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Flood Risk Assessment Using Deep Learning and State-of-the-Art Machine Learning

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Hazards and Sustainability".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 10314

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Special Issue Editors

Dr. Binh Thai Pham

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Guest Editor

Department of Geotechnical Engineering, University of Transport Technology, Hanoi 100000, Vietnam
Interests: geotechnical engineering; natural hazard assessment; groundwater; machine learning; GIS; remote sensing

Dr. Indra Prakash

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Guest Editor

Geological Survey of India, Dy.DG (R), Sector-10 A, Gandhingar 382010, India
Interests: geological engineering; application of GIS and machine learning in water resources and geotechnical engineering

Special Issue Information

Dear Colleagues,

Flood is one of the most devastating natural hazards affecting society by damaging properties, disrupting communication, and causing death all around the world. Recently, anthropogenic activities such as deforestation and land-use pattern changes have contributed to severe flooding incidents. The incidence of flash floods worldwide has been increasing due to climate change, especially in coastal and low-lying areas. Floods in hilly areas are also caused by the sudden breaking of glaciers (natural dams) or melting of ice in higher elevation river valleys, causing huge devastation in the surrounding and downstream areas. Therefore, prior flood risk assessment of an area is extremely important for the proper planning of flood management to prevent loss of life and property. Researchers have developed and used different approaches, including statistical and machine learning languages, in their models for flood risk assessment. In recent years, new advanced machine learning techniques, including deep learning and meta-heuristic algorithms, have been developed and applied effectively in flood studies. However, different available single and hybrid machine learning algorithms, including deep learning, and new models for flood risk assessment must be applied.

The main aim of the proposed Special Issue is to synthesize the state-of-the-art research findings related to the latest developments and challenges in the field of machine learning techniques in one place for proper flood risk assessment and prediction. High-quality original research papers on flood risk modeling related to flood risk assessment and management that present theoretical frameworks, methodologies, and applications from a single-or cross-country perspective are welcome. Case histories and review articles on this topic are also welcome.

Potential topics of interest include but are not limited to the following:

Cutting-edge machine learning methods including deep learning techniques and meta-heuristic algorithms for flood risk assessment;
Software development that promotes the application of machine learning in flood risk assessment;
Real-world case studies in flood risk assessment.

Dr. Binh Thai Pham
Dr. Indra Prakash
Guest Editors

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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. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

flood risk
flood vulnerability
flood hazard
flood susceptibility
deep learning
machine learning

Published Papers (3 papers)

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Research

23 pages, 7619 KiB

Open AccessArticle

Impact of Structural and Non-Structural Measures on the Risk of Flash Floods in Arid and Semi-Arid Regions: A Case Study of the Gash River, Kassala, Eastern Sudan

by Kamal Abdelrahim Mohamed Shuka, Ke Wang, Ghali Abdullahi Abubakar and Tianyue Xu

Sustainability 2024, 16(5), 1752; https://0-doi-org.brum.beds.ac.uk/10.3390/su16051752 - 21 Feb 2024

Viewed by 702

Abstract

Sediment precipitation in riverbeds influences the effectiveness of structural and non-structural measures for flash flood mitigation and increases the potential for flooding. This study aimed to disclose the effectiveness of the implemented measures for flood risk mitigation in Kassala town, eastern Sudan. We employed remote sensing (RS) and GIS techniques to determine the change in the Gash River riverbed, the morphology, and the leveling of both the eastern and western sides of the river. Flood model simulation and a 3D path profile were generated using the digital elevation model (DEM) with a data resolution of 12.5 m from the ALOS BILSAR satellite. The main purpose of this study is to extract the layer of elevation of the riverbed on both the western and eastern banks and to determine the variations and their relationship to flood occurrence and mitigation. The construction of dikes and spurs near Kassala town has led to sediment precipitation, causing the riverbed to rise. The results show that it is now 1.5 m above the eastern Kassala town level, with a steep slope of 2 m/km, and the cross-section area at Kassala bridge has shrunk, which indicates that the bridge body will partially impede the river’s high discharge and increase the potential for flood risk in the study area. The eastern part of Kassala town has a higher likelihood of flooding than the western side. This study suggests redesigning structural measures like widening the Gash River, extending Kassala bridge for normal water flow, strengthening early warning systems, and implementing soil conservation activities for normal water flow. Full article

