game using FWGAP could find the problems in the SOPs and helps horizontal coordination among departments. It concludes that FWGAP is useful. On the other hand, users comment that the estimations of the flooded areas and affected population are too large than expected and suggest to improve them.

Title: Using Ensemble Quantitative Precipitation Forecast to Develop the Early Warning System for Rainfall-Induced Shallow Landslide in Taiwan
Authors: Jui-Yi Ho; Kwan Tun Lee; Chih-Hsin Chang; Che-Hsin Liu; Wei-Bo Chen
Affiliation: National Science and Technology Center for Disaster Reduction, New Taipei, Taiwan
Abstract: Heavy rainfall brought by typhoons has been recognized as a major trigger of landslides in Taiwan. On average, 3.75 typhoons strike the island every year, and cause large amounts of shallow landslides and debris flow in mountainous region. Because landslide occurrence strongly corresponds to the storm dynamics, a reliable typhoon forecast is therefore essential to landslide hazard management in Taiwan. Given early warnings with sufficient lead time, rainfall-induced shallow landslide forecasting can help people prepare disaster prevention measures. To account for inherent weather uncertainties, this study adopted an ensemble forecasting model for executing precipitation forecasts, instead of using a single model output. A shallow landslide prediction model based on the infinite slope model and topography-based hydrology model was developed. Considering the detailed topographic characteristics of a subwatershed, the proposed model can estimate the change in saturated water levels during rainstorms and then link with the slope instability analysis to clarify whether shallow landslides can occur in the subwatershed. The proposed model has potential for application in landslide early warning systems to reduce loss of life and property.

Title: Prediction for Local Precipitation in Meiyu Season over Southern Taiwan Using Machine Learning Scheme
Authors: Jung-Lien Chu; Chou-Chun Chiang; Yi-Chiang Yu; Li-Rung Hwang; Kuan-Ling Lin; Li-Huan Hsu; Chieh-Ju Wang; Shih-Hao Su; Ting-Shuo Yo
Affiliation: Meteorological Division, National Science and Technology Center for Disaster Reduction, New Taipei, Taiwan; Department of Atmospheric Science, Chinese Culture University, Taipei, Taiwan; Department of Atmospheric Science, National Taiwan University, Taipei, Taiwan
Abstract: This study aims to investigate the potential of SVM-based machine learning scheme for predicting the heavy rainfall of southern Taiwan during Meiyu season. Station rainfall data derived from Central Weather Bureau, with the target season of May and June, is used as the predictand. The corresponded predictors are adopted from the historical data of Climate Forecast System reanalysis (CFSR). According to the analyzed result, the extreme rainfall tends to happen in the mountain area of southern Taiwan during this season. To predict the heavy rainfall, the machine learning schemes are explored and evaluated by several experiments, including the choice of predictors and study areas. Among the experiments, schemes with the predictors that are associated with Meiyu fronts and southwesterly wind would give higher skill scores than those schemes that consider the whole predictors. Generally, the results reveal that the SVM-based schemes perform better in predicting heavy rainfall over southern Taiwan during Meiyu season.

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