Coupled Use of Hydrologic-Hydraulic Model and Geomorphological Descriptors for Flood-Prone Areas Evaluation: A Case Study of Lama Lamasinata

Lioi, Beatrice; Gioia, Andrea; Totaro, Vincenzo; Balacco, Gabriella; Iacobellis, Vito; Chiaia, Giancarlo

doi:10.1007/978-3-030-58811-3_44

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…Furthermore, the Australian Emergency Floodplain Framework highlights the requirement of a coordinated approach for the management of flood risks at various levels of government, through the provision of technical, financial, legislative and regulatory flood management efforts [55]. The barriers to coordinated efforts between different levels of government and poor coordination issues associated with a complex arrangement of agencies in Australia have been reported by various authors as being responsible for poor flood risk management strategies [56].…”

Section: General Perspective and Gaps On The Hawkesbury-nepean Flood Risk Management Strategymentioning

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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Section: General Perspective and Gaps On The Hawkesbury-nepean Flood Risk Management Strategymentioning

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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“…As a result, we combined the SWAT model with geomorphological information to create a comprehensive flood model. Lioi et al [55] and Lastra et al [56] both found that geomorphological information can enhance a flood model, especially in data-scarce areas. A machine learning program, Picterra, was then used to find erosion patterns in the drone imagery.…”

Section: Discussionmentioning

confidence: 99%

Drone-Based Community Assessment, Planning, and Disaster Risk Management for Sustainable Development

et al. 2021

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Accessible, low-cost technologies and tools are needed in the developing world to support community planning, disaster risk assessment, and land tenure. Enterprise-scale geographic information system (GIS) software and high-resolution aerial or satellite imagery are tools which are typically not available to or affordable for resource-limited communities. In this paper, we present a concept of aerial data collection, 3D cadastre modeling, and disaster risk assessment using low-cost drones and adapted open-source software. Computer vision/machine learning methods are used to create a classified 3D cadastre that contextualizes and quantifies potential natural disaster risk to existing or planned infrastructure. Building type and integrity are determined from aerial imagery. Potential flood damage risk to a building is evaluated as a function of three mechanisms: undermining (erosion) of the foundation, hydraulic pressure damage, and building collapse due to water load. Use of Soil and Water Assessment Tool (SWAT) provides water runoff estimates that are improved using classified land features (urban ecology, erosion marks) to improve flow direction estimates. A convolutional neural network (CNN) is trained to find these flood-induced erosion marks from high-resolution drone imagery. A flood damage potential metric scaled by property value estimates results in individual and community property risk assessments.

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“…Roads and bridges can be used to identify the Detection of significant landmarks from images is another method being used to analyse various features from the images to determine whether a region is flooded or not. Researchers are focusing on extracting landmarks present on the ground, such as bridges, roads, houses and buildings [48][49][50][51]. These features can be further processed using image processing techniques to aid in disaster response.…”

Section: Rqmentioning

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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Coupled Use of Hydrologic-Hydraulic Model and Geomorphological Descriptors for Flood-Prone Areas Evaluation: A Case Study of Lama Lamasinata

Cited by 3 publications

References 29 publications

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

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

Drone-Based Community Assessment, Planning, and Disaster Risk Management for Sustainable Development

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

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