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

Abstract

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.

Flood Risk Assessment and Mapping: A Case Study from Australia’s Hawkesbury-Nepean Catchment

“…The result of this index accurately reflects the highly vulnerable nature of this catchment, particularly with respect to the aforementioned hydrological environmental conditions, which have created the bathtub effect. These conditions are well-documented, with studies such as [104] noting the ability of the rivers here to rise much higher than similar catchments in a flood event with a similar frequency. In addition to the characterisation of the physical vulnerability of the study area, the socio-economic vulnerability of the HNC is also quantified through the IRSD input.…”

Automatic Target Detection from Satellite Imagery Using Machine Learning

“…Table 7 shows that YOLO has good speed rather than Faster RCNN. The SSD gave the same speed when we tested one image and a batch of ten images for detection [ 98 , 99 , 100 , 101 , 102 ]. It means SSD gives good speed on real-time videos and batches of images ( Figure 13 ).…”

The Problem of Effective Evacuation of the Population from Floodplains under Threat of Flooding: Algorithmic and Software Support with Shortage of Resources

“…First, a significant effect is the dependence of the pedestrian evacuation time on the age and gender characteristics of the population, the season, the presence of panic, etc. [89][90][91][92][93][94][95]. Another significant effect is associated both with the refusal of part of the population to evacuate, and with the uncertainty in the behavior of people evacuating on their own, for example, when choosing evacuation routes and evacuation points [89,96,97].…”