(This article belongs to the Special Issue Flood Risk Assessment Using Deep Learning and State-of-the-Art Machine Learning)

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20 pages, 19174 KiB

Open AccessArticle

DeepSDC: Deep Ensemble Learner for the Classification of Social-Media Flooding Events

by Muhammad Hanif, Muhammad Waqas, Amgad Muneer, Ayed Alwadain, Muhammad Atif Tahir and Muhammad Rafi

Sustainability 2023, 15(7), 6049; https://0-doi-org.brum.beds.ac.uk/10.3390/su15076049 - 31 Mar 2023

Cited by 5 | Viewed by 1357

Abstract

Disasters such as earthquakes, droughts, floods, and volcanoes adversely affect human lives and valuable resources. Therefore, various response systems have been designed, which assist in mitigating the impact of disasters and facilitating relief activities in the aftermath of a disaster. These response systems require timely and accurate information about affected areas. In recent years, social media has provided access to high-volume real-time data, which can be used for advanced solutions to numerous problems, including disasters. Social-media data combines two modalities (text and associated images), and this information can be used to detect disasters, such as floods. This paper proposes an ensemble learning-based Deep Social Media Data Classification (DeepSDC) approach for social-media flood-event classification. The proposed algorithm uses datasets from Twitter to detect the flooding event. The Deep Social Media Data Classification (DeepSDC) uses a two-staged ensemble-learning approach which combines separate models for textual and visual data. These models obtain diverse information from the text and images and combine the information using an ensemble-learning approach. Additionally, DeepSDC utilizes different augmentation, upsampling and downsampling techniques to tackle the class-imbalance challenge. The performance of the proposed algorithm is assessed on three publically available flood-detection datasets. The experimental results show that the proposed DeepSDC is able to produce superior performance when compared with several state-of-the-art algorithms. For the three datasets, FRMT, FCSM and DIRSM, the proposed approach produced F1 scores of 46.52, 92.87, and 92.65, respectively. The mean average precision (MAP@480) of 91.29 and 98.94 were obtained on textual and a combination of textual and visual data, respectively. Full article

(This article belongs to the Special Issue Flood Risk Assessment Using Deep Learning and State-of-the-Art Machine Learning)

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33 pages, 6943 KiB

Open AccessEditor’s ChoiceArticle

Application of GIS and Machine Learning to Predict Flood Areas in Nigeria

by Eseosa Halima Ighile, Hiroaki Shirakawa and Hiroki Tanikawa

Sustainability 2022, 14(9), 5039; https://0-doi-org.brum.beds.ac.uk/10.3390/su14095039 - 22 Apr 2022

Cited by 15 | Viewed by 7039

Abstract

Floods are one of the most devastating forces in nature. Several approaches for identifying flood-prone locations have been developed to reduce the overall harmful impacts on humans and the environment. However, due to the increased frequency of flooding and related disasters, coupled with the continuous changes in natural and social-economic conditions, it has become vital to predict areas with the highest probability of flooding to ensure effective measures to mitigate impending disasters. This study predicted the flood susceptible areas in Nigeria based on historical flood records from 1985~2020 and various conditioning factors. To evaluate the link between flood incidence and the fifteen (15) explanatory variables, which include climatic, topographic, land use and proximity information, the artificial neural network (ANN) and logistic regression (LR) models were trained and tested to develop a flood susceptibility map. The receiver operating characteristic curve (ROC) and area under the curve (AUC) were used to evaluate both model accuracies. The results show that both techniques can model and predict flood-prone areas. However, the ANN model produced a higher performance and prediction rate than the LR model, 76.4% and 62.5%, respectively. In addition, both models highlighted that those areas with the highest susceptibility to flood are the low-lying regions in the southern extremities and around water areas. From the study, we can establish that machine learning techniques can effectively map and predict flood-prone areas and serve as a tool for developing flood mitigation policies and plans. Full article

(This article belongs to the Special Issue Flood Risk Assessment Using Deep Learning and State-of-the-Art Machine Learning)

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