Bayesian network model for flood forecasting based on atmospheric ensemble forecasts

Goodarzi, Leila; Banihabib, Mohammad Ebrahim; Roozbahani, Abbas; Dietrich, Jörg

doi:10.5194/nhess-19-2513-2019

Cited by 16 publications

(6 citation statements)

References 54 publications

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“…BBNs were introduced in the late 1980s, and they have been used frequently in water resources planning and management (Govender et al, 2021;Phan et al, 2016;Xue et al, 2017). For instance, BBNs were applied to estimate missing rainfall data (Sun et al, 2017) and forecast flood peaks (Goodarzi et al, 2019). Khan and Coulibaly (2006) reported better performance of a BBN compared to an Artificial Neural Network for simulating daily river flow and reservoir inflow.…”

Section: Bbn Downscaling Methodsmentioning

confidence: 99%

“…ZRR is the largest and most important river in UL. The average annual runoff of this river is about 2000 MCM, and it supplies more than 40 % of the total annual environmental flow to UL (Ghaheri et al, 1999;Meydani et al, 2021). Moreover, Bukan reservoir, as the biggest reservoir in the ULB, is located on the ZRR (Dehghanipour et al, 2020(Dehghanipour et al, , 2019.…”

Section: Study Areamentioning

confidence: 99%

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Daily reservoir inflow forecasting using weather forecast downscaling and rainfall-runoff modeling: Application to Urmia Lake basin, Iran

Meydani

Dehghanipour

Schoups

et al. 2022

Journal of Hydrology: Regional Studies

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Section: Bbn Downscaling Methodsmentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Daily reservoir inflow forecasting using weather forecast downscaling and rainfall-runoff modeling: Application to Urmia Lake basin, Iran

Meydani

Dehghanipour

Schoups

et al. 2022

Journal of Hydrology: Regional Studies

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“…Ref. [179] used the Bayesian network (BN) model to estimate flood peaks from atmospheric ensemble forecasts (AEFs) and simulate historic storms. The BN model was trained to compute flood peak forecasts from AEFs and hydrological preconditions, with results showing a mean absolute relative error of 0.076 for validation data compared to 0.39 for ANN, indicating that BN is less sensitive to small data sets than ANN, thus being more suited for flood peak forecasting than ANN in data-scarce regions.…”

Section: Solutions Of Data-scarce Regions and Fewssmentioning

confidence: 99%

Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review

Byaruhanga,

Kibirige,

Gokool

et al. 2024

Water

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Floods are recognised as one of the most destructive and costliest natural disasters in the world, which impact the lives and livelihoods of millions of people. To tackle the risks associated with flood disasters, there is a need to think beyond structural interventions for flood protection and move to more non-structural ones, such as flood early warning systems (FEWSs). Firstly, this study aimed to uncover how flood forecasting models in the FEWSs have evolved over the past three decades, 1993 to 2023, and to identify challenges and unearth opportunities to assist in model selection for flood prediction. Secondly, the study aimed to assist in model selection and, in return, point to the data and other modelling components required to develop an operational flood early warning system with a focus on data-scarce regions. The scoping literature review (SLR) was carried out through a standardised procedure known as Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The SLR was conducted using the electronic databases Scopus and Web of Science (WoS) from 1993 until 2023. The results of the SLR found that between 1993 and 2010, time series models (TSMs) were the most dominant models in flood prediction and machine learning (ML) models, mostly artificial neural networks (ANNs), have been the most dominant models from 2011 to present. Additionally, the study found that coupling hydrological, hydraulic, and artificial neural networks (ANN) is the most used ensemble for flooding forecasting in FEWSs due to superior accuracy and ability to bring out uncertainties in the system. The study recognised that there is a challenge of ungauged and poorly gauged rainfall stations in developing countries. This leads to data-scarce situations where ML algorithms like ANNs are required to predict floods. On the other hand, there are opportunities to use Satellite Precipitation Products (SPP) to replace missing or poorly gauged rainfall stations. Finally, the study recommended that interdisciplinary, institutional, and multisectoral collaborations be embraced to bridge this gap so that knowledge is shared for a faster-paced advancement of flood early warning systems.

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“…It belongs to the group of probabilistic graphical simulations based on a set of random variables and directed acyclic graphs to exhibit the probable dependence between variables [ 48 ].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Risk Prediction by Using Artificial Neural Network in Global Software Development

Iftikhar

Alam

Ahmed

et al. 2021

Computational Intelligence and Neuroscience

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The demand for global software development is growing. The nonavailability of software experts at one place or a country is the reason for the increase in the scope of global software development. Software developers who are located in different parts of the world with diversified skills necessary for a successful completion of a project play a critical role in the field of software development. Using the skills and expertise of software developers around the world, one could get any component developed or any IT-related issue resolved. The best software skills and tools are dispersed across the globe, but to integrate these skills and tools together and make them work for solving real world problems is a challenging task. The discipline of risk management gives the alternative strategies to manage risks that the software experts are facing in today’s world of competitiveness. This research is an effort to predict risks related to time, cost, and resources those are faced by distributed teams in global software development environment. To examine the relative effect of these factors, in this research, neural network approaches like Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient have been implemented to predict the responses of risks related to project time, cost, and resources involved in global software development. Comparative analysis of these three algorithms is also performed to determine the highest accuracy algorithms. The findings of this study proved that Bayesian Regularization performed very well in terms of the MSE (validation) criterion as compared with the Levenberg–Marquardt and Scaled Conjugate Gradient approaches.

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Bayesian network model for flood forecasting based on atmospheric ensemble forecasts

Cited by 16 publications

References 54 publications

Daily reservoir inflow forecasting using weather forecast downscaling and rainfall-runoff modeling: Application to Urmia Lake basin, Iran

Daily reservoir inflow forecasting using weather forecast downscaling and rainfall-runoff modeling: Application to Urmia Lake basin, Iran

Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review

Risk Prediction by Using Artificial Neural Network in Global Software Development

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