Artificial Intelligence Methodologies Applied to Prompt Pluvial Flood Estimation and Prediction

Chang, Deng; Yang, Sheng‐Ming; Hsieh, Sheau Ling; Wang, Hui Jung; Yeh, Kuo‐Chen

doi:10.3390/w12123552

Cited by 23 publications

(12 citation statements)

References 31 publications

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“…However, few situations are covered and, thus, more research should focus on filling this gap. An alternative method is to predict the rainfall in real-time and then retrieve the corresponding water depth map by using a similarity measure on a large dataset of previous simulations (Chang et al, 2020). However, such a solution may be challenging because of the large storage requirements.…”

Section: Flood Applications and Usabilitymentioning

confidence: 99%

Deep Learning Methods for Flood Mapping: A Review of Existing Applications and Future Research Directions

Bentivoglio

Isufi

Jonkman

et al. 2021

Preprint

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Abstract. Deep Learning techniques have been increasingly used in flood risk management to overcome the limitations of accurate, yet slow, numerical models, and to improve the results of traditional methods for flood mapping. In this paper, we review 45 recent publications to outline the state-of-the-art of the field, identify knowledge gaps, and propose future research directions. The review focuses on the type of deep learning models used for various flood mapping applications, the flood types considered, the spatial scale of the studied events, and the data used for model development. The results show that models based on convolutional layers are usually more accurate as they leverage inductive biases to better process the spatial characteristics of the flooding events. Traditional models based on fully-connected layers, instead, provide accurate results when coupled with other statistical models. Deep learning models showed increased accuracy when compared to traditional approaches and increased speed when compared to numerical methods. While there exist several applications in flood susceptibility, inundation, and hazard mapping, more work is needed to understand how deep learning can assist real-time flood warning during an emergency, and how it can be employed to estimate flood risk. A major challenge lies in developing deep learning models that can generalize to unseen case studies and sites. Furthermore, all reviewed models and their outputs, are deterministic, with limited considerations for uncertainties in outcomes and probabilistic predictions. The authors argue that these identified gaps can be addressed by exploiting recent fundamental advancements in deep learning or by taking inspiration from developments in other applied areas. Models based on graph neural networks and neural operators can work with arbitrarily structured data and thus should be capable of generalizing across different case studies and could account for complex interactions with the natural and built environment. Neural operators can also speed up numerical models while preserving the underlying physical equations and could thus be used for reliable real-time warning. Similarly, probabilistic models can be built by resorting to Deep Gaussian Processes.

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Section: Flood Applications and Usabilitymentioning

confidence: 99%

Deep Learning Methods for Flood Mapping: A Review of Existing Applications and Future Research Directions

Bentivoglio

Isufi

Jonkman

et al. 2021

Preprint

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“…Compared to international flood warning systems, TFWS is technically robust. The TFWS consists of six different components which give a holistic picture of the flood scenario, i.e., prediction, interpretation, message construction, communication, response and review [15]. Reduction in flood damage can be carried out with access to real-time information and data which will help in generating flood maps, identifying the evacuation routes and locating the victims.…”

Section: Introductionmentioning

confidence: 99%

An AI/ML-Based Strategy for Disaster Response and Evacuation of Victims in Aged Care Facilities in the Hawkesbury-Nepean Valley: A Perspective

et al. 2022

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The Hawkesbury-Nepean Valley, Australia’s longest coastal catchment, is spanned by a river system of more than 470 km, that runs from Goulburn to Broken Bay, covering a total area of over 2.2 million hectares. This region has remained prone to flood events, with considerable mortalities, economic impacts and infrastructural losses occurring quite regularly. The topography, naturally variable climatic conditions and the ‘bathtub’ effect in the region are responsible for the frequent flood events. In response, the Government at the national/federal, state and local level has focused on the design of efficient flood risk management strategies with appropriate evacuation plans for vulnerable communities from hospitals, schools, childcare and aged care facilities during a flood event. Despite these overarching plans, specialized response and evacuation plans for aged care facilities are critical to reducing the loss incurred by flood events in the region. This is the focus of this present paper, which reviews the history of flood events and responses to them, before examining the utilization of artificial intelligence (AI) techniques during flood events to overcome the flood risks. An early flood warning system, based on AI/Machine Learning (ML) strategy is being suggested for a timely decision, enhanced disaster prediction, assessment and response necessary to overcome the flood risks associated with aged care facilities within the Hawkesbury-Nepean region. A framework entailing AI/ML methods for identifying the safest route to the destination using UAV and path planning has been proposed for timely disaster response and evacuation of the residents of aged care facilities.

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“…Shortage of data could be an obstacle for efficiently training the models. To overcome these issues, authorities can apply GAN [89][90][91]. The generative models can be successfully applied for producing realistic images, thus assisting in generating more data.…”

Section: Rq-4 How Can the Authorities Improve The Existing Flood Management Operation With Cutting-edge Technologies?mentioning

confidence: 99%

An Integrated Approach for Post-Disaster Flood Management Via the Use of Cutting-Edge Technologies and UAVs: A Review

et al. 2021

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Rapid advances that improve flood management have facilitated the disaster response by providing first aid services, finding safe routes, maintaining communication and developing flood maps. Different technologies such as image processing, satellite imagery, synthetic imagery and integrated approaches have been extensively analysed in the literature for disaster operations. There is a need to review cutting-edge technologies for flood management. This paper presents a review of the latest advancements in the flood management domain based on image processing, artificial intelligence and integrated approaches with a focus on post-disaster. It answers the following research questions: (1) What are the latest developments in image processing for flood management in a post-disaster scenario? (2) What are the latest techniques for flood management based on artificial intelligence in a post-disaster scenario? (3) What are the existing gaps in the selected technologies for post-disaster? (4) How can the authorities improve the existing post-disaster management operation with cutting-edge technologies? A novel framework has been proposed to optimise flood management with the application of a holistic approach.

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Artificial Intelligence Methodologies Applied to Prompt Pluvial Flood Estimation and Prediction

Cited by 23 publications

References 31 publications

Deep Learning Methods for Flood Mapping: A Review of Existing Applications and Future Research Directions

Deep Learning Methods for Flood Mapping: A Review of Existing Applications and Future Research Directions

An AI/ML-Based Strategy for Disaster Response and Evacuation of Victims in Aged Care Facilities in the Hawkesbury-Nepean Valley: A Perspective

An Integrated Approach for Post-Disaster Flood Management Via the Use of Cutting-Edge Technologies and UAVs: A Review

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