Advances in Hydrologic Forecasts and Water Resources Management

Chang, Fi-John; Guo, Shenglian

doi:10.3390/w12061819

Cited by 25 publications

(15 citation statements)

References 35 publications

(61 reference statements)

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“…Developing flood resilience has emerged as an important component of strategies to deal with extreme weather conditions and associated adverse effects, but significant knowledge gaps are evident in terms of exploring resilience-based concepts and management approaches [16]. To address these knowledge gaps and improve disaster resilience, new technologies offer ways of gathering detailed data to inform policymakers to make actionable decisions for at-risk aged care facilities.…”

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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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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“…Water resource forecasting generally centres on understanding hydrological variability over coming months or years, so that water managers can prepare for extremes such as droughts or floods (Chang & Guo, 2020; Hao et al, 2018). Some forecasting systems seek to project further into the future to allow long‐term planning of infrastructure and resilience to extremes and climate change (Svensson et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Non‐stationary control of the NAO on European rainfall and its implications for water resource management

Rust

Bloomfield

Cuthbert

et al. 2021

Hydrological Processes

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Water resource forecasting generally centres on understanding hydrological variability over coming months or years, so that water managers can prepare for extremes such as droughts or floods (Chang & Guo, 2020; Hao et al., 2018). Some forecasting systems seek to project further into the future to allow long-term planning of infrastructure and resilience to extremes and climate change (Svensson et al., 2015). These systems can rely directly or indirectly on outputs from Global Climate Models (GCMs; such as gridded reanalysis datasets) to forecast hydrological conditions (Bhatt & Mall, 2015; Ionita & Nagavciuc, 2020). In the North Atlantic region, in particular Western Europe, the North Atlantic Oscillation (NAO) is used as an indicator for hydrometeorological conditions given its leading control on winter rainfall totals (Hurrell & Deser, 2010; Scaife et al., 2008, 2014). A dipole of pressure anomalies over the North Atlantic, the NAO's positive phase (greater than average pressure gradient; NAO+) results in wetter conditions in northwest Europe with dryer conditions in southwest Europe (Rust et al., 2018; Trigo et al., 2004). Its negative phase (weaker than average pressure gradient; NAO−) results in the inverse effect on rainfall (Folland et al., 2015; and as shown by the correlation coefficients in Figure 1). Given this relationship, and, con

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“…An artificial neural network (ANN) is a type of machine-learning method that can adequately simulate non-linear complex systems without requiring a physical understanding of the underlying mathematical relationship between the input and output, and they have been widely used in hydrological fields [23][24][25][26]. The development of an ANN consists of partitioning the data, identifying important input variables, optimizing the network architecture and its parameters, and evaluating the model performance.…”

Section: Development Of Artificial Neural Network (Ann)-based Instantaneous Peak Flow (Ipf) Estimation Methodsmentioning

confidence: 99%

Estimation of Hourly Flood Hydrograph from Daily Flows Using Artificial Neural Network and Flow Disaggregation Technique

Lee

Kim

2020

Water

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Flood data on a high temporal scale are required for the design of hydraulic structures, flood risk assessment, flood protection, and reservoir operations. Such flood data are typically generated using rainfall-runoff models through an accurate calibration process. The data also can be estimated using a simple relationship between the daily and the sub-daily flow records as an alternative to rainfall–runoff modelling. In this study, we propose an approach combining an artificial neural network (ANN) model for peak flow estimation and the steepness index unit volume flood hydrograph (SIUVFH) method for sub-daily flow disaggregation to generate hydrographs on an hourly time scale. The SIUVFH method is based on the strong relationship between the flood peak and the steepness index, which is defined as the difference between the daily flood peak and daily flow several days before the peak; it is also used for selecting a reference unit volume flood hydrograph to be scaled to obtain the sub-daily flood hydrograph. In this study, to improve the applicability of the SIUVFH method for locations with a weak relationship between the flood peak and steepness index, the ANN-based flood peak estimation was used as an additional indicator to determine a reference unit volume flood hydrograph. To apply the proposed method, ANN models for estimating the peak flows from the mean daily flows during peak and adjacent days were constructed for the studied dam sites. The optimal ANN structures were determined through Monte Carlo cross-validation. The results showed a good performance with statistical measurements of relative root mean square errors of 0.155–0.224, 0.208–0.301, and 0.244–0.382 for the training, validation, and testing datasets, respectively. An application of the combined use of the ANN-based peak estimation and the SIUVFH-based flow disaggregation revealed that the disaggregated hourly flows satisfactorily matched the observed flood hydrograph.

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Advances in Hydrologic Forecasts and Water Resources Management

Cited by 25 publications

References 35 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

Non‐stationary control of the NAO on European rainfall and its implications for water resource management

Estimation of Hourly Flood Hydrograph from Daily Flows Using Artificial Neural Network and Flow Disaggregation Technique

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