Two decades of ensemble flood forecasting: a state-of-the-art on past developments, present applications and future opportunities

Das, Jew; Manikanta, Velpuri; Teja, K. Nikhil; Umamahesh, N. V.

doi:10.1080/02626667.2021.2023157

Cited by 19 publications

(3 citation statements)

References 115 publications

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“…On the use of new technologies, implementing a robust weather forecasting system is a vital component of any effective disaster risk management plan and of evacuation. Ensemble flood forecasting techniques [39] and operational forecasting and monitoring systems [40], have emerged as promising approaches to enhance the prediction of potential threats, such as hurricanes and floods. By employing these techniques, it becomes possible to disseminate more timely and reliable messages regarding the potential level of hazard to the affected population.…”

Section: Discussionmentioning

confidence: 99%

Emergency Evacuation Behavior in Small Island Developing States: Hurricane Irma in Sint Maarten

Medina

Sánchez

Vojinović

2023

Water

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Disasters triggered by natural hazards are becoming more frequent and more intense, causing damage to infrastructure and causing loss of life. One way to reduce disaster risk is by evacuating the hazardous area. However, despite the amount of literature that exists on evacuation behavior, there is still a lack of agreement on which variables can be used as predictors for individuals (or households) to actually evacuate. This lack of agreement can be related to the many variables that can affect the evacuation decision, from demographics, geographic, the hazard itself, and also local or cultural differences that may influence evacuation. Hence, it is essential to analyze and understand these variables based on the specifics of a case study. This study aims to find the most significant variables to be used as predictors of evacuation on the island of Sint Maarten, using data collected after the disaster caused by Hurricane Irma in September 2017. The results suggest that the variables gender, homeownership, percentage of property damage, quality of information, number of storeys of the house, and the vulnerability index are the most significant variables influencing evacuation decisions on the island. We believe the results of this paper offer a clear view to risk managers on the island as to which variables are most important in order to increase evacuation rates on Sint Maarten and to plan more efficiently for future evacuations. In addition, the variables found in this study have the potential to be the base information to set up, validate, and calibrate evacuation models.

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Section: Discussionmentioning

confidence: 99%

Emergency Evacuation Behavior in Small Island Developing States: Hurricane Irma in Sint Maarten

Medina

Sánchez

Vojinović

2023

Water

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“…Recent advances in DL models have improved the performance of different researchers to improve flood prediction precision. Piadeh et al (2022), Nevo et al (2022), and Das et al (2022) introduced machine learning (ML), these DL models possess the ability to derive patterns from extensive data sets.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Flood Forecasting Model Using an Optimal Deep Belief Network

2024

Global NEST Journal

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<p>Floods inflict significant damage globally each year, underscoring the importance of accurate and timely flood prediction to mitigate property loss and life. Precise flood prediction provides governments with crucial preemptive alerts regarding potential flood disasters, enabling timely evacuations and life-saving measures. Although various ML (machine learning) models have demonstrated improved performance compared to traditional statistical models in flood prediction, they often overlook the spatial features crucial to understanding the generation of floods. DL (deep learning) is used in flood prediction to enhance the promptness and efficiency of flood levels predictions. This work presents an optimized DL model to forecast floods using time series data. Initially, the data set was cleaned and normalized by linear interpolation. Then, the DL model ODBN (optimal deep belief network) is utilized for flood forecasting time series prediction. ODBN is the integration of DBN and SCA (Sinh-Cosh algorithm). The experimental analysis is carried out on the real-time dataset and achieved better MSE and RMSE values of 0.75 and 0.94 respectively. The findings suggest that the use of an ODBN is an effective method of accurately forecasting floods.</p>

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“…While ensemble hydrological forecasts have shown advantages over single-value ones for hydropower purposes, for instance, by improving operational decisions and leveraging economic benefits (e.g., [17,[25][26][27][28][29]), the development of studies on this topic is still slow in South America compared to other regions of the world, especially in the northern hemisphere [30][31][32]. In parallel, the scientific community has dedicated substantial efforts to developing ensemble streamflow forecasting methods also at continental and global scales [33][34][35][36][37][38][39][40][41], and analyses of streamflow forecasts produced with such techniques have been possible for Brazil as a whole [42].…”

Section: Introductionmentioning

confidence: 99%

Advancing Medium-Range Streamflow Forecasting for Large Hydropower Reservoirs in Brazil by Means of Continental-Scale Hydrological Modeling

Neto

Siqueira

Gama

et al. 2023

Water

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Streamflow forecasts from continental to global scale hydrological models have gained attention, but their performance against operational forecasts at local to regional scales must be evaluated. This study assesses the skill of medium-range, weekly streamflow forecasts for 147 large Brazilian hydropower plants (HPPs) and compares their performance with forecasts issued operationally by the National Electric System Operator (ONS). A continental-scale hydrological model was forced with ECMWF medium-range forecasts, and outputs were corrected using quantile mapping (QM) and autoregressive model approaches. By using both corrections, the percentage of HPPs with skillful forecasts against climatology and persistence for 1–7 days ahead increased substantially for low to moderate (9% to 56%) and high (72% to 94%) flows, while using only the QM correction allowed positive skill mainly for low to moderate flows and for 8–15 days ahead (29% to 64%). Compared with the ONS, the corrected continental-scale forecasts issued for the first week exhibited equal or better performance in 60% of the HPPs, especially for the North and Southeast subsystems, the DJF and MAM months, and for HPPs with less installed capacity. The findings suggest that using simple corrections on streamflow forecasts issued by continental-scale models can result in competitive forecasts even for regional-scale applications.

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Two decades of ensemble flood forecasting: a state-of-the-art on past developments, present applications and future opportunities

Cited by 19 publications

References 115 publications

Emergency Evacuation Behavior in Small Island Developing States: Hurricane Irma in Sint Maarten

Emergency Evacuation Behavior in Small Island Developing States: Hurricane Irma in Sint Maarten

An Efficient Flood Forecasting Model Using an Optimal Deep Belief Network

Advancing Medium-Range Streamflow Forecasting for Large Hydropower Reservoirs in Brazil by Means of Continental-Scale Hydrological Modeling

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