Modeling Road Accessibility in a Flood-Prone Area in Romania

Popescu, Cristian; Bărbulescu, Alina; Dumitriu, Cristian Ștefan

doi:10.3390/engproc2023039022

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Therefore, learning from the past is essential to observe and understand the evolution of the water cycle and river flow dynamic. Setting an early-warning system for taking timely and informed measures to avoid (if possible) or reduce the floods of drought effects on human activities is necessary [3][4][5][6][7][8]. Research of the rivers' flow was performed in different directions, as follows:…”

Section: Introductionmentioning

confidence: 99%

“…It does not effectively provide a river discharge model, which is essential for a reliable forecast. To complement the findings provided by statistical analysis [7,8], the HEC-RAS software was used to model the susceptibility to floods in different basins. HEC-HMS was employed to simulate losses, snowmelt, sub-basin routing, and river flow routing [35].…”

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confidence: 99%

“…In Romania, only a few researchers investigated the change in the river flow regimen after the dam inauguration [53][54][55]. Moreover, the research of rivers' dynamics by AI methods is insignificant, the most utilized tools being statistical methods and hydraulic modeling [5][6][7][53][54][55].…”

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confidence: 99%

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Forecasting the River Water Discharge by Artificial Intelligence Methods

Bărbulescu,

Zhen

2024

Water

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The management of water resources must be based on accurate models of the river discharge in the context of the water flow alteration due to anthropic influences and climate change. Therefore, this article addresses the challenge of detecting the best model among three artificial intelligence techniques (AI)—backpropagation neural networks (BPNN), long short-term memory (LSTM), and extreme learning machine (ELM)—for the monthly data series discharge of the Buzău River, in Romania. The models were built for three periods: January 1955–September 2006 (S1 series), January 1955–December 1983 (S2 series), and January 1984–December 2010 (S series). In terms of mean absolute error (MAE), the best performances were those of ELM on both Training and Test sets on S2, with MAETraining = 5.02 and MAETest = 4.01. With respect to MSE, the best was LSTM on the Training set of S2 (MSE = 60.07) and ELM on the Test set of S2 (MSE = 32.21). Accounting for the R2 value, the best model was LSTM on S2 (R2Training = 99.92%, and R2Test = 99.97%). ELM was the fastest, with 0.6996 s, 0.7449 s, and 0.6467 s, on S, S1, and S2, respectively.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Forecasting the River Water Discharge by Artificial Intelligence Methods

Bărbulescu,

Zhen

2024

Water

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Advancing Water Resource Management in Romania through National-Level Decision Support Systems

Chevereşan,

Mainerici,

Bărbulescu

2024

2024 10th International Conference on Control, Decision and Information Technologies (CoDIT)

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A Practical Approach on Reducing the Flood Impact: A Case Study from Romania

Popescu,

Bărbulescu

2024

Applied Sciences

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Recently, the frequency and intensity of extreme weather events have increased in many regions worldwide. Among them, floods, whose effects are devastating in many cases, have been recorded in Romania in the last few years. Built to reduce the flooding effects on the communities, structures such as embankments can sometimes accentuate the hazard. This article investigates such a situation and proposes solutions to reduce or even eliminate the flood impact on the community living in the Vărbilău Catchment in Romania. Recorded data series, field observations, GIS techniques, and hydraulic modeling were used to design the hazard maps and perform the 3D representations that illustrate the actual situation (when the small opening of the bridge favors the flooding) and the proposed solutions (extending the bridge opening and shortening the embankment). It is shown that adding seven pillars to the bridge would reduce the flooded surface by more than 1.5 times and the affected buildings’ surface by more than 3.5 times compared to the current situation.

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Modeling Road Accessibility in a Flood-Prone Area in Romania

Abstract: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Cited by 4 publications

References 17 publications

Forecasting the River Water Discharge by Artificial Intelligence Methods

Forecasting the River Water Discharge by Artificial Intelligence Methods

Advancing Water Resource Management in Romania through National-Level Decision Support Systems

A Practical Approach on Reducing the Flood Impact: A Case Study from Romania

